* A free license to install MATLAB for the duration of the course is available from MathWorks. Learners gain experience writing computer vision programs through online labs using MATLAB* and supporting toolboxes. Material includes online lectures, videos, demos, hands-on exercises, project work, readings and discussions. Learners should also be familiar with the following: basic linear algebra (matrix vector operations and notation), 3D co-ordinate systems and transformations, basic calculus (derivatives and integration) and basic probability (random variables). Learners should have basic programming skills and experience (understanding of for loops, if/else statements), specifically in MATLAB (Mathworks provides the basics here: ). It is also useful for those who desire a refresher course in mathematical concepts of computer vision. This course is ideal for anyone curious about or interested in exploring the concepts of computer vision. Learners will be able to apply mathematical techniques to complete computer vision tasks. Topics include color, light and image formation early, mid- and high-level vision and mathematics essential for computer vision. The course covers crucial elements that enable computer vision: digital signal processing, neuroscience and artificial intelligence. They are equipped to identify some key application areas of computer vision and understand the digital imaging process. You can check out this video of some of the research she covered in class.By the end of this course, learners will understand what computer vision is, as well as its mission of making computers see and interpret the world as humans do, by learning core concepts of the field and receiving an introduction to human vision capabilities. Unfortunately, the audio again didn't record. Ira stopped by class to tell students about some awesome research going on in her lab.Lecture 17: Guest Lecture from Ira Kemelmacher-Shlizerman If you are sad there isn't another great lecture here, please email UW President Ana Mari Cauce, and let her know! I can post the video if you want but it's very boring. Unfortunately, the audio did not get recorded. Due to the UW grad student strike, Ali gave this lecture.Lecture 13: Convolutional Neural Networks Lecture 11: More Machine Learning for Computer Vision Lecture 10: Machine Learning for Computer Vision Lecture 7: Matching, RANSAC, HOG, and SIFT CVAMA - 7 Linear Filters and Convolution. Lecture 4: Resizing, Filters, Convolutions Lecture 2: Human Vision, Color Spaces, Transforms This YouTube playlist has all of the lectures in sequential order. Unfortunately, some were recorded at the wrong aspect ratio and the audio was not recorded in the image segmentation lecture. Lectures were automatically recorded with the schools Pantopto system. If you don't have an idea you can train a classifier on birds and compete in the Kaggle competition posted on the Google Group. Projects can focus on developing new techniques or tools in computer vision or applying existing tools to a new domain. Each project should have a significant technical component, software implementation, or large-scale study. Pick any area of computer vision that interests you and pursue some independent work in that area. There was a final project worth 20% of the final grade. Homework 4: Neural Networks and Machine Learning.We cover basic image manipulations, filtering, features, stitching, optical flow, machine learning, and convolutional neural networks. The class has 6 homeworks where you will build out a computer vision library in C. Just make your own copy of the slides on Google Docs, don't ask to modify mine! Homeworks Lectures 8 and 9 on Flow, 3d, and stereo are given by Connor Schenck.Īll of the slides, videos, and homeworks are free to use, modify, redistribute as you like without permission. Special thanks to: Rob Fergus, Linda Shapiro, Harvey Rhody, Rick Szeliski, Ali Farhadi, Robert Collins. We might set aside some time at the end for questions. Slides are a mishmash of lots of other people's work. Because lectures include fellow students, you and they may be personally identifiable on the recordings. It was originally offered in the spring of 2018 at the University of Washington. (old-school vision), as well as newer, machine-learning based computer vision. It covers standard techniques in image processing like filtering, edge detection, stereo, flow, etc. This class is a general introduction to computer vision.
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