1	Computer Vision (CSE P576) Staff Prof: Steve Seitz (seitz@cs ) TA: Jiun-Hung Chen (jhchen@cs) Web Page http://www.cs.washington.edu/education/courses/csep576/05wi/ Handouts signup sheet intro slides image filtering slides image sampling slides
2	Today Intros Computer vision overview Course overview Image processing Readings for this week Forsyth & Ponce textbook, chapter 7
3	Every picture tells a story Goal of computer vision is to write computer programs that can interpret images
4	Can computers match human perception? Yes and no (but mostly no!) humans are much better at “hard” things computers can be better at “easy” things
5	Perception
6	Perception
7	Perception
8	Low level processing Low level operations Image enhancement, feature detection, region segmentation
9	Mid level processing Mid level operations 3D shape reconstruction, motion estimation
10	High level processing High level operations Recognition of people, places, events
11	Image Enhancement
12	Image Enhancement
13	Image Enhancement
14	Application: Document Analysis
15	Applications: 3D Scanning
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23	Applications: Motion Capture, Games
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25	Application: Medical Imaging
26	Applications: Robotics
27	Syllabus Image Processing (2 weeks) filtering, convolution image pyramids edge detection feature detection (corners, lines) hough transform Image Transformation (2 weeks) image warping (parametric transformations, texture mapping) image compositing (alpha blending, color mosaics) segmentation and matting (snakes, scissors) Motion Estimation (1 week) optical flow image alignment image mosaics feature tracking
28	Syllabus Light (1 week) physics of light color reflection shading shape from shading photometric stereo 3D Modeling (3 weeks) projective geometry camera modeling single view metrology camera calibration stereo Object Recognition and Applications (1 week) eigenfaces applications (graphics, robotics)
29	Project 1: Intelligent Scissors
30	Project 2: Panorama Stitching http://www.cs.washington.edu/education/courses/455/03wi/projects/project2/artifacts/crosetti/index.shtml
31	Project 3: 3D Shape Reconstruction
32	Project 4: Face Recognition
33	Class Webpage http://www.cs.washington.edu/education/courses/csep576/05wi/
34	Grading Programming Projects (100%) image scissors panoramas 3D shape modeling face recognition
35	General Comments Prerequisites—these are essential! Data structures A good working knowledge of C and C++ programming (or willingness/time to pick it up quickly!) Linear algebra Vector calculus Course does not assume prior imaging experience computer vision, image processing, graphics, etc.