Office Hours T/Th 3:40-5:20 Rm 1418 Computer Science Building
Required Textbook: Computer vision: a modern approach
Optional books: here and here
- Feb 1 lecture 0 (includes course outline and grading system)
- Feb 3 warm-up for perspective cameras
- Feb 8 math for perspective cameras and camera calibration part 1
- Feb 10 math for perspective cameras and camera calibration part 2. Assignment #1 out (use these points for part 4), due Feb 23 midnight. Also see collection of slides from Prof. Lazebnik on cameras here. These notes from Prof. Duraiswami
- Feb 15 warm-up for illumination and reflectance
- Feb 17 more illunation and reflectance. See chapters 4,5,6 in text and Prof. Lazebnik's slides
- Feb 22 matlab demos and examples from recent research on illumination and reflectance. Papers 1, 2, 3
- Feb 24 color and filtering. See slides from the text book here.
- March 2 Change blindness, background subtraction, filtering notes and here on filtering.
- March 4 More filtering and background subtraction.
- March 8 Test 1
- March 10 Matlab demonstration. Some examples.
- March 15 Homework discussion, Intro to: Optical Flow, Expectation Maximization (EM), Classification and Recognition.
- March 17 More detail on Optical Flow, Expectation Maximization (EM), Classification and Recognition (read this paper and notes 10 here ).
- March 22 Fitting lines.
- March 24 Fitting curves and E.M.
- March 29 See these notes on E.M.
- March 31 Features for recognition. Homework Assignment 2.
- April 5 Tracking guest lecture see notes here to prepare...
- April 7 Feature and classifiers for recognition
- April 12 Homework due, presentations on proposed projects
- April 14 Finish classfiers and recognition
- ...
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