"Behind every stack of books there is a flood of knowledge."
Course Catalog Entry
How can computers understand the visual world of humans? This course treats vision as a process of inference from noisy and uncertain data and emphasizes probabilistic, statistical, data-driven approaches. Topics include image processing; segmentation, grouping, and boundary detection; recognition and detection; motion estimation and structure from motion. This offering of CS 143 will emphasize the core vision task of recognition in particular. We will train and evaluate classifiers to recognize various visual phenomena.
The course will consist of five programming projects, two written quizzes, and a self-chosen final project. Students can earn graduate credit for the course but will need to meet higher requirements on all projects throughout the semester and need the instructor’s permission. This course can satisfy the graduate A.I area requirement.
This course requires programming experience as well as linear algebra, basic calculus, and basic probability. Previous knowledge of visual computing will be helpful. The following courses (or equivalent courses at other institutions) are helpful prerequisites:
Some of the course topics overlap with these related courses, but none of the assignments will.
|Hybrid images with Laplacian pyramids||Andy Loomis, Emanuel Zgraggen, Dylan Field||Project 1 results|
|pB Lite: boundary detection||Paul Sastrasinh, Li Sun, Hang Su||Project 2 results|
|Scene recognition with bag of words||Paul Sastrasinh, Chen Xu, Yun Zhang||Project 3 results|
|Face detection with a sliding window||Emanuel Zgraggen, Hang Su, Paul Sastrasinh||Project 4 results|
|Tracking and Structure from Motion or …||Hang Su||Project 5 results|
|Your choice for final project||Seth Goldenberg, Emanuel Zgraggen||Final project results|
It is strongly recommended that all projects be completed in Matlab. All starter code will be provided for Matlab. Students may implement projects through other means but it will generally be more difficult.
Your final grade will be made up from
You have three “late days” for the whole course. That is to say, the first 24 hours after the due date and time counts as 1 day, up to 48 hours is two and 72 for the third late day. This will not be reflected in the initial grade reports for your assignment, but they will be factored in and distributed at the end of the semester so that you get the most points possible.
Graduate credit is available and each project will specifiy the minimum requirements to earn such credit.
You can contact the professor or TA staff with any of the following:
James’ office hours will be held in his office (CIT 445). TA office hours will be held in the Brindy Bowl (CIT 271).
|W, Sept 7th||Introduction to computer vision||.ppt, .pdf||Szeliski 1|
|Image Formation and Filtering|
|F, Sep 9th||Cameras and optics||.ppt, .pdf||Szeliski 2.1, especially 2.1.5||Project 1 out|
|M, Sep 12th||Light and color||.ppt, .pdf||Szeliski 2.2 and 2.3|
|W, Sep 14th||Pixels and image filters||.ppt, .pdf||Szeliski 3.2|
|F, Sep 16th||Thinking in frequency||.ppt, .pdf||Szeliski 3.4|
|M, Sep 19th||Image pyramids and applications||.ppt, .pdf||Szeliski 3.5.2 and 8.1.1|
|Machine Learning Crash Course|
|W, Sep 21st||Machine learning: overview||.ppt, .pdf|
|F, Sep 23rd||Machine learning: clustering||.ppt, .pdf||Szeliski 5.3|
|M, Sep 26th||Machine learning: classification||.ppt, .pdf||Project 1 due|
|Grouping and Fitting|
|W, Sep 28th||Edge detection and line fitting w/ Hough transform||.ppt, .pdf||Szeliski 4.2||Project 2 out|
|F, Sep 30th||Robust fitting (Hough Transform)||.ppt, .pdf||Szeliski 4.3|
|M, Oct 3rd||Robust fitting (RANSAC and others)||.ppt, .pdf||Szeliski 4.3|
|W, Oct 5th||Mixture of Gaussians and EM||.ppt, .pdf|
|F, Oct 7th||Gestalt cues, MRFs, and graph cuts||.ppt, .pdf||Szeliski 5.5|
|M, Oct 10th||No classes||Project 2 due|
|W, Oct 12th||Recoginition Overview and History||.ppt, .pdf||Szeliski 14||Project 3 out|
|F, Oct 14th||Image features and bag of words models||.ppt, .pdf||Szeliski 4.1.2, 14.4.1, and 14.3.2|
|M, Oct 17th||Interest points: corners||.ppt, .pdf||Szeliski 4.1.1|
|W, Oct 19th||Quiz 1|
|F, Oct 21st||Interest points and instance recognition||.ppt, .pdf||Szeliski 14.3|
|M, Oct 24th||Large-scale instance recognition||.ppt, .pdf||Szeliski 14.3.2||Project 3 due|
|W, Oct 26th||Detection with sliding windows||.ppt, .pdf||Szeliski 14.1|
|F, Oct 28th||Guest talk: Jim Rehg, Behavior Imaging and the Study of Autism|
|M, Oct 31st||Detection with sliding windows continued||.ppt, .pdf||Szeliski 14.2||Project 4 out|
|W, Nov 2nd||Context and Spatial Layout||.ppt, .pdf||Szeliski 14.5|
|F, Nov 4th||Guest talk: Gabriel Taubin, 3d photography|
|Multiple Views and Motion|
|M, Nov 7th||Feature Tracking||.ppt, .pdf||Szeliski 4.1.4|
|W, Nov 9th||Optical Flow||see above||Szeliski 8.4|
|F, Nov 11th||Guest lecture: Deqing Sun, Optical flow||Project 4 due|
|M, Nov 14th||Epipolar Geometry||.ppt, .pdf||Szeliski 11|
|W, Nov 16th||Stereo Correspondence||.ppt, .pdf||Project 5 out|
|F, Nov 18th||Structure from Motion||.ppt, .pdf||Szeliski 7||Final Project out|
|M, Nov 21st||Activity Recognition||.ppt, .pdf|
|W, Nov 23rd||No classes|
|F, Nov 25th||No classes|
|M, Nov 28th||Internet Scale Vision||.ppt, .pdf|
|W, Nov 30th||Guest lecture: Pedro Felzenszwalb, Object Detection|
|F, Dec 2nd||Crowdsourcing||.ppt, .pdf|
|M, Dec 5th||Attributes and Course Summary||.ppt, .pdf|
|W, Dec 7th||Quiz 2|
|F, Dec 9th||No classes, reading period|
|M, Dec 12th||No classes, reading period||Final Project / Project 5 due|
|T, Dec 13th, 9:00 AM||Exam Period – final presentations|
The materials from this class rely significantly on slides prepared by other instructors, especially Derek Hoiem and Svetlana Lazebnik. Each slide set and assigment contains acknowledgements. Feel free to use these slides for academic or research purposes, but please maintain all acknowledgements.
Michael Black’s most recent offering of CS 143 can be found here
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