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Computer Vision (CSE 455)

Computer Vision (CSE 455). Staff Prof: Steve Seitz ( seitz@cs ) TAs: Jeff Bigham ( jbigham@cs ) Dave Jones ( djones3@cs ) Jenny Yuen ( jenny@cs ) Web Page http://www.cs.washington.edu/education/courses/cse455/06wi/ Handouts signup sheet intro slides image filtering slides.

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Computer Vision (CSE 455)

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  1. Computer Vision (CSE 455) • Staff • Prof: Steve Seitz (seitz@cs) • TAs: Jeff Bigham (jbigham@cs) Dave Jones (djones3@cs) Jenny Yuen (jenny@cs) • Web Page • http://www.cs.washington.edu/education/courses/cse455/06wi/ • Handouts • signup sheet • intro slides • image filtering slides

  2. Today • Intros • Computer vision overview • Course overview • Image processing • Readings for this week • Forsyth & Ponce, chapter 7 (in reader, available at Comm building copy center) • Mortensen, Intelligent Scissors (online)

  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 Image Inpainting, M. Bertalmío et al. http://www.iua.upf.es/~mbertalmio//restoration.html

  12. Image Enhancement Image Inpainting, M. Bertalmío et al. http://www.iua.upf.es/~mbertalmio//restoration.html

  13. Image Enhancement Image Inpainting, M. Bertalmío et al. http://www.iua.upf.es/~mbertalmio//restoration.html

  14. Application: Document Analysis Digit recognition, AT&T labs http://www.research.att.com/~yann/

  15. Applications: 3D Scanning • Scanning Michelangelo’s “The David” • The Digital Michelangelo Project • - http://graphics.stanford.edu/projects/mich/ • UW Prof. Brian Curless, collaborator • 2 BILLION polygons, accuracy to .29mm

  16. The Digital Michelangelo Project, Levoy et al.

  17. ESC Entertainment, XYZRGB, NRC

  18. Applications: Motion Capture, Games

  19. Andy Serkis, Gollum, Lord of the Rings

  20. Application: Medical Imaging

  21. Applications: Robotics

  22. 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

  23. 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)

  24. Project 1: Intelligent Scissors

  25. Project 2: Panorama Stitching • http://www.cs.washington.edu/education/courses/455/03wi/projects/project2/artifacts/crosetti/index.shtml

  26. Project 3: 3D Shape Reconstruction

  27. Project 4: Face Recognition

  28. Class Webpage • http://www.cs.washington.edu/education/courses/cse455/06wi/

  29. Grading • Programming Projects (70%) • image scissors • panoramas • 3D shape modeling • face recognition • Midterm (15%) • Final (15%)

  30. 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.

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