Resources for Self-guided Study in Computer VisionPosted: September 15, 2011
Computer science has it easy. The web is littered with useful information from online forums to books to open courseware. It’s hard not to get overwhelmed by the wealth of knowledge. While there are numerous other postings around surveying all of the places to get open courseware or PDFs of important papers [Books], [Sites/courseware], [Papers], I thought I would share some of the resources I have previous used or am currently going through in the realm of computer vision and robotics.
Especially as an undergrad, it’s difficult to delve into an area if you don’t have the appropriate classes at your school. So take some of the following to expand your reach. It has been beneficial for me to start by looking through class lectures/presentations and then jump deeper into the math.
There are a number of fairly different computer vision courses around. Some of them focus more on image processing methods while others are more machine learning oriented. The following courses give what I feel is a good/broad overview of the field. I think a ‘good’ course includes topics on camera models, low-level techniques like edge detection, texture, high-level object recognition, appropriate datastructures (ie quadtrees), model fitting (ie RANSAC), and discussion of other spaces (ie fourier space).
In many (most?) instances, especially in high-level vision, the mathematical models come down to applied machine learning. It’s important to at least have a grasp on the many different types of methods available. While you might not need to know the details of hidden markov models for your interests in low-level segmentation, I think it’s important to have a broad background including many related topics.
CV Class: Intro to Computer Vision (Stanford; Prof Fei-Fei Li) Fairly standard CV course.
CV Class: Computer Vision (UIUC; Prof Forsyth) Fairly standard CV course.
ML Class: Practical Machine Learning (Berkely; Prof Michael Jordan) This is where I first started to get interested in machine learning.
ML Notes: Statistical Data Mining Tutorials (Andrew Moore) Great resource for getting starting with a variety of different ML techniques such as SVMs, Mixture Models, Hidden Markov Models, and many others.
ML Class: Pattern Recognition (SUNY Buffalo; Prof Jason Corso) Lecture note links in the calendar. These slides are full of text and are a good reference for both the math and intuition.
CV Book: Computer Vision: Models, Learning, and Inference - This is a great (free!) preprint that leans heavily towards machine learning. Each section provides background on a set of models or machine learning tools involved, and methods of inference. The beginning is an in-depth overview of the necessary probability and machine learning concepts. I just started going through this book but it has been really useful for getting an overview of things like parts models and shape models.
CV Book: Computer Vision: Algorithms and Applications - This is more traditionally laid out textbook that is referenced in a number of current Intro to CV classes such as Fei-Fei Li’s above and the current CV course at my school (JHU).
CV Class: Learning-based Methods in Vision (CMU; Prof Alexei Efros) I learned a lot about texture (texton) recognition and some state of the art methods using fancy ML techniques.
CV Class: Grounding Object Recognition and Scene Understanding (CMU; Prof Antonio Torralba) This is an ongoing class focusing on higher level vision. The first lecture looks promising, but I’m not exactly sure what the rest of the class will be like.
CV Papers is a collection of recent computer vision papers from the top/largest vision conferences. It includes CVPR, ECCV, ICCV, Siggraph, and others. It includes all of the paper titles and appropriate PDFs and project files for a large number of them.
Video Lectures This site hosts thousands of academic videos including many in computer vision. For some conferences, like CVPR2010, they host a lot of videos for the talks. They also have a lot of ML videos for summer schools.
Tech Talks For some conferences, like ICML2011, they host video for most (all?) of the talks from the event. Others, like CVPR2011, only have selected videos. This is a great way to learn about a lot of recent work without solely relying on reading papers.