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15 Computer Visions Projects You Can Do Right Now

Computer vision deals with how computers extract meaningful information from images or videos. It has a wide range of applications, including reverse engineering, security inspections, image editing and processing, computer animation, autonomous navigation, and robotics. 

In this article, we’re going to explore 15 great OpenCV projects, from beginner-level to expert-level . For each project, you’ll see the essential guides, source codes, and datasets, so you can get straight to work on them if you want.

Top Tools to Run a Computer Vision Project

What is Computer Vision?

Computer vision is about helping machines interpret images and videos. It’s the science of interacting with an object through a digital medium and using sensors to analyze and understand what it sees. It’s a broad discipline that’s useful for machine translation, pattern recognition, robotic positioning, 3D reconstruction, driverless cars, and much more.

The field of computer vision keeps evolving and becoming more impactful thanks to constant technological innovations. As time goes by, it will offer increasingly powerful tools for researchers, businesses, and eventually consumers.

Computer Vision today

Computer vision has become a relatively standard technology in recent years due to the advancement of AI. Many companies use it for product development, sales operations, marketing campaigns, access control, security, and more. 

Computer vision has plenty of applications in healthcare (including pathology), industrial automation, military use, cybersecurity, automotive engineering, drone navigation—the list goes on.

How does Computer Vision work?

Machine learning finds patterns by learning from its mistakes. The training data makes a model, which guesses and predicts things. Real-world images are broken down into simple patterns. The computer recognizes patterns in images using a neural network built with many layers.  

The first layer takes pixel value and tries to identify the edges . The next few layers will try to detect simple shapes with the help of edges . In the end, all of it is put together to understand the image.

It can take thousands, sometimes millions of images, to train a computer vision application. Sometimes even that’s not enough—some facial recognition applications can’t detect people of different skin colors because they’re trained on white people. Sometimes the application might not be able to find the difference between a dog and a bagel. Ultimately, the algorithm will only ever be as good as the data that was used for training it. 

OK, enough introduction! Let’s get into the projects.

Beginner level Computer Vision projects 

If you’re new or learning computer vision, these projects will help you learn a lot.

1. Edge & Contour Detection 

If you’re new to computer vision, this project is a great start. CV applications detect edges first and then collect other information. There are many edge detection algorithms, and the most popular is the Canny edge detector because it’s pretty effective compared to others. It’s also a complex edge-detection technique. Below are the steps for Canny edge detection:

• Reduce noise and smoothen image,
• Calculate the gradient,
• Non-maximum suppression,
• Double the threshold,
• Linking and edge detecting – hysteresis.

Code for Canny edge detection:

Contours are lines joining all the continuous objects or points (along the boundary), having the same color or intensity. For example, it detects the shape of a leaf based on its parameters or border. Contours are an important tool for shape and object detection. The contours of an object are the boundary lines that make up the shape of an object as it is. Contours are also called outline, edges, or structure, for a very good reason: they’re a way to mark changes in depth.

Code to find contours:

Recommended reading & source code: 

• Canny Edge Detection Step by Step in Python — Computer Vision
• Comparing Edge Detection Methods
• Edge Detection Github 
• Contours: Getting Started
• Contour Detection using OpenCV (Python/C++)
• Contour Features

2. Colour Detection & Invisibility Cloak

This project is about detecting color in images. You can use it to edit and recognize colors from images or videos. The most popular project that uses the color detection technique is the invisibility cloak. In movies, invisibility works by doing tasks on a green screen, but here we’ll be doing it by removing the foreground layer. The invisibility cloak process is this:

• Capture and store the background frame (just the background),
• Detect colors,
• Generate a mask,
• Generate the final output to create the invisible effect. 

It works on HSV (Hue Saturation Value). HSV is one of the three ways that Lightroom lets us change color ranges in photographs. It’s particularly useful for introducing or removing certain colors from an image or scene, such as changing night-time shots to day-time shots (or vice versa). It’s the color portion, identified from 0 to 360. Reducing this component toward zero introduces more grey and produces a faded effect. 

Value (brightness) works in conjunction with saturation. It describes the brightness or intensity of the color, from 0–100%. So 0 is completely black, and 100 is the brightest and reveals the most color.

• Github Repo – https://github.com/its-harshil/invisible_cloak
• Invisibility Cloak using OpenCV – Guide

3. Text Recognition using OpenCV and Tesseract (OCR)

Here, you use OpenCV and OCR (Optical Character Recognition) on your image to identify each letter and convert them into text. It’s perfect for anyone looking to take information from an image or video and turn it into text-based data. Many apps use OCR, like Google Lens, PDF Scanner, and more.

Ways to detect text from images:

• Use OpenCV – popular,
• Use Deep Learning models – the newest method,
• Use your custom model.

Text Classification: All Tips and Tricks from 5 Kaggle Competitions

Text Detection using OpenCV

Sample code after processing the image and contour detection:

Text Detection with Tesseract

It’s an open-source application that can recognize text in 100+ languages, and it’s backed by Google. You can also train this application to recognize many other languages. 

Code to detect text using tesseract: 

Recommended reading & datasets: 

• A comprehensive guide to OCR with Tesseract, OpenCV and Python
• KAIST Scene Text Database
• The Street View House Numbers (SVHN) Dataset
• Tesseract documentation
• Tesseract-ocr Github

4. Face Recognition with Python and OpenCV

It’s been just over a decade since the American television show CSI: Crime Scene Investigation first aired. During that time, facial recognition software has become increasingly sophisticated. Present-day software isn’t limited by superficial features like skin or hair color—instead, it identifies faces based on facial features that are more stable through changes in appearance, like eye shape and distance between eyes. This type of facial recognition is called “template matching”. You can use OpenCV, Deep learning, or a custom database to create facial recognition systems/applications. 

Process of detecting a face from an image:

• Find face locations and encodings,
• Extract features using face embedding,
• Face recognition, compare those faces.          

How to Choose a Loss Function for Face Recognition Create a Face Recognition Application Using Swift, Core ML, and TuriCreate

Below is the full code for recognizing faces from images:

Code to recognize faces from webcam or live camera:

• Face Recognition with OpenCV – Docs
• Face Recognition- Guide
• AT&T Face database  
• The Extended Yale Face Database B

5. Object Detection

Object detection is the automatic inference of what an object is in a given image or video frame. It’s used in self-driving cars, tracking, face detection, pose detection, and a lot more. There are 3 major types of object detection – using OpenCV, a machine learning-based approach, and a deep learning-based approach.

May interest you

Below is the full code to detect objects:

• Object Detection (objdetect module)
• Detecting Objects – Guide
• Object Detection – Tutorial 

Intermediate level Computer Vision projects 

We’re taking things to the next level with a few intermediate-level projects. These projects will probably be more fun than beginner projects, but also more challenging.

6. Hand Gesture Recognition

In this project, you need to detect hand gestures. After detecting the gesture, we’ll assign commands to them. You can even play games with multiple commands using hand gesture recognition.

How gesture recognition works:

• Install the Pyautogui library – it helps to control the mouse and keyboard without any user interaction,
• Convert it into HSV,
• Find contours,
• Assign command at any value – below we used 5 (from hand) to jump.

Full code to play the dino game with hand gestures: 

• Hand Recognition and Gesture Control – Docs
• Playing Chrome’s Dinosaur Game using OpenCV – Tutorial 
• Github Repo

7. Human Pose Detection

Many applications use human pose detection to see how a player plays in a specific game (for example – baseball). The ultimate goal is to locate landmarks in the body .  Human pose detection is used in many real-life videos and image-based applications, including physical exercise, sign language detection, dance, yoga, and much more. 

• Deep Learning-based Human Pose Estimation using OpenCV – Tutorial 
• MPII Human Pose Dataset
• Human Pose Evaluator Dataset
• Human-Pose-Estimation – Github

8. Road Lane Detection in Autonomous Vehicles

If you want to get into self-driving cars, this project will be a good start. You’ll detect lanes, edges of the road, and a lot more. Lane detection works like this:

• Apply the mask,
• Do image thresholding (thresholding converts an image to grayscale by replacing each pixel >= specified gray level with the corresponding gray level),
• Do hough line transformation (detecting lane lines).

• Car Lane Detection – Github
• Real-time lane detection for autonomous vehicles – Docs
• Real-time Car Lane Detection – Tutorial 

9. Pathology Classification

Computer vision is emerging in healthcare. The amount of data that pathologists analyze in a day can be too much to handle. Luckily, deep learning algorithms can identify patterns in large amounts of data that humans wouldn’t notice otherwise. As more images are entered and categorized into groups, the accuracy of these algorithms becomes better and better over time.

It can detect various diseases in plants, animals, and humans. For this application, the goal is to get datasets from Kaggle OCT and classify data into different sections. The dataset has around 85000 images. Optical coherence tomography (OCT) is an emerging medical technology for performing high-resolution cross-sectional imaging. Optical coherence tomography uses light waves to look inside a living human body. It can be used to evaluate thinning skin, broken blood vessels, heart diseases, and many other medical problems.

Over time, it’s gained the trust of doctors around the globe as a quick and effective way of diagnosing more quality patients than traditional methods. It can also be used to examine tattoo pigments or assess different layers of a skin graft that’s placed on a burn patient.

Code for Gradcam library used for classification:

• Kaggle Datasets Link

10. Fashion MNIST for Image Classification

One of the most used MNIST datasets was a database of handwritten images, which contains around 60,000 train and 10,000 test images of handwritten digits from 0 to 9. Inspired by this, they created Fashion MNIST, which classifies clothes. As a result of the large database and all the resources provided by MNIST, you get a high accuracy range from 96-99%.

This is a complex dataset containing 60,000 training images of clothes (35 categories) from online shops like ASOS or H&M. These images are divided into two subsets, one with clothes similar to the fashion industry, and the other with clothes belonging to the general public. The dataset contains 1.2 million samples (clothes and prices) for each category.

• MNIST colab file
• Fashion MNIST Colab file
• Handwritten datasets
• Fashion MNIST Dataset
• Fashion MNIST Tutorial 

Advanced level Computer Vision projects 

Once you’re an expert in computer vision, you can develop projects from your own ideas. Below are a few advanced-level fun projects you can work with if you have enough skills and knowledge. 

11. Image Deblurring using Generative Adversarial Networks

Image deblurring is an interesting technology with plenty of applications. Here, a generative adversarial network (GAN) automatically trains a generative model, like Image DeBlur’s AI algorithm. Before looking into this project, let’s understand what GANs are and how they work.

Understanding GAN Loss Functions 6 GAN Architectures You Really Should Know

Generative Adversarial Networks is a new deep-learning approach that has shown unprecedented success in various computer vision tasks, such as image super-resolution. However, it remains an open problem how best to train these networks. A Generative Adversarial Network can be thought of as two networks competing with one another; just like humans compete against each other on game shows like Jeopardy or Survivor. Both parties have tasks and need to come up with strategies based on their opponent’s appearance or moves throughout the game, while also trying not to be eliminated first. There are 3 major steps involved in training for deblurring:

• Create fake inputs based on noise using the generator,
• Train it with both real and fake sets, 
• Train the whole model.
• Application to Image Deblurring
• Blind Motion Deblurring Using Conditional Adversarial Networks – Paper
• Datasets of blurred street view

12. Image Transformation 

With this project, you can transform any image into different forms. For example, you can change a real image into a graphical one. This is kind of a creative and fun project to do. When we use the standard GAN method, it becomes difficult to transform the images, but for this project, most people use Cycle GAN. 

What Image Processing Techniques Are Actually Used in the ML Industry?

The idea is that you train two competing neural networks against each other. One network creates new data samples, called the “generator,” while the other network judges whether it’s real or fake. The generator alters its parameters to try to fool the judge by producing more realistic samples. In this way, both networks improve with time and continue to improve indefinitely – this makes GANs an ongoing project rather than a one-off assignment. This is a different type of GAN, it’s an extension of GAN architecture. What Cycle Gan does is create a cycle of generating the input. Let’s say you’re using Google Translate, you translate English to German, you open a new tab, copy the german output and translate German to English—the goal here is to get the original input you had. Below is an example of how transforming images to artwork works.

• CycleGAN – Github
• Transforming real photos into master artworks with gans – Guide

13. Automatic Colorization of Photos using Deep Neural Networks

When it comes to coloring black and white images, machines have never been able to do an adequate job. They can’t understand the boundary between grey and white, leading to a range of monochromatic hues that seem unrealistic. To overcome this issue, scientists from UC Berkeley, along with colleagues at Microsoft Research, developed a new algorithm that automatically colorizes photographs by using deep neural networks.

Deep neural networks are a very promising technique for image classification because they can learn the composition of an image by looking at many pictures. Densely connected convolutional neural networks (CNN) have been used to classify images in this study. CNN’s are trained with large amounts of labeled data, and output a score corresponding to the associated class label for any input image. They can be thought of as feature detectors that are applied to the original input image.

Colourization is the process of adding color to a black and white photo. It can be accomplished by hand, but it’s a tedious process that takes hours or days, depending on the level of detail in the photo. Recently, there’s been an explosion in deep neural networks for image recognition tasks such as facial recognition and text detection. In simple terms, it’s the process of adding colors to grayscale images or videos. However, with the rapid advance of deep learning in recent years, a Convolutional Neural Network (CNN) can colorize black and white images by predicting what the colors should be on a per-pixel basis. This project helps to colorize old photos. As you can see in the image below, it can even properly predict the color of coca-cola, because of the large number of datasets.

Recommended reading & guide: 

• Auto-Colorization of Historical Images Using Deep Convolutional Neural Networks
• Research 
• Colorizing Images – Guide

14. Vehicle Counting and Classification

Nowadays, many places are equipped with surveillance systems that combine AI with cameras, from government organizations to private facilities. These AI-based cameras help in many ways, and one of the main features is to count the number of vehicles. It can be used to count the number of vehicles passing by or entering any particular place. This project can be used in many areas like crowd counting, traffic management, vehicle number plate, sports, and many more.  The process is simple:

• Frame differencing,
• Image thresholding,
• Contour finding,
• Image dilation.

And finally, vehicle counting:

• Vehicle-counting Github
• Vehicle Detection Guide

15. Vehicle license plate scanners

A vehicle license plate scanner in computer vision is a type of computer vision application that can be used to identify plates and read their numbers. This technology is used for a variety of purposes, including law enforcement, identifying stolen vehicles, and tracking down fugitives.

A more sophisticated vehicle license plate scanner in computer vision can scan, read and identify hundreds, even thousands of cars per minute with 99% accuracy from distances up to half a mile away in heavy traffic conditions on highways and city streets. This project is very useful in many cases. 

The goal is to first detect the license plate and then scan the numbers and text written on it. It’s also referred to as an automatic number plate detection system. The process is simple:

• Capture image,
• Search for the number plate,
• Filter image,
• Line separate using row segmentation,
• OCR for the numbers and characters.

• Number Plate Recognition Tutorial
• Automatic Number Plate Recognition System for Vehicle Identification Using Optical Character Recognition

Conclusion 

And that’s it! Hope you liked the computer vision projects. As a cherry on top, I’ll leave you with several extra projects that you might also be interested in.

Extra projects 

• Photo Sketching
• Collage Mosaic Generator
• Blur the Face
• Image Segmentation
• Sudoku Solver
• Object Tracking
• Watermarking Images 
• Image Reverse Search Engine

Additional research and recommended reading

• https://neptune.ai/blog/building-and-deploying-cv-models
• https://www.forbes.com/sites/cognitiveworld/2019/06/26/the-present-and-future-of-computer-vision/?sh=490b290f517d
• https://www.youtube.com/watch?v=2hXG8v8p0KM
• https://towardsdatascience.com/everything-you-ever-wanted-to-know-about-computer-vision-heres-a-look-why-it-s-so-awesome-e8a58dfb641e
• https://docs.opencv.org/3.4/d2/d96/tutorial_py_table_of_contents_imgproc.html
• https://www.analyticsvidhya.com/blog/2020/05/build-your-own-ocr-google-tesseract-opencv/
• https://machinelearningmastery.com/what-are-generative-adversarial-networks-gans/

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15+ Top Computer Vision Project Ideas for Beginners for 2023

Computer vision is one of the hottest topics in the AI field.

It’s easy to get confused trying to figure out what’s the best way to learn and master this field.

Our advice?

Don’t get stuck analyzing theoretical concepts.

Instead, combine your conceptual knowledge with practical experience, and start building your own computer vision models! 

In this article we’ll share with you a bunch of computer vision project ideas to help you get started in less than an hour:

Here’s what we’ll cover:

People counting tool

Colors detection.

• Object tracking in videos

Pedestrian detection

Hand gesture recognition, human emotion recognition, road lane detection, business card scanner, license plate recognition, handwritten digit recognition.

• Iris flowers classification

Family photo face detection

• LEGO brick finder
• PPE detection

Face mask detection

Traffic light detection.

In case you are ready to get started, V7 arms you with the tools needed to build robust computer vision models, and the good news is that you don’t need any prior experience.

Train ML models and solve any computer vision task faster with V7.

Don't start empty-handed. Explore our repository of 500+ open datasets and test-drive V7's tools.

Ready to streamline AI product deployment right away? Check out:

• V7 Model Training
• V7 Workflows
• V7 Auto Annotation
• V7 Dataset Management ‍

Building a people counting solution could be both—a fun project and one that actually finds real-world applications.

To detect and count people present in an image, you’ll need a relevant training dataset and a data training platform. You can use a free tool like OpenCV to label your data or an auto annotation tool like V7 to complete this project faster.

Since the COVID-19 outbreak, people counting solutions have been growing in popularity, helping to enforce social distancing rules and improve safety.

Here’s a recommended dataset to get you started:

• People Counting Dataset (PCDS)

Next up is a simple colors detector that you can use for a wide variety of visual tasks.

From detecting colors to build the green screen app—replacing the green background with a custom video or background—to a simple photo editing software, building a color recognizer is an awesome project to get started with Computer Vision.

Here are a few interesting datasets you might want to use for your project:

• Google-512 dataset
• Lego colors
• Passport colors

Object tracking in a video

Next, consider taking on a bit more advanced computer vision task—object tracking in a video. 

Object tracking is about estimating the state of the target object present in the scene from previous information. 

You can build simple object tracking models using videos involving one object, such as a car, or multiple objects like pedestrians, animals, and whatnot. 

Essentially, the model will perform two tasks—predicting the object’s next state and correcting this state with respect to the object’s real condition. Object tracking models find applications in traffic control and human-computer interactions.

Here are a few video datasets you might find interesting for this computer vision task:

• TLP Datasets
• Tracking Net

💡 Pro tip: Check out The Complete Guide to Object Tracking [+V7 Tutorial].

Building an object detection model to detect pedestrians is one of the simplest and fastest computer vision projects to complete.

All you need is a relevant dataset of high-quality images and a data training platform to train and test your model. You can use one of the free image annotation tools or try out V7.

Pedestrian detectors are commonly used in the automotive industry for traffic safety as well as human-robot interactions and intelligent video systems.

Consider these datasets to get started:

• Caltech Pedestrian Dataset 
• Penn-Fudan Database for Pedestrian Detection
• Pedestrian Detection Dataset (Kaggle)

Hand gesture recognition is a bit more advanced computer vision task requiring you to firstly separate the hand region from the background and then to segment the fingers to predict hand gestures.

You can use OpenCV if you want to keep your model simple or take advantage of V7’s keypoint skeleton & custom polygons tools to make labeling faster and more accurate.

After training, you can test your model using a webcam. Hand gesture models can be used in VR games and sign languages.

Check out those datasets to get started:

• Hand Gestures of digits from 0 to 5
• Hand Gesture Recognition Database
• Multi-Modal Hand Gesture Dataset

💡 Pro tip: Check out A Comprehensive Guide to Human Pose Estimation to learn more.

If you decide to go with a bit more challenging task, consider building an emotion detection model. You can base your model on six main facial emotions: happiness, sadness, anger, fear, disgust, and surprise.

The three main components of this project include Image Pre-processing , Feature Extraction, and Feature Classification. 

Here are the datasets that might come in handy:

Road lane detection is yet another computer vision model that plays a key role in the development of the automotive industry.

Used primarily for self-driving cars, a road lane detector can be a fun beginner project that will help you get hands-on experience with both images and videos.

Here is a couple of datasets to help you out:

• CULane Dataset
• KITTI-Road/Lane Detection Evaluation 2013

Developing a business card scanner can be done using the OCR (Optical Character Recognition) technology. Your trained model will find and extract information from business cards.

Essentially, this project will be divided into three phases: image processing (noise cancellation), OCR (text extraction), and classification (classifying key properties).

You can use your business card reader to automate data entry.

Pick on one of those datasets to begin:

• Stanford Mobile Visual Search Data Set: Business Cards
• Indian Business Cards Sample Images

💡 Pro Tip: V7 allows you to automatically scan and read text using in-built Text Scanner.

A license plate recognizer is another idea for a computer vision project using OCR.

However, there are two challenges related to this project: data collection and the differences in license plates formats depending on the location/country.

Therefore, your model might not be accurate unless you train large amounts of data (if you manage to obtain it).

Note: License plate numbers are considered sensitive data, so make sure you stick to the publicly available datasets when building your models.

A simple automatic license plate recognition system can use basic image processing techniques, and you can build it using OpenCV and Python. 

However, more advanced systems use object detectors like YOLO or Fast C-RNN.

Automatic license plate recognition can be used for security, parking, smart cities, automatic toll collection, and access control.

Here are a few datasets you might consider:

• Car License Plate Detection
• UCSD Car Dataset
• Vehicles License Plates

This project is a perfect start for computer vision newbies—you can build a simple digit recognizer using the MNIST dataset. 

As you get a chance to train your model using Convolutional Neural Networks , you’ll learn how to develop, evaluate, and use convolutional deep learning neural networks for image classification.

The MNIST dataset contains a training set of 60,000 examples and a test set of 10,000 examples. You can access it here:

• MNIST Digit Recognition Dataset

💡 Pro tip: Read our Guide to Handwriting Recognition to learn more

Iris flowers classification .

Here’s another computer vision project based on one of the most popular and thus readily available datasets for pattern recognition—Iris Flowers Classification Dataset.

It contains three classes of 50 instances each, where each class refers to a type of iris plant. 

It’s a great beginner’s project that’ll help you get hands-on experience with image classification as you’ll train your model to predict the species of a new iris flower.

You can download the dataset here:

• Iris Flowers Classification Dataset

💡 Pro Tip: Check out 65+ Best Free Datasets for Machine Learning to find more datasets to train on.

Grab your family album to collect original data and build a face recognition model to identify your family members in the photos.

You can label your data using a free annotation tool or V7 and train your model in less than an hour. This task is a multi-stage process consisting of face detection, alignment, feature extraction, and feature recognition.

To make your project more interesting and your model more accurate consider using video data, too.

If you can’t obtain data on your own, check out these datasets to get started with facial recognition projects:

• Flickr-Faces-HQ Dataset
• Labeled Faces in the Wild Home

LEGO Brick Finder

If you’ve ever spent hours building LEGO in your childhood, this project could be a perfect way to get you hooked on computer vision. 

In its simplest form, you can build a model to detect and identify LEGO brick in real-time using your webcam or your phone camera. All you need is a large set of training data and a tool to train your model.

Here are the datasets for you:

• Lego vs. Generic Brick Identification set
• Images of LEGO Bricks

PPE Detection

The goal of this computer vision project is to build a model identifying the elements of PPE or face masks. You can complete it in a couple of hours and test it using a web cam and wearing a face mask in front of your computer.

Here’s how we’ve labeled worker PPE using V7’s auto-annotate tool in less than a minute.

PPE detection models find application in industries such as construction or healthcare (hospitals).

See how V7 handles PPE detection on a video.

Check out these datasets to get started:

• COVID-19 PPE Dataset

Plot the best routes for your training data with 8 workflow stages to arrange, connect, and loop any way you need.

Similarly to PPE detection, you can build a simple face mask detection model to identify people who wear and don't wear a mask in public.

Remember to collect large amounts of data to ensure model's accuracy in handling varying kinds of occlusions.

Check out this dataset to get started:

• Face Mask Detection

Finally, consider spending some time on training a traffic light detector. This project is relatively easy to complete because of the availability of data and research that you can access for free.

Traffic light detection finds applications in the intelligent transportation field including popular use cases such as autonomous cars and smart cities.

Here are a few datasets you can use:

• Bosch Small Traffic Lights Dataset
• LISA Traffic Light Dataset

See how V7 handles traffic light detection in this video.

Building your first computer vision model: Key takeaways

Now that you’ve got a bunch of ideas for your computer vision projects, it’s time to get some hands-on experience and start developing your own AI models.

If you want to keep things simple—start with image classification using the Iris Flowers dataset or pedestrian detection.

When considering more advanced projects, go for object tracking in videos, or a simple business card scanner app that you can develop to test your AI model in real-world conditions.

Either way, you are now ready to combine your theoretical knowledge with practical experience and start building computer vision models that can be turned into real products with a few lines of codes!

We are excited to see what you build and we keep our fingers crossed for your projects!

💡 Read more:

3 Signs You Are Ready to Annotate Data for Machine Learning

How to Split Your Machine Learning Data: Train, Validation, Test Sets

Data Cleaning Checklist: How to Prepare Your Machine Learning Data

Annotating With Bounding Boxes: Quality Best Practices

The Complete Guide to CVAT—Pros & Cons

5 Alternatives to Scale AI

YOLO: Real-Time Object Detection Explained

6 AI Applications Shaping the Future of Retail

7 Game-Changing AI Applications in the Sports Industry

6 Viable AI Use Cases in Insurance

8 Practical Applications of AI In Agriculture

6 Innovative Artificial Intelligence Applications in Dentistry

7 Job-ready AI Applications in Construction

7 Out-of-the-Box Applications of AI in Manufacturing

Previously CEO at Aipoly - First smartphone engine for convolutional neural networks. Management & Stats grad at Cass Business School and Singularity University. Never had a real job.

“Collecting user feedback and using human-in-the-loop methods for quality control are crucial for improving Al models over time and ensuring their reliability and safety. Capturing data on the inputs, outputs, user actions, and corrections can help filter and refine the dataset for fine-tuning and developing secure ML solutions.”

Building AI products? This guide breaks down the A to Z of delivering an AI success story.

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CS 7476 Advanced Computer Vision

Spring 2024, tr 12:30 to 1:45, molecular sciences and engineering g011 instructor: james hays ta: akshay krishnan.

Course Description

Course requirements, reading and discussion topics, class participation, presentation(s), semester group projects, prerequisites.

• Computer Vision CS 4476 / 6476
• Machine Learning
• Deep Learning
• Computational Photography
• 20% Reading summaries and questions posted to Canvas
• 10% Classroom participation and attendance
• 15% Leading discussion for particular research paper
• 20% Semester project updates
• 35% Final semester project presentation and writeup

Office Hours:

Tentative schedule.

Comments, questions to James Hays .

CS231A: Computer Vision, From 3D Reconstruction to Recognition

Course project.

The Course Project is an opportunity for you to apply what you have learned in class to a problem of your interest.

Potential projects usually fall into these two tracks:

• Applications. If you're coming to the class with a specific background and interests (e.g. biology, engineering, physics), we'd love to see you apply computer vision to problems related to your particular domain of interest. Pick a real-world problem and apply the techniques covered in the class (or even beyond the class) to solve it!
• Models. You can build a new model (algorithm) for computer vision, or a new variant of existing models, and apply it to tackle vision tasks. This track might be more challenging, and sometimes leads to a piece of publishable work. Talk to the course staff if you would like to pursue this route for more ideas!

To help with coming up with ideas for your project, take a look at the projects from 2021 or 2022 .

You might look at recent deep learning publications from top-tier vision conferences, as well as other resources below.

• Awesome Deep Vision
• CVPR : IEEE Conference on Computer Vision and Pattern Recognition
• ICCV : International Conference on Computer Vision
• ECCV : European Conference on Computer Vision
• NIPS : Neural Information Processing Systems
• ICLR : International Conference on Learning Representations
• Kaggle challenges : An online machine learning competition website.

You are welcome to come to our office hours to brainstorm and suggest your project ideas. We also provide a list of popular computer vision datasets:

• Meta Pointer: A large collection organized by CV Datasets.
• Yet another Meta pointer
• ImageNet : a large-scale image dataset for visual recognition organized by WordNet hierarchy
• SUN Database : a benchmark for scene recognition and object detection with annotated scene categories and segmented objects
• Places Database : a scene-centric database with 205 scene categories and 2.5 millions of labelled images
• NYU Depth Dataset v2 : a RGB-D dataset of segmented indoor scenes
• Microsoft COCO : a new benchmark for image recognition, segmentation and captioning
• Flickr100M : 100 million creative commons Flickr images
• Labeled Faces in the Wild : a dataset of 13,000 labeled face photographs
• Human Pose Dataset : a benchmark for articulated human pose estimation
• YouTube Faces DB : a face video dataset for unconstrained face recognition in videos
• UCF101 : an action recognition data set of realistic action videos with 101 action categories
• HMDB-51 : a large human motion dataset of 51 action classes
• Visual Genome : a large-scale dataset that connects structured image concepts to natural language
• ObjectNet3D : a large-scale database aligning images with 3D shapes

Important Dates

Grading policy.

• Project Proposal: 4%
• Midterm Project Report: 10%
• Final Project Report: 22%
• Final Project Presentation: 10%

Project Proposal

• What is the problem that you will be investigating? Why is it interesting?
• What reading will you examine to provide context and background? Additionally, has is some prior work related to this problem? Please provide at least 2 specific citations.
• What method or algorithm are you proposing? If there are existing implementations, will you use them and how? How do you plan to improve or modify such implementations?
• What data will you use, if any? If you are collecting new datasets, how do you plan to collect them?
• How will you evaluate your results? Qualitatively, what kind of results do you expect (e.g. plots or figures)? Quantitatively, what kind of analysis will you use to evaluate and/or compare your results (e.g. what performance metrics or statistical tests)?
• By what dates will you complete certain parts of your project? List specific goals for the midterm progress report.

Midterm Progress Report

• Title, Author(s)
• Introduction: this section introduces your problem, and the overall plan for approaching your problem
• Problem statement: Describe your problem precisely specifying the dataset to be used, expected results and evaluation
• Technical Approach: Describe the methods you intend to apply to solve the given problem
• Intermediate/Preliminary Results: State and evaluate your results up to the current date

Submission : Please upload a PDF file to the assignments tab on Gradescope . If you are working in a team, please use the team function on Gradescope. The late days are counted by the timestamp of the last submission in the team.

Final Submission

• A PDF file of your final report
• A link to your Git repository. Please put this somewhere in your final report (and add a member of the course staff if it is a private repository).
• Abstract: It should not be more than 300 words
• Background/Related Work: This section discusses relevant literature for your project
• Approach: This section details the framework of your project. Be specific, which means you might want to include equations, figures, plots, etc
• Experiments/Analysis: This section begins with what kind of experiments you're doing, what kind of dataset(s) you're using, and what is the way you measure or evaluate your results. It then shows in details the results of your experiments. By details, we mean both quantitative evaluations (show numbers, figures, tables, etc) as well as qualitative results (show images, example results, etc).
• Conclusion: What have you learned? Suggest future ideas.
• References: This is absolutely necessary.
• Source code (if your project proposed an algorithm, or code that is relevant and important for your project.).
• Cool videos, interactive visualizations, demos, etc.
• All of your source code. Instead give a link to a Git repository
• Various ordinary data preprocessing scripts.
• Any code that is larger than 1MB.
• Model checkpoints.
• A computer virus.

Project Presentations

Presentation contents.

• Introduction: Introduce the motivation and your problem, and then relevant prior work and approaches for this problem (1 minute)
• Approach: Provide an overview of your approach and highlight the key technical aspects you worked on. If you have not fully finished implementing your approach, highlight what parts are done and which are still planned for the final report. (2 minutes)
• Experiments and evaluation: Explain the experimental setup and summarize the quantitative results (numbers, figures, tables, etc) and qualitative results (images, example results, etc). If the results are not as expected, explain what the challenges are and how you plan to improve the results in the final report. (2 minutes)
• 20% for problem statement, motivation, and background
• 30% for technical approach
• 30% for sufficient and informative quantitative and qualitative results
• 10% for visual style
• 10% for addressing questions raised during Q&A

Example Project Reports

• Winter 2021
• Spring 2016
• Winter 2015

Collaboration Policy

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Ready-to-use Presentations

Pick a topic to present with ready-made presentations, introduction to deep learning for computer vision.

In this workshop, you will learn the basics of Deep Learning for Computer Vision using one of the popular frameworks: PyTorch or Tensorflow . You can chose one of the frameworks that you will use.

Module Source Link

The workshop is based on the following Learn Modules (you will be using one of them according to the framework of your choice):

• Introduction to Computer Vision with PyTorch
• Introduction to Computer Vision with Tensorflow

In this workshop, we will learn how to determine the breed of a dog or a cat from a photograph using neural networks. It is an example of more general task called image classification .

🎥 Click this image to watch Dmitry walk you through the workshop

Pre-Learning

If it is the first time you hear about neural networks and frameworks such as PyTorch or Tensorflow - we recommend taking one of the introductory modules on Microsoft Learn:

• Introduction to PyTorch
• Introduction to TensorFlow using Keras

You can also read this short introduction to Neural Networks instead.

Prerequisites

You do not need anything installed on your machine, as you will be using Microsoft Learn Sandbox to carry out the exercise.

What students will learn

Imagine you need to develop and application for pet nursery to catalog all pets. One of the great features of such an application would be automatically discovering the breed from a photograph. This can be successfully done using neural networks.

We will use the Oxford-IIIT pets dataset that contains 35 different breeds of dogs and cats, and build a model that can recognize breed from the picture.

Milestone 0 (optional): Get Introduced to Deep Learning

If you are completely new to Deep Learning, you may want to go over short introduction to Neural Networks . To gain even deeper understanding of what does on, you may take one of the courses depending on the framework that you chose to use:

It is not strictly necessary to follow the workshop, but you will feel much more confident. It is better that you do it before the workshop as pre-reading, since that takes around 1 hour.

Milestone 1: Start the Sandbox

Depending on the framework of your choice, start one of the Learn Modules:

• Introduction to Computer Vision with TensorFlow

You may read through the first few units, up to and including the unit Use convolutional neural network .

Going through the module includes starting the Jupyter Notebook sandbox . Feel free to start the sandbox, but be aware that the virtual machine is allocated for a limited amount of time (around 2 hours). After this time, or in case of long inactivity time, your changes will be lost, and you would have to start from scratch, including downloading the dataset.

Stop at the unit Use Convolutional Neural Network , go to the end of the notebook sandbox, and make sure that you can create new cells and execute code.

We suggest you write your solution inside the sandbox , using the cells at the end of the notebook. Learn Jupyter Sandbox supports GPU, which makes training your model faster. Also, you can always refer to the code in the cells above, in case you need to copy-paste parts of the code.

If you want to work on the solution on your own machine, you can use Faces.ipynb Notebook as a starting point, and [Pets.ipynb] for final optional milestone. However, having a GPU is heavily recommended.

Milestone 2: Getting the Data

For our first task, we will use simplified PetFaces dataset, which was derived from original Oxford-IIIT Pets Dataset by cutting out the pet’s face, and arranging all files for each breed to be in a separate directory.

To download the dataset onto the sandbox, use the following code (copy-paste it into Jupyter cell and run it):

This will create a directory called petfaces on your sandbox virtual machine.

You can try to plot the dataset at this point. Feel free to use the following code to display the list of images:

Next, try to use functions from PyTorch/Tensorflow frameworks to load images from disk and prepare them for classification. There are functions that can take a directory with image files (where each class of images is in its own subdirectory) and return the dataset together with classes:

• torchvision.datasets.ImageFolder for PyTorch
• tf.keras.preprocessing.image_dataset_from_directory for Tensorflow/Keras

You also need to split the original dataset into two datesets: train (that contains 80% of data) and test (that contains 20%). Functions above handle dataset splitting automatically. When creating datasets, you can also divide images into minibatches, of 16-64 images.

While loading images, you also need to take a few additional steps:

• Resize all images to the same size. Since most of the images are close to square aspect ratio, select square image size, eg. 128x128 or 224x224.
• Convert all images to tensors
• Normalize all images, so that input data is in the range [0..1]. This is a standard step in preparing data for neural network training. In the simplest case, we can assume that all pixel intensities are within 0..255 range, so we just need to divide by 255 (converting to float datatype before that). In PyTorch, normalization is automatically handled by ToTensor transform.

Most of those steps in PyTorch can be implemented using transformations ( learn more ), while in Tensorflow you can just specify different parameters for image_dataset_from_directory function.

At the end, you might want to plot the first few images of the minibatch to make sure that everything is loaded correctly. You can use the same display_images function, which accepts tensors as input.

Milestone 3: Define and Train Neural Network

Now that we have the data, it’s time to define neural network architecture and train it. You can take the inspiration from the code you have in Microsoft Learn module, keeping in mind the following things:

• Since the initial image size is rather big (suggested size 128x128), you need a few convolutional layers (at least 3).
• Use combination of convolution - max pooling
• You can have 1 or 2 final Dense layers

In order for the training to work correctly, we need to be especially careful about using the right combination of final activation function and loss function. While on the diagram above we indicate that softmax is used to normalize network outputs to produce probabilities before feeding them into loss function, some frameworks (eg. PyTorch) include softmax normalization into the loss function itself. In particular:

• In PyTorch, final layer does not need an activation function, and you can use CrossEntropyLoss as a loss function. It also expects class number , and not one-hot encoded vector as target label.
• In TensorFlow, use softmax as activation function, and sparse_categorical_crossentropy as a loss function. The term sparse means that it expects a class number as a target, while categorical_crossentropy expects one-hot encoded vectors.

Next, train the neural network for a few epochs (~10), observing both training and validation accuracy during training.

For PyTorch, feel free to use the train function defined in the learn module to train your network. If you want deeper understanding of how PyTorch training works, you can define your own train function from scratch using the one from Learn Module as an inspiration. Also, keep in mind that you need to move both the model and data to GPU during training using .to() , in order to take advantage of GPU acceleration.

You can them plot the graph of training and validation accuracy, which should look something like this:

What can you say from this graph about overfitting? What is the accuracy of your model according to the graph?

We have done the main part of our tutorial - we now have the model that can classify a pet into 35 different categories with relatively high accuracy! Note that even the accuracy around 50% is not too bad - blind guessing would give us less than 3% accuracy.

You can save the model in order to use it later without re-training.

[Optional] Milestone 4: Compute Top-K Accuracy

When classifying for large number of classes, it often happens that some classes are quite similar to each other. For example, if a model makes a mistake classifying British cat for a Russian Blue, it is not a very big deal, because even human beings often make this mistake. However, confusing a Siamese and Persian cat is not such a light error.

Thus, plain accuracy might not be the best indicator of model’s performance. We can also calculate top-k accuracy , i.e. percentage of cases where correct label is within top k predictions. For example, if for a British cat the model predicted Russian Blue as the top result, and British as a second one - it would be considered a correct case.

Try to calculate top 3 accuracy of the model and see how good it is. Some hints:

• In Tensorflow, use tf.nn.in_top_k function to see if the predictions (output of the model) are in top-k (pass k=3 as parameter), with respect to targets . This function returns a tensor of boolean values, which can be converted to int using tf.cast , and then accumulated using tf.reduce_sum .
• In PyTorch, you can use torch.topk function to get indices of classes with highers probabilities, and then see if the correct class belongs to them. See this for more hints.

This exercise requires better understanding of tensor operations, so do not worry if you cannot figure it out. Searching on the internet for the solution might help.

[Optional] Milestone 5: Classifying original images using Transfer Learning

The images that we were classifying were nicely framed to include just the face of a pet. In real life, we want to create an application that will take a normal photo of a pet and be able to classify it as well. Let’s take the original Oxford Pets dataset and see how accurate the model can get.

There are solution notebooks available for PyTorch and TensorFlow .

Before starting the exercise, study the next unit on Transfer learning in the Learn module. Do this exercise at the end of the sandbox notebook in Transfer Learning section.

Use the following code to download the dataset:

Notice that all files are in one images directory, but they include the class name in the file name. To use the same loading code as in the previous section, we need to move file into different directories per class. If you are not sure how to do it, refer to the solution files.

If you try to train the model using the neural network from the previous section, you are likely go get low accuracy (you can try it if you want). In cases like this, it makes sense to use pre-trained networks and transfer learning .

Both TensorFlow/Keras and PyTorch allow you to easily load pre-trained network models, such as VGG-16 or ResNet 50, which can be used as feature extractors. In this case, pre-trained model weights are automatically loaded from the Internet.

Note : When running in the Microsoft Learn sandbox, access to arbitrary Internet resources is limited. You can use the following code to load pre-trained ResNet-50 model in Tensorflow. You may also look at the original Microsoft Learn content to see how they handle loading pre-trained networks.

You can construct one neural network for transfer learning, but keep in mind the following:

• In TensorFlow/Keras, use tf.keras.resnet50.preprocess_input (substitute resnet50 for the network you are using)
• In PyTorch, use the code for preprocessing provided in the Learn module
• You need to freeze the weights of the pre-trained network, otherwise the weights would be destroyed by the first passes of back propagation

With transfer learning, you should be able to achieve the accuracy around 80-90% on the raw data without much fine-tuning of the model.

In this workshop, we have learnt about Deep Learning application to computer vision and image classification. Here are some ideas for further exploration:

• Explore how neural networks can be used for other computer vision tasks - object detection, instance segmentation, etc.
• Explore how neural networks can be used to deal with text - here are corresponding modules for PyTorch and TensorFlow
• Think about how you can deploy your model to use it from mobile application

Optional Transfer Knowledge activity

Now that you have trained the model, you can try to build a complete mobile application that will recognize the breed of cats/dogs. There are two possible ways to implement it:

• Use the same transfer learning approach to train lightweight mobilenet model that can be deployed directly on mobile device
• Deploy the model to Azure as a REST service, and have your mobile application call it to perform inference. You can use Azure Functions or Azure ML Cluster

Be sure to give feedback about this workshop !

Code of Conduct

CS4670/5670 - Introduction to Computer Vision

• Geometry / Physics of image formation
• Properties of images and basic image processing
• 3D reconstruction
• Grouping (of image pixels into objects)
• Machine learning in computer vision: basics, hand-designed feature vectors, convolutional networks
• Detecting and localizing objects

Office Hour Calendar

Lectures / notes:.

Computer vision is a field of artificial intelligence (AI) that uses machine learning and neural networks to teach computers and systems to derive meaningful information from digital images, videos and other visual inputs—and to make recommendations or take actions when they see defects or issues.  

If AI enables computers to think, computer vision enables them to see, observe and understand. 

Computer vision works much the same as human vision, except humans have a head start. Human sight has the advantage of lifetimes of context to train how to tell objects apart, how far away they are, whether they are moving or something is wrong with an image.

Computer vision trains machines to perform these functions, but it must do it in much less time with cameras, data and algorithms rather than retinas, optic nerves and a visual cortex. Because a system trained to inspect products or watch a production asset can analyze thousands of products or processes a minute, noticing imperceptible defects or issues, it can quickly surpass human capabilities.

Computer vision is used in industries that range from energy and utilities to manufacturing and automotive—and the market is continuing to grow. It is expected to reach USD 48.6 billion by 2022. 1

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Computer vision needs lots of data. It runs analyses of data over and over until it discerns distinctions and ultimately recognize images. For example, to train a computer to recognize automobile tires, it needs to be fed vast quantities of tire images and tire-related items to learn the differences and recognize a tire, especially one with no defects.

Two essential technologies are used to accomplish this: a type of machine learning called deep learning and a convolutional neural network (CNN).

Machine learning uses algorithmic models that enable a computer to teach itself about the context of visual data. If enough data is fed through the model, the computer will “look” at the data and teach itself to tell one image from another. Algorithms enable the machine to learn by itself, rather than someone programming it to recognize an image.

A CNN helps a machine learning or deep learning model “look” by breaking images down into pixels that are given tags or labels. It uses the labels to perform convolutions (a mathematical operation on two functions to produce a third function) and makes predictions about what it is “seeing.” The neural network runs convolutions and checks the accuracy of its predictions in a series of iterations until the predictions start to come true. It is then recognizing or seeing images in a way similar to humans.

Much like a human making out an image at a distance, a CNN first discerns hard edges and simple shapes, then fills in information as it runs iterations of its predictions. A CNN is used to understand single images. A recurrent neural network (RNN) is used in a similar way for video applications to help computers understand how pictures in a series of frames are related to one another.

Scientists and engineers have been trying to develop ways for machines to see and understand visual data for about 60 years. Experimentation began in 1959 when neurophysiologists showed a cat an array of images, attempting to correlate a response in its brain. They discovered that it responded first to hard edges or lines and scientifically, this meant that image processing starts with simple shapes like straight edges. 2

At about the same time, the first computer image scanning technology was developed, enabling computers to digitize and acquire images. Another milestone was reached in 1963 when computers were able to transform two-dimensional images into three-dimensional forms. In the 1960s, AI emerged as an academic field of study and it also marked the beginning of the AI quest to solve the human vision problem.

1974 saw the introduction of optical character recognition (OCR) technology, which could recognize text printed in any font or typeface. 3   Similarly, intelligent character recognition (ICR) could decipher hand-written text that is using neural networks. 4  Since then, OCR and ICR have found their way into document and invoice processing, vehicle plate recognition, mobile payments, machine conversion and other common applications.

In 1982, neuroscientist David Marr established that vision works hierarchically and introduced algorithms for machines to detect edges, corners, curves and similar basic shapes. Concurrently, computer scientist Kunihiko Fukushima developed a network of cells that could recognize patterns. The network, called the Neocognitron, included convolutional layers in a neural network.

By 2000, the focus of study was on object recognition; and by 2001, the first real-time face recognition applications appeared. Standardization of how visual data sets are tagged and annotated emerged through the 2000s. In 2010, the ImageNet data set became available. It contained millions of tagged images across a thousand object classes and provides a foundation for CNNs and deep learning models used today. In 2012, a team from the University of Toronto entered a CNN into an image recognition contest. The model, called AlexNet, significantly reduced the error rate for image recognition. After this breakthrough, error rates have fallen to just a few percent. 5

Access videos, papers, workshops and more.

There is a lot of research being done in the computer vision field, but it doesn't stop there. Real-world applications demonstrate how important computer vision is to endeavors in business, entertainment, transportation, healthcare and everyday life. A key driver for the growth of these applications is the flood of visual information flowing from smartphones, security systems, traffic cameras and other visually instrumented devices. This data could play a major role in operations across industries, but today goes unused. The information creates a test bed to train computer vision applications and a launchpad for them to become part of a range of human activities:

• IBM used computer vision to create My Moments for the 2018 Masters golf tournament. IBM Watson® watched hundreds of hours of Masters footage and could identify the sights (and sounds) of significant shots. It curated these key moments and delivered them to fans as personalized highlight reels.
• Google Translate lets users point a smartphone camera at a sign in another language and almost immediately obtain a translation of the sign in their preferred language. 6
• The development of self-driving vehicles relies on computer vision to make sense of the visual input from a car’s cameras and other sensors. It’s essential to identify other cars, traffic signs, lane markers, pedestrians, bicycles and all of the other visual information encountered on the road.
• IBM is applying computer vision technology with partners like Verizon to bring intelligent AI to the edge and to help automotive manufacturers identify quality defects before a vehicle leaves the factory.

Many organizations don’t have the resources to fund computer vision labs and create deep learning models and neural networks. They may also lack the computing power that is required to process huge sets of visual data. Companies such as IBM are helping by offering computer vision software development services. These services deliver pre-built learning models available from the cloud—and also ease demand on computing resources. Users connect to the services through an application programming interface (API) and use them to develop computer vision applications.

IBM has also introduced a computer vision platform that addresses both developmental and computing resource concerns. IBM Maximo® Visual Inspection includes tools that enable subject matter experts to label, train and deploy deep learning vision models—without coding or deep learning expertise. The vision models can be deployed in local data centers, the cloud and edge devices.

While it’s getting easier to obtain resources to develop computer vision applications, an important question to answer early on is: What exactly will these applications do? Understanding and defining specific computer vision tasks can focus and validate projects and applications and make it easier to get started.

Here are a few examples of established computer vision tasks:

• Image classification sees an image and can classify it (a dog, an apple, a person’s face). More precisely, it is able to accurately predict that a given image belongs to a certain class. For example, a social media company might want to use it to automatically identify and segregate objectionable images uploaded by users.
• Object detection can use image classification to identify a certain class of image and then detect and tabulate their appearance in an image or video. Examples include detecting damages on an assembly line or identifying machinery that requires maintenance.
• Object tracking follows or tracks an object once it is detected. This task is often executed with images captured in sequence or real-time video feeds. Autonomous vehicles, for example, need to not only classify and detect objects such as pedestrians, other cars and road infrastructure, they need to track them in motion to avoid collisions and obey traffic laws. 7
• Content-based image retrieval uses computer vision to browse, search and retrieve images from large data stores, based on the content of the images rather than metadata tags associated with them. This task can incorporate automatic image annotation that replaces manual image tagging. These tasks can be used for digital asset management systems and can increase the accuracy of search and retrieval.

Put the power of computer vision into the hands of your quality and inspection teams. IBM Maximo Visual Inspection makes computer vision with deep learning more accessible to business users with visual inspection tools that empower.

IBM Research is one of the world’s largest corporate research labs. Learn more about research being done across industries.

Learn about the evolution of visual inspection and how artificial intelligence is improving safety and quality.

Learn more about getting started with visual recognition and IBM Maximo Visual Inspection. Explore resources and courses for developers.

Read how Sund & Baelt used computer vision technology to streamline inspections and improve productivity.

Learn how computer vision technology can improve quality inspections in manufacturing.

Unleash the power of no-code computer vision for automated visual inspection with IBM Maximo Visual Inspection—an intuitive toolset for labelling, training, and deploying artificial intelligence vision models.

1. https://www.forbes.com/sites/bernardmarr/2019/04/08/7-amazing-examples-of-computer-and-machine-vision-in-practice/#3dbb3f751018  (link resides outside ibm.com)

2.   https://hackernoon.com/a-brief-history-of-computer-vision-and-convolutional-neural-networks-8fe8aacc79f3 (link resides outside ibm.com)

3. Optical character recognition, Wikipedia  (link resides outside ibm.com)

4. Intelligent character recognition, Wikipedia  (link resides outside ibm.com)

5. A Brief History of Computer Vision (and Convolutional Neural Networks), Rostyslav Demush, Hacker Noon, February 27, 2019  (link resides outside ibm.com)

6. 7 Amazing Examples of Computer And Machine Vision In Practice, Bernard Marr, Forbes, April 8, 2019  (link resides outside ibm.com)

7. The 5 Computer Vision Techniques That Will Change How You See The World, James Le, Heartbeat, April 12, 2018  (link resides outside ibm.com)

Advanced Topics in Computer Vision

Enrollment Comments : Same course as ECE 281B. Advanced topics in computer vision: image sequence analysis, spatio-temporal filtering, camera calibration and hand-eye coordination, robot navigation, shape representation, physically-based modeling, regularization theory, multi-sensory fusion, biological models, expert vision systems, and other topics selected from recent research papers.

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Introduction to Computer Vision

This course is part of Computer Vision for Engineering and Science Specialization

Taught in English

Some content may not be translated

Instructors: Amanda Wang +4 more

Instructors

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Recommended experience

Intermediate level

If you are new to image data, it’s recommended to first complete the Image Processing for Engineering and Science specialization.

What you'll learn

Use common algorithms for feature detection, extraction, & matching

Perform image registration by identifying control points & estimating geometric transformations

Complete a final project where you stitch together images from NASA’s Mars Curiosity Rover

Combine images with image stitching to create panorama images

Skills you'll gain

• Image Processing
• Computer Vision

Image Stitching

Image registration, details to know.

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There are 4 modules in this course

In the first course of the Computer Vision for Engineering and Science specialization, you’ll be introduced to computer vision. You'll learn and use the most common algorithms for feature detection, extraction, and matching to align satellite images and stitch images together to create a single image of a larger scene.

Features are used in applications like motion estimation, object tracking, and machine learning. You’ll use features to estimate geometric transformations between images and perform image registration. Registration is important whenever you need to compare images of the same scene taken at different times or combine images acquired from different scientific instruments, as is common with hyperspectral and medical images. You will use MATLAB throughout this course. MATLAB is the go-to choice for millions of people working in engineering and science, and provides the capabilities you need to accomplish your computer vision tasks. You will be provided free access to MATLAB for the course duration to complete your work. To be successful in this course, it will help to have some prior image processing experience. If you are new to image data, it’s recommended to first complete the Image Processing for Engineering and Science specialization.

Introduction to Features

What's included.

2 videos 4 readings 1 quiz 1 discussion prompt

2 videos • Total 5 minutes

• Computer Vision for Engineering and Science • 3 minutes • Preview module
• Introduction: What Are Features? • 2 minutes

4 readings • Total 40 minutes

• Prerequisite Knowledge • 5 minutes
• Meet Your Instructors • 5 minutes
• Download and Install MATLAB • 15 minutes
• Course Files • 15 minutes

1 quiz • Total 15 minutes

• Graded Quiz: Introduction to Features • 15 minutes

1 discussion prompt • Total 10 minutes

• Choosing a Feature Type • 10 minutes

Working With Features

3 videos 3 readings 1 quiz 1 discussion prompt

3 videos • Total 13 minutes

• Detecting Features • 4 minutes • Preview module
• Extracting Features • 4 minutes
• Matching Features • 4 minutes

3 readings • Total 85 minutes

• Refining Feature Detection • 45 minutes
• Feature Detection and Extraction Reference • 10 minutes
• Matching Features • 30 minutes

1 quiz • Total 45 minutes

• Graded Quiz: Detecting, Extracting, and Matching Features • 45 minutes
• Feature Detection on Your Own Images • 10 minutes

3 videos 1 reading 2 quizzes

3 videos • Total 18 minutes

• Estimating and Applying Geometric Transformations • 6 minutes • Preview module
• Feature-Based Image Registration • 7 minutes
• Visually Selecting Control Points • 4 minutes

1 reading • Total 20 minutes

• Practice With Geometric Transformations • 20 minutes

2 quizzes • Total 75 minutes

• Graded Quiz: Image Registration • 60 minutes
• Concept Check: Geometric Transformations • 15 minutes

2 videos 4 readings 2 quizzes 1 app item 1 discussion prompt 1 plugin

2 videos • Total 8 minutes

• Stitching Images Together • 6 minutes • Preview module
• Summary of Introduction to Computer Vision • 1 minute

4 readings • Total 67 minutes

• Introduction to Image Stitching • 30 minutes
• Stitching Images Example • 20 minutes
• Mars Rover: Your Final Project • 15 minutes
• What's Next? • 2 minutes

2 quizzes • Total 40 minutes

• Project: Check the Panorama Image • 30 minutes
• Concept Check: Image Stitching • 10 minutes

1 app item • Total 30 minutes

• Project: Check Your Registration Function • 30 minutes
• Project: Third Mars Rover Image • 10 minutes

1 plugin • Total 5 minutes

• Share Your Feedback • 5 minutes

Accelerating the pace of discovery, innovation, development, and learning in engineering and science.

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Machine Learning for Computer Vision

Object Tracking and Motion Detection with Computer Vision

Coursera Project Network

Implementando modelo Computer Vision en Amazon Sagemaker

Guided Project

Introduction to Image Processing

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Open Computer Vision Library

A Comprehensive Guide to Computer Vision Research in 2024

bharat January 17, 2024 Leave a Comment AI Careers Tags: ai computer vision computer vision research computer vision research groups deep learning OpenCV

Introduction 

In our earlier blogs , we discussed the best institutes across the world for computer vision research. In this fun read, we’ll look at the different stages of Computer Vision research and how you can go about publishing your research work. Let us delve into them now. Looking to become a Computer Vision Engineer? Check out our Comprehensive Guide !

Table of Contents

• Introduction
• Different Stages of Computer Vision

Research Publications

Different stages of computer vision research.

Computer Vision Research can be put into various stages, one building to the next. Let us look at them in detail.

Identification of Problem Statement

Computer Vision research starts with identifying the problem statement. It is a crucial step in defining the scope and goals of a research project. It involves clearly understanding the specific challenge or task the researchers aim to address using computer vision techniques. Here are the steps involved in identifying the problem statement in computer vision research:

• Problem Statement Analysis: The first step is to pinpoint the specific application domain within computer vision. This could be related to object recognition in autonomous vehicles or medical image analysis for disease detection.
• Defining the problem: Next, we define the precise problem we want to solve within that domain, like classifying images of animals or diagnosing diseases from X-rays.
• Understanding the objectives: We need to understand the research objectives and outline what we intend to achieve through this project. For instance, improving classification accuracy or reducing false positives in a medical imaging system.
• Data availability: Next, we need to analyze the availability of data for our project. Check if existing datasets are suitable for our task or if we need to gather our own data, like collecting images of specific objects or medical cases.
• Review: Conduct a thorough review of existing research and the latest methodologies in the field. This will help you gain insights into the current state-of-the-art techniques and the challenges others have faced in similar projects.
• Question formulation: Once we review the work, we can formulate research questions to guide our experiments. These questions could address specific aspects of our computer vision problem and help better structure our research.
• Metrics: Next, we define the evaluation metrics that we’ll use to measure the performance of our vision system. Some common metrics include accuracy, precision, recall, and F1-score.
• Highlighting: Highlight how solving the problem will have an effect in the real world. For instance, improving road safety through better object recognition or enhanced medical diagnoses for early treatment.
• Research Outline: Finally, outline the research plan, and detail the methodology employed for data collection, model development, and evaluation. A structured outline will ensure we are on the right track throughout our research project.

Let us move to the next step, data collection and creation.

Dataset Collection and Creation

Creating and gathering datasets is one of the key building blocks in computer vision research. These datasets facilitate the algorithms and models used in vision systems. Let us see how this is done.

• Firstly we need to know what we are trying to solve. For instance, are we training models to recognize dogs in photos or identify anomalies in medical images?
• Now, we’ll need images or videos. Depending on the research needs, we can find them on public datasets or collect our own.
• Next, we mark up the data. For instance, if you’re teaching a computer to spot dogs in pictures, you’ll draw boxes around the cars and say, “These are dogs!”
• Raw data can be a mess. We may need to resize images, adjust colors, or add more examples to ensure our dataset is neat and complete.
• 1-part for training your model
• 1-part for fine-tuning
• 1-part for testing how well your model works
• Next, ensure the dataset fairly represents the real world and doesn’t favor one group or category too much.

One can also share their dataset and research with others for inputs and improvements. Dataset collection and creation are vital in computer vision research.

Exploratory Data Analysis

Exploratory Data Analysis (EDA) briefly analyzes a dataset to answer preliminary questions and guide the modeling process. For instance, this could be looking for patterns across different classes. This is not only used by Computer Vision Engineers but also Data Scientists to ensure that the data they provide are aligned with different business goals or outcomes. This step involves understanding the specifics of image datasets. For instance, EDA is used to spot anomalies, understand data distribution, or gain insights to further model training. Let us look at the role of EDA in model development.

• With EDA, one can develop data preprocessing pipelines and choose data augmentation strategies.
• We can analyze how the findings from EDA can affect the choice of model architecture. For instance, the need for some convolutional layers or input images.
• EDA is also crucial for advanced Computer Vision tasks like object detection, segmentation, and image generation backed by studies.

Now let us dive into the specifics of EDA methods and preparing image datasets for model development.

Visualization

• Sample Image Visualization involves displaying a random set of images from the dataset. This is a fundamental step where we get an idea of the data like lighting conditions or variations in image quality. From this, one can infer the visual diversity and any challenges in the dataset.
• Analyzing the pixel distribution intensities offers insights into the brightness and contrast variations across the dataset if there is any need for image enhancement techniques.
• Next, creating histograms for different color channels gives us a better understanding of the color distribution of the dataset. This is a crucial step for tasks such as image classification.

Image Property Analysis

• Another crucial part is understanding the resolution and the aspect ratio of images in the dataset. It helps make decisions like resizing the image or normalizing the aspect ratio, which is crucial in maintaining consistency in input data for neural networks.
• Analyzing the size and distribution of annotated objects can be insightful in datasets with annotations. This influences the design layers in the neural network and understanding the scale of objects.

Correlation Analysis

• With some advanced EDA processes like high dimensional image data, analyzing the relation between different features is helpful. This would aid with dimensionality reduction or feature selection.
• Next, it is crucial to understand the spatial correlations within images, like the relationship between different regions in an image. It helps in the development of spatial hierarchies in neural networks. 

Class Distribution Analysis

• EDAs are important in understanding the imbalances in class distribution. This is key in classification tasks where imbalanced data can lead to biased models.
• Once the imbalances are identified, we can adopt techniques like undersampling majority classes or oversampling minority classes during model training. 

Geometric Analysis

• Understanding geometric properties like edges, shapes, and textures in images offers insights into the features important for the problem at hand. We can make informed decisions on selecting specific filters or layers in the network architecture. 
• It’s important to understand how different morphological transformations affect images for segmentation and object detection tasks.

Sequential Analysis

The sequential analysis applies to video data. 

• For instance, analyzing changes between frames can offer information like motion, temporal consistency, or the need for temporal modeling in video datasets or video sequences.
• Identifying temporal variations and scene changes gives us insights into the dynamics within the video data that are crucial for tasks like event detection or action recognition.   

Now that we’ve discussed Exploratory Data Analysis and some of its techniques let us move to the next stage in Computer Vision research, defining the model architecture.

Defining Model Architecture 

Defining a model architecture is a critical component of research in computer vision, as it lays the foundation for how a machine learning model will perceive, process, and interpret visual data. We analyze a model that impacts the ability of the model to learn from visual data and perform tasks like object detection or semantic segmentation. 

Model architecture in computer vision refers to the structural design of an artificial neural network. The architecture defines how the model processes input images, extracts features, and makes predictions and classifications.  

What are the components of a model architecture? Let’s explore them.

Input Layer

This is where the model receives the image data, mostly in the form of a multi-dimensional array. For colored images, this could be a 3D array where color channels show RGB values. Preprocessing steps like normalization are applied here.

Convolutional Layers

These layers apply a set of filters to the input. Every filter convolves across the width and height of the input volume, computing the dot product between the entries of the filter and the input, producing a 2D activation map for each filter. Preserving the relationship between pixels captures spatial hierarchies in the image.

Activation Functions

Activation functions facilitate networks to learn more complex representations by introducing them to non-linear properties. For instance, the ReLU (Rectified Linear Unit) function applies a non-linear transformation (f(x) = max(0,x)) that retains only positive values and sets all negative values to zero. Other functions include sigmoid and tanh.

Pooling Layers

These layers are used to perform a down-sampling operation along the spatial dimensions (width, height), reducing the number of parameters and computations in the network. For instance, Max pooling, a common approach, takes the maximum value from a set of values in the filter area, is a common approach. This operation offers spatial variance, making the recognition of features in the input invariant to scale and orientation changes.

Fully Connected Layers 

Here, the layers connect every neuron in one layer to every neuron in the next layer. In a CNN, the high-level reasoning in the neural network is performed via these dense layers. Typically, they are positioned near the end of the network and are used to flatten the output of convolutional and pooling layers to form a single vector of features used for final classification or regression tasks.

Dropout Layers

Dropout is a regularization technique where randomly selected neurons are ignored during training. This means that the contribution of these neurons to activate the downstream neurons is removed temporally on the forward pass and any weight updates are not applied to the neuron on the backward pass. This helps in preventing overfitting.

Batch Normalization

In batch normalization, the output from a previous activation layer is normalized by subtracting the batch mean and then dividing it by the standard deviation of the batch. This technique helps stabilize the learning process and significantly reduces the number of training epochs required for deep network training.

Loss Function

The difference between the expected outcomes and the predictions made by the model is quantified by the loss function. Cross-entropy for classification tasks and mean squared error for regression tasks are some of the common loss functions in computer vision.

The optimizer is an algorithm used to minimize the loss function. It updates the network’s weights based on the loss gradient. Some common optimizers include Stochastic Gradient Descent (SGD), Adam, and RMSprop. They use backpropagation to determine the direction in which each weight should be adjusted to minimize the loss.

Output Layer

This is the final layer, where the model’s output is produced. The output layer typically includes a softmax function for classification tasks that converts the outputs to probability values for each class. For regression tasks, the output layer may have a single neuron.

Frameworks like TensorFlow, PyTorch, and Keras are widely used for designing and implementing model architectures. They offer pre-built layers, training routines, and easy integration with hardware accelerators.

Defining a model architecture requires a good grasp of both the theoretical aspects of neural networks and the practical aspects of the specific task.

Training and Validation

Training and validation are crucial in developing a model. They help evaluate a model’s performance, especially when dealing with object detection or image classification tasks.

In this phase, the model is represented as a neural network that learns to recognize image patterns and features by altering its internal parameters iteratively. These parameters are weights and biases related to the network’s layers. Training is key for extracting meaningful features from raw visual data. Let us see how one can go about training a model.

• Acquiring a dataset is the first step. It could be in the form of images or videos for model learning purposes. For robustness, they cover various environmental conditions, variations, and object classes.
• Resizing is where all the input data has the same dimensions for batch processing.
• In Normalization, pixels are standardized to zero mean and unit variance, aiding convergence.
• Augmentation applies random transformations to increase the size of the dataset artificially, thereby improving the model’s ability to generalize.
• Once data preprocessing is done, we must choose the appropriate neural network architecture catering to the specific vision task. For instance, CNNs are widely used for image-related tasks.
• Next, we initialize the model parameters, usually weights, and biases, using random values or pre-trained weights from a model trained on a simple dataset. Transfer learning can significantly improve performance, especially when data is limited.
• Then we can optimize the algorithm to adjust its parameters iteratively with stochastic gradient descent (SGD) or RMSprop. Gradients in relation to the model’s parameters are computed through backpropagation which are used to update the parameters.
• Once the algorithm is optimized, the data is trained in mini-batches through the network, computing the loss for each mini-batch and performing gradient updates. This happens until the loss falls below a predefined threshold.
• Next, we must optimize the training performance and convergence speed by fine-tuning the hyperparameters. This could done by optimizing learning rates, batch sizes, weight regulation terms, or network architectures. 
• We need to assess the model’s performance using validation or test datasets and eventually deploy the model in real-world applications through software integrations or embedded devices.

Now let us move to the next step- Validation.

Validation is fundamental for the quantitative assessment of performance and generalization capabilities of algorithms. It ensures the reliability and effectiveness of the models when applied to real-world data. Validation evaluates the ability of a model to make accurate predictions of previously unseen data hence being able to gauge its ability for generalization.

Now let us explore some of the key techniques involved in validation.

Cross-Validation Techniques

• K-Fold Cross-Validation is the method where the dataset is partitioned into K non-overlapping subsets. The model is trained and evaluated K times, with each fold taking turns as the validation set while the rest serve as the training set. The results are averaged to obtain a robust performance estimate.
• Leave-One-Out Cross-Validation or LOOCV is an extreme form of cross-validation where each data point is used as the validation set while the remaining data points constitute the training set.LOOCV offers an exhaustive evaluation of model performance.

Stratified Sampling

In some imbalanced datasets where a few classes have significantly fewer instances than others, stratified sampling ensures the balance between training and validation sets for the distribution of classes.

Performance Metrics

To assess the model’s performance, a range of performance metrics specified for computer vision tasks are deployed. They are not limited to the following.

• Accuracy is the ratio of the correctly predicted instances to the total number of instances.
• Precision is the proportion of true positive predictions among all positive predictions.
• Recall is the proportion of true positive predictions among all positive instances.
• F1-Score is the harmonic mean of precision and recall.
• Mean Average Precision (mAP)is commonly used in object detection and image retrieval tasks to evaluate the quality of ranked lists of results.

Hyperparameter Tuning

Validation is closely integrated with hyperparameter tuning, where the model’s hyperparameters are systematically adjusted and evaluated using the validation set. Techniques such as grid search, random search, or Bayesian optimization help identify the optimal hyperparameter configuration for the model.

Data Augmentation

Data augmentation techniques are applied to test the model’s robustness and the ability to handle different conditions or transformations during validation to simulate variations in the input data.

Training is where the model learns from labeled data, and Validation is where the model’s learning and generalization capabilities are assessed. They ensure that the final model is robust, accurate, and capable of performing well on unseen data, which is critical for computer vision research.

Hyperparameter tuning refers to systematically optimizing hyperparameters in deep learning models for tasks like image processing and segmentation. They control the learning algorithm’s performance but did not learn from the training data. Fine-tuning hyperparameters are crucial if we wish to achieve accurate results. 

It is the number of training examples used in every forward and backward pass. Large batch sizes offer smoother convergence but need more memory. On the contrary, small batch sizes need less memory and can help escape local minima.

Number of Epochs

The Number of epochs defines how often the entire training dataset is processed during training. Too few epochs can lead to underfitting, and too many can lead to overfitting. 

Learning Rate

This determines the step size during gradient-based optimization. If the learning rate is too high, it can lead to overshooting, causing the loss function to diverge, and if the learning rate is too short, it can cause slow convergence. 

Weight Initialization

The training stability is affected by the initialization of weights. Techniques such as Glorot initialization are designed to address the vanishing gradient problems.

Regularization Techniques

Some techniques like dropout and weight decay aid in preventing overfitting. The model generalization is enhanced through random rotations using data augmentation. 

Choice of Optimizer

The updates during training for model weights are determined by the optimizer. They have their parameters like momentum, decay rates and epsilon.

Hyperparameter tuning is usually approached as an optimization problem. Few techniques like Bayesian optimization efficiently explore the hyperparameter space balancing computational costs and do not slack on the performance. A well-defined hyperparameter tuning includes not just adjusting individual hyperparameters but also also considers their interactions.

Performance Evaluation on Unseen Data 

In the earlier section, we discussed how one must go about doing the training and validation of a model. Now we’ll discuss how to evaluate the performance of a dataset on unseen data.

Training and validation dataset split is paramount when developing and evaluating models. This is not to be confused with the training and validation we discussed earlier for a model. Splitting the dataset for training and validation aids in understanding the model’s performance on unseen data. This ensures that the model generalizes well to new data. Let us look at them.

• A training dataset is a collection of labeled data points for training the model, adjusting parameters, and inferring patterns and features.
• A separate dataset is used for evaluating the model during development for hyperparameter tuning and model selection. This is the Validation dataset. 
• Then there is the test dataset , an independent dataset used for assessing the final performance and generalization ability on unseen data.

Splitting datasets is needed to prevent the model from training on the same data. This would hinder the model’s performance. Some commonly used split ratios for the dataset are 70:30, 80:20, or 90:10. The larger portion is used for training, while the smaller portion is used for validation.

You have put so much effort into your research paper. But how do we publish it? Where do we publish it? How do I find the right computer vision research groups? That is what this section covers, so let’s get to it.

Conferences

There are some top-tier computer vision conferences happening across the globe. They are among the best places to showcase research work, look for future collaborations, and build networks.

Conference on Computer Vision and Pattern Recognition (CVPR)

Also called the CVPR , it is one of the most prestigious conferences in the world of Computer Vision. It is organized by the IEEE Computer Society and is an annual event. It has an amazing history of showcasing cutting-edge research papers in image analysis, object detection, deep learning techniques, and much more. CVPR has set the bar high, placing a strong emphasis on the technical aspects of the submissions. They must meet the following criteria.

Papers must possess an innovative contribution to the field. This could be the development of new algorithms, techniques, or methodologies that can bring advancements in computer vision.

If applicable, the submissions must have mathematical formulations of their methods, like equations and theorem proofs. This offers a solid theoretical foundation for the paper’s approach.

Next, the paper should include comprehensive experimental results involving many datasets and benchmarking against existing models. These are key to demonstrating the effectiveness of your proposed approach.

Clarity – this is a no-brainer; the writing and presentation must be clear and concise. The writers are expected to explain the algorithms, models, and results in a technically sound manner. 

CVPR is an amazing platform for networking and engaging with the community. It’s a great place to meet academics, researchers, and industry experts to collaborate and exchange ideas. The acceptance rate for papers is only 25.8% hence the recognition within the vision community is impressive. It often leads to citations, greater visibility, and potential collaborations with renowned researchers and professionals.

International Conference on Computer Vision (ICCV)

The ICCV is another premier conference held annually once, offering an amazing platform for cutting-edge computer vision research. Much like the CVPR, the ICCV is also organized by the IEEE Computer Society, attracting worldwide visionaries, researchers, and professionals. Topics range from object detection and recognition all the way to computational photography. ICCV invites original papers offering a significant contribution to the field. The criteria for submissions are very similar to the CVPR. They must possess mathematical formulations, algorithms, experimental methodology, and results. ICCV adopts peer review to add a layer of technical rigor and quality to the accepted papers. Submissions usually undergo multiple stages of review, giving detailed feedback on the technical aspects of the research paper. The acceptance rates at ICCV are typically low at 26.2%.

Besides the main conference, the ICCV hosts workshops and tutorials that offer in-depth discussions and presentations in emerging research areas. It also offers challenges and competitions associated with computer vision tasks like image segmentation and object detection. 

Like the CVPR, it offers excellent opportunities for future collaborations, networking with peers, and exchanging ideas. The papers accepted at the ICCV are typically published in the IEEE Computer Society and made available to the vision community. This offers significant visibility and recognition to researchers for papers that are accepted.

European Conference on Computer Vision (ECCV)

The European Conference on Computer Vision, or ECCV , is another comprehensive conference if you are looking for the top computer vision conferences globally. The ECCV lays a lot of emphasis on the scientific and technical quality of the paper. Like the above two conferences we discussed, it emphasizes how the researcher incorporates the mathematical foundations, algorithms, and detailed derivations and proofs with extensive experimental evaluations. 

According to the ECCV formatting guidelines, the research paper ideally ranges from 10 to 14 pages. It adopts a double-blind peer review, where the researchers must make their submissions anonymous to curb any discrepancies.

ECCV also offers huge opportunities for collaborations and establishing connections. With an acceptance rate of 31.8%, a researcher can benefit from academic recognition, high visibility, and citations.

Winter Conference on Applications of Computer Vision (WACV)

WACV is a top international computer vision event with the main conference and a few workshops and tutorials. Much like the other conferences, it is held annually. With an acceptance rate below 30%, it attracts leading researchers and industry professionals. The conference usually takes place in the first week of January. 

As a computer vision researcher, one must publish one’s works in journals to show your findings and give more insights into the field. Let us look at a few of the computer vision journals.

Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

Also called the TPAMI , this journal focuses on the various aspects of machine intelligence, pattern recognition, and computer vision. It offers a hybrid publication permitting traditional or author-paid open-access manuscript submissions. 

With open-access manuscripts, the paper has unrestricted access to it through the IEEE Xplore and Computer Society Digital Library. 

Regarding traditional manuscript submissions, the IEEE Computer Society has various award-winning journals for publication. One can browse through the different topics that fit their research. They often publish special sections on emerging topics. Some factors you need to consider are submission to publications time, bibliometric scores like impact factor, and publishing fees.

International Journal of Computer Vision (IJCV)

The IJCV offers a platform for new research results. With 15 issues a year, the International Journal of Computer Vision offers high-quality, original contributions to the field of computer vision. The length of the articles ranges from 10-page regular articles to up to 30 pages for survey papers that offer state-of-the-art presentations and results. The research must cover mathematical, physics, and computational aspects of computer vision, like image formation, processing, interpretation, machine learning techniques, and statistical approaches. Researchers are not charged to publish on IJCV . It is not only a journal that opens doors for researchers to showcase their papers but also a goldmine of information in deep learning, artificial intelligence, and robotics.

Journal of Machine Learning Research (JMLR)

Established in 2000, JMLR is a forum for electronic and paper publications of comprehensive research papers. This platform covers topics like machine learning algorithms and techniques, deep learning, neural networks, robotics, and computer vision. JMLR is freely available to the public. It is run by volunteers, and the papers undergo rigorous reviews, which serve as a valuable resource for the latest updates in the field.

You’ve invested weeks and months into this paper. Why not get the recognition and credibility your work deserves? The above Journals and Conferences offer the ultimate gateway for a researcher to showcase their works and open up a plethora of opportunities for academic and industry collaborations.

In conclusion, our journey through the intricate world of computer vision research has been a fun one. From the initial stages of understanding the problem statements to the final steps of publication in computer vision research groups, we’ve comprehensively delved into each of them.

There is no research, big or small; each offers its own contributions to the ever-evolving field of the Computer Vision domain. 

We’ve more detailed posts coming your way. Stay tuned! See you guys in the next one!!

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151+ Computer Presentation Topics [Updated 2024]

For both professionals and fans, keeping up with the most recent developments and trends in the rapidly evolving field of technology is essential. One effective way to share and acquire knowledge is through computer presentations. 

Whether you are a seasoned presenter or someone looking to enhance your tech presentation skills, choosing the right topics is key to delivering a compelling and informative session. 

In this blog, we’ll explore various computer presentation topics, their relevance, and provide insights into tailoring presentations for different audiences and occasions.

How do you Tailor Topics According to Audience and Occasion?

Table of Contents

Tailoring topics according to the audience and occasion is a crucial aspect of delivering an effective and engaging presentation. Here are some strategies and considerations to help you customize your computer presentation topics based on your audience and the specific occasion:

• Know Your Audience
• Assess Knowledge Levels: Understand the expertise of your audience. Are they beginners, intermediate users, or experts in the field? This assessment will guide you in selecting the appropriate depth and complexity of your topics.
• Consider Backgrounds: Take into account the professional backgrounds, interests, and industries of your audience. Tailor your examples and case studies to resonate with their experiences.
• Identify Audience Needs and Goals:
• Address Pain Points: If possible, research or survey your audience to identify their challenges and pain points. Tailor your presentation to address these concerns, providing practical solutions and insights.
• Align with Goals: Understand the goals and objectives of your audience. Tailor your topics to align with their aspirations, whether it’s professional development, problem-solving, or staying updated on industry trends.
• Adapt to the Occasion:
• Event Type: Consider the type of event you are presenting at. Is it a conference, workshop, seminar, or a more informal gathering? The format and expectations of the event will influence your choice of topics.
• Time Constraints: Be mindful of the time allotted for your presentation. Tailor the scope and depth of your topics to fit within the designated time frame.
• Customize Content:
• Relevance to Industry: If your audience belongs to a specific industry, tailor your topics to address challenges and innovations relevant to that industry. Provide concrete examples and case studies that resonate with their professional experiences.
• Localize Examples: Consider the cultural context and geographic location of your audience. If possible, use examples and references that are familiar to them, making the content more relatable.
• Engage in Interactivity:
• Q&A Sessions: Plan for interactive sessions, allowing the audience to ask questions. This helps you gauge their interests and tailor your responses to address specific concerns.
• Polls and Surveys: Incorporate interactive elements such as polls or surveys to gather real-time feedback. Use the results to adjust your presentation on the fly if necessary.
• Provide Actionable Takeaways:
• Practical Applications: Tailor your topics to include practical applications and actionable takeaways. Ensure that your audience can apply the knowledge gained from your presentation in their professional or personal endeavors.
• Workshops and Demos: For hands-on sessions, tailor your topics to include workshops or live demonstrations. This enhances the learning experience and allows the audience to see practical implementations.
• Be Adaptable:
• Read the Room: Pay attention to the audience’s reactions during the presentation. Be adaptable and ready to adjust your approach based on their engagement levels and feedback.
• Flexibility in Content: Have backup content or supplementary materials that can be introduced based on audience interest or questions.

Software Development and Programming

• Trends in Programming Languages: A Comprehensive Overview
• Introduction to Python: Basics and Beyond
• Exploring the World of JavaScript Frameworks
• Best Practices in Software Development Methodologies
• The Evolution of Mobile App Development
• Low-Code Platforms: Revolutionizing Software Development
• The Impact of Microservices Architecture on Modern Applications
• DevOps Practices: Streamlining Development and Operations
• Code Review Techniques for Quality Assurance
• GUI vs. Command Line Interfaces: Pros and Cons

Emerging Technologies

• Artificial Intelligence (AI): An Introduction and Applications
• Machine Learning Algorithms: A Deep Dive
• The Role of Natural Language Processing (NLP) in AI
• Computer Vision: Applications and Challenges
• Internet of Things (IoT) and its Transformative Power
• Blockchain Technology: Beyond Cryptocurrencies
• Augmented Reality (AR) and Virtual Reality (VR) in Computing
• Edge Computing: Enhancing Network Performance
• Quantum Computing: A Glimpse into the Future
• 6G Technology: Enabling the Next Generation of Connectivity

Cybersecurity

• Cyber Threats: Types, Trends, and Prevention Strategies
• Ethical Hacking: Unveiling Security Vulnerabilities
• Biometric Security Systems: Enhancing Authentication
• Cryptography: Ensuring Secure Communication
• Security Measures for Computer Networks: A Practical Guide
• Privacy Concerns in the Digital Age: Safeguarding Information
• Incident Response Planning for Cybersecurity
• Cloud Security Best Practices
• Cybersecurity Awareness Training for Employees
• The Future of Cybersecurity: Emerging Challenges

Data Science and Big Data

• Introduction to Data Science: Concepts and Applications
• Data Analysis Techniques: From Descriptive to Predictive Analytics
• Big Data Technologies: Hadoop, Spark, and Beyond
• Data Warehousing: Storing and Retrieving Massive Datasets
• Data Visualization Tools: Making Sense of Complex Data
• Predictive Modeling in Business: Leveraging Data Insights
• Internet of Things (IoT) and Big Data Integration
• Real-Time Analytics: Turning Data into Actionable Insights
• Data Ethics: Navigating the Challenges of Responsible Data Use
• Data-driven Decision Making in Organizations

Computer Hardware and Networking

• Latest Advancements in Computer Hardware
• The Role of Graphics Processing Units (GPUs) in Modern Computing
• Networking Protocols: A Deep Dive into TCP/IP, UDP, and More
• Wireless Technologies: Wi-Fi 6 and Beyond
• Cloud Computing Models: IaaS, PaaS, and SaaS Explained
• Edge Computing vs. Cloud Computing: Choosing the Right Approach
• Green Computing: Sustainable Practices in IT
• Quantum Computing and its Potential Impact on Industry
• 5G Technology: Revolutionizing Mobile Communication
• Wearable Technology: Integrating Computing into Everyday Life

Artificial Intelligence (AI) Applications

• AI in Healthcare: Transforming Diagnosis and Treatment
• AI in Finance: Applications and Risk Management
• AI in Customer Service: Enhancing User Experience
• AI in Education: Personalized Learning and Assessment
• AI in Autonomous Vehicles: Navigating the Future
• AI in Agriculture: Precision Farming and Crop Monitoring
• AI in Cybersecurity: Detecting and Preventing Threats
• AI in Natural Language Processing (NLP): Conversational Interfaces
• AI in Robotics: Innovations and Challenges
• AI in Retail: Personalized Shopping Experiences

Internet and Web Technologies

• Evolution of the Internet: From ARPANET to the Present
• Web Development Trends: Responsive Design and Progressive Web Apps
• Content Management Systems (CMS): Choosing the Right Platform
• E-commerce Platforms: Building Successful Online Stores
• Search Engine Optimization (SEO) Strategies for Web Visibility
• Cloud-based Web Hosting Solutions: Comparisons and Best Practices
• Web Accessibility: Designing Inclusive and User-Friendly Websites
• Social Media Integration: Enhancing Online Presence
• Web Security Best Practices: SSL, HTTPS, and Beyond
• The Future of the Internet: Trends and Predictions

Mobile Technologies

• Mobile Operating Systems: A Comparison of iOS and Android
• Mobile App Monetization Strategies: Ads, Subscriptions, and Freemium Models
• Cross-platform Mobile Development: Pros and Cons
• Mobile Payment Technologies: From NFC to Cryptocurrencies
• Mobile Health (mHealth) Applications: Improving Healthcare Access
• Location-based Services in Mobile Apps: Opportunities and Challenges
• Mobile Gaming Trends: Augmented Reality and Multiplayer Experiences
• The Impact of 5G on Mobile Applications
• Mobile App Testing: Ensuring Quality User Experiences
• Mobile Security: Protecting Devices and User Data

Human-Computer Interaction (HCI)

• User Experience (UX) Design Principles: Creating Intuitive Interfaces
• Usability Testing Methods: Evaluating the User-Friendliness of Products
• Interaction Design Patterns: Enhancing User Engagement
• Accessibility in Design: Designing for All Users
• Virtual Reality (VR) and User Experience: Design Considerations
• Gamification in User Interface Design: Enhancing Engagement
• Voice User Interface (VUI) Design: Building Natural Interactions
• Biometric User Authentication: Balancing Security and Convenience
• The Evolution of Graphical User Interfaces (GUIs)
• Wearable Technology Design: Integrating Fashion and Functionality

Cloud Computing

• Cloud Service Models: IaaS, PaaS, and SaaS Explained
• Cloud Deployment Models: Public, Private, and Hybrid Clouds
• Cloud Security Best Practices: Protecting Data in the Cloud
• Serverless Computing: Streamlining Application Development
• Cloud Computing in Business: Cost Savings and Scalability
• Cloud-Native Technologies: Containers and Orchestration
• Microservices Architecture in the Cloud: Breaking Down Monoliths
• Cloud Computing Trends: Edge Computing and Multi-cloud Strategies
• Cloud Migration Strategies: Moving Applications to the Cloud
• Cloud Computing in Healthcare: Enhancing Patient Care

Robotics and Automation

• Robotics in Manufacturing: Increasing Efficiency and Precision
• Autonomous Robots: Applications and Challenges
• Humanoid Robots: Advancements in AI-driven Robotics
• Robotic Process Automation (RPA): Streamlining Business Processes
• Drones in Industry: Surveillance, Delivery, and Beyond
• Surgical Robotics: Innovations in Medical Procedures
• Robotic Exoskeletons: Assisting Human Mobility
• Social Robots: Interacting with Humans in Various Settings
• Ethical Considerations in Robotics and AI
• The Future of Robotics: Trends and Predictions

Ethical Considerations in Technology

• Responsible AI: Ethical Considerations in Artificial Intelligence
• Data Privacy Laws: Navigating Compliance and Regulations
• Bias in Algorithms: Addressing and Mitigating Unintended Consequences
• Ethical Hacking: Balancing Security Testing and Privacy Concerns
• Technology and Mental Health: Addressing Digital Well-being
• Environmental Impact of Technology: Green Computing Practices
• Open Source Software: Community Collaboration and Ethical Licensing
• Technology Addiction: Understanding and Combating Dependencies
• Social Media Ethics: Privacy, Fake News, and Cyberbullying
• Ethical Considerations in Biometric Technologies

Future Trends in Technology

• The Future of Computing: Quantum Computing and Beyond
• Edge AI: Bringing Intelligence to the Edge of Networks
• Biocomputing: Merging Biology and Computing
• Neurotechnology: Brain-Computer Interfaces and Cognitive Enhancement
• Sustainable Technologies: Innovations in Green Computing
• 7G and Beyond: Envisioning the Next Generation of Connectivity
• Space Technology and Computing: Exploring the Final Frontier
• Biohacking and DIY Tech: A Look into Citizen Science
• Tech for Social Good: Using Technology to Address Global Challenges
• The Convergence of Technologies: AI, IoT, Blockchain, and More

Miscellaneous Topics

• Technology and Education: Transforming Learning Experiences
• Digital Transformation: Strategies for Modernizing Businesses
• Tech Startups: Navigating Challenges and Achieving Success
• Women in Technology: Empowering Diversity and Inclusion
• The History of Computing: Milestones and Innovations
• Futuristic Interfaces: Brain-Computer Interfaces and Holography
• Tech and Art: Exploring the Intersection of Creativity and Technology
• Hackathons: Fostering Innovation in Tech Communities
• The Role of Technology in Disaster Management
• Exploring Careers in Technology: Opportunities and Challenges

Tips for Effective Computer Presentations

• Mastering the Art of Public Speaking in the Tech Industry
• Designing Engaging Visuals for Technical Presentations
• The Dos and Don’ts of Live Demonstrations in Tech Presentations
• Building a Compelling Narrative: Storytelling Techniques in Tech Talks
• Handling Q&A Sessions: Tips for Addressing Audience Questions
• Time Management in Tech Presentations: Balancing Content and Interaction
• Incorporating Humor in Technical Presentations: Dos and Don’ts
• Creating Interactive Workshops: Engaging Audiences in Hands-on Learning
• Leveraging Social Media for Tech Presentations: Tips for Promotion
• Continuous Learning in the Tech Industry: Strategies for Staying Informed

Case Studies and Real-World Applications

Real-world examples and case studies add practical relevance to computer presentations. Showcase successful projects, discuss challenges faced, and share lessons learned. 

Analyzing the impact of technology in real-world scenarios provides valuable insights for the audience and encourages a deeper understanding of the subject matter.

Future Trends in Computer Presentation Topics

Predicting future trends in technology is both exciting and challenging. Presenters can offer insights into upcoming technological developments, anticipate challenges and opportunities, and encourage continuous learning in the rapidly evolving tech landscape.

Discussing the potential impact of technologies like 6G, augmented reality, or advancements in quantum computing sparks curiosity and keeps the audience abreast of the latest innovations.

In conclusion, computer presentations serve as powerful tools for knowledge sharing and skill development in the tech industry. Whether you’re presenting to novices or seasoned professionals, the choice of topics, presentation skills, and a thoughtful approach to ethical considerations can elevate the impact of your presentation. 

As technology continues to evolve, staying informed and exploring diverse computer presentation topics will be instrumental in fostering a culture of continuous learning and innovation. 

Embrace the dynamic nature of technology and embark on a journey of exploration and enlightenment through engaging computer presentations.

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Computer Vision

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Department of EECS Announces 2024 Promotions

The Department of Electrical Engineering and Computer Science (EECS) is proud to announce multiple promotions.

Image recognition accuracy: An unseen challenge confounding today’s AI

“Minimum viewing time” benchmark gauges image recognition complexity for AI systems by measuring the time needed for accurate human identification.

EECS Alliance Roundup: 2023

Founded in 2019, The EECS Alliance program connects industry leading companies with EECS students for internships, post graduate employment, networking, and collaborations. In 2023, it has grown to include over 30 organizations that have either joined the Alliance or participate in its flagship program, 6A.

Three MIT students selected as inaugural MIT-Pillar AI Collective Fellows

The graduate students will aim to commercialize innovations in AI, machine learning, and data science.

A computer scientist pushes the boundaries of geometry

Justin Solomon applies modern geometric techniques to solve problems in computer vision, machine learning, statistics, and beyond.

2023-24 EECS Faculty Award Roundup

This ongoing listing of awards and recognitions won by our faculty is added to all year, beginning in September.

Sanjoy Mitter, interdisciplinary explorer, dies at 89.

The co-founder and director of CICS, which later became LIDS, blended intellectual rigor with curiosity.

Recent chair announcements within EECS

The Department of Electrical Engineering and Computer Science (EECS) recently announced the following crop of chair appointments, all effective July 1, 2022. Karl Berggren has been named the …

New recipients of Meta (Facebook) Fellowship for 2022

Meta (Facebook) recently announced the winners of its highly competitive 2022 fellowships. The incoming group of Fellowship recipients includes four MIT graduate students, two of whom study within …

Nonsense can make sense to machine-learning models

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