How to begin your career in Computer Vision and Machine Learning Field?

This post is for Computer vision enthusiasts. We are highlighting the focal point of resources and computer vision tutorials for all CV aficionados and get themselves started in this emerging field. As a passionate beginner in the field of computer vision, we hope you find this useful.

Some prior knowledge about linear algebra, calculus, probability and statistics would definitely be a plus but its not always required. The most important thing is to get started and you can learn other essential things on the fly.

Courses:

• Computer Vision – Mubarak Shah (UCF) : All the materials related to the course are available online and what is more interesting is that even the video lectures are available.
• Computer Vision – Subhransu Maji (UMass Amherst) : Provides access to all the lecture materials and assignments but there are no video lectures.
• Visual Recognition – Kristen Grauman (UT Austin) : This Provides links to some of the interesting and fundamental papers in computer vision.
• Language and Vision – Tamara Berg (UNC Chapel Hill) : This course is basically aimed towards exploring topics straddling the boundary between Natural Language Processing and Computer Vision.
• Convolutional Neural Networks for Visual Recognition – Fei-Fei Li and Andrej Karpathy (Stanford University) : This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for computer vision tasks with a main focus on image classification.

Some additional resources:

• Computer Vision – Rob Fergus (NYU)
• Computer Vision – Derek Hoiem (UIUC)
• Computer Vision: Foundations and Applications – Kalanit Grill-Spector and Fei-Fei Li (Stanford University)
• Advances in Computer Vision – Antonio Torralba and Bill Freeman (MIT)

Books:

Computer Vision

• Computer Vision: A Modern Approach (2nd edition) – David Forsyth and Jean Ponce 2011
• Computer Vision: Models, Learning, and Inference – Simon J. D. Prince 2012
• Computer Vision: Theory and Application – Rick Szeliski 20

In addition to this, it is very useful to be aware of most of the basic image processing techniques presented in this book Digital Image Processing – Gonzalez 2007

OpenCV Programming

• Practical Python and OpenCV – Adrian Rosebrock
• OpenCV Essentials – Oscar Deniz Suarez, Mª del Milagro Fernandez Carrobles, Noelia Vallez Enano, Gloria Bueno Garcia, Ismael Serrano Gracia
• Learning OpenCV Computer Vision with the OpenCV Library – Gary Bradski and Adrian Kaehler

Software Packages:

You can find an exhaustive list of links presenting code which implements some of the standard vision algorithms at http://www.cvpapers.com/rr.html

Major Conferences: 

Below are some of the major conferences listed in their ranking order.

• CVPR – Computer Vision and Pattern Recognition
• ICCV – International Conference on Computer Vision
• ECCV – European Conference on Computer Vision
• WACV – Workshop on Applications of Computer Vision
• BMVC – British Machine Vision Conference

Most of the papers published in the above mentioned conferences can be accessed at http://www.cvpapers.com/index.html

Nice way to keep track of the conferences deadline is via http://conferences.visionbib.com/Iris-Conferences.html

Now that you have acquired some knowledge of computer vision and Deep Learning (from the previous post), please feel free to compete in Kaggle competitions (best way to put your learning into practice).

If you would like to have any guidance/support in CV domain or have any additional resource information, we would love to hear it without judging you. You may drop your comments below.