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Computer Vision

Computer Vision. Spring 2012 15-385,-685 Instructor: S. Narasimhan Wean Hall 5409 T-R 10:30am – 11:50am. A Picture is Worth 100 Words. A Picture is Worth 10,000 Words. A Picture is Worth a Million Words. A Picture is Worth a ...?. Necker’s Cube Reversal. A Picture is Worth a ...?.

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Computer Vision

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  1. Computer Vision Spring 2012 15-385,-685 Instructor: S. Narasimhan Wean Hall 5409 T-R 10:30am – 11:50am

  2. A Picture is Worth 100 Words

  3. A Picture is Worth 10,000 Words

  4. A Picture is Worth a Million Words

  5. A Picture is Worth a ...? Necker’s Cube Reversal

  6. A Picture is Worth a ...? Checker Shadow Illusion – [E. H. Adelson]

  7. A Picture is Worth a ...? Checker Shadow Illusion – [E. H. Adelson]

  8. Human Vision • Can do amazing things like: • Recognize people and objects • Navigate through obstacles • Understand mood in the scene • Imagine stories • But still is not perfect: • Suffers from Illusions • Ignores many details • Ambiguous description of the world • Doesn’t care about accuracy of world

  9. Computer Vision What we see What a computer sees

  10. Computer Vision What we see What a computer sees

  11. What is Computer Vision? • Inverse Optics • Intelligent interpretation of Imagery • Building a Visual Cortex • No matter what your definition is… • Vision is hard. • But is fun...

  12. Scene Interpretation Components of a Computer Vision System Camera Lighting Computer Scene

  13. Topics covered

  14. Image Processing Image enhancement Feature detection Fourier Transform Sampling, Convolution

  15. Surface Reflectance [CURET]

  16. Lightness and Perception Checker Shadow Illusion – [E. H. Adelson]

  17. Understanding Optical Illusions Which is bigger? Straight Lines? Dots White? Or Black? Spinning Wheels?

  18. 3D from Shading Photometric Stereo Shape from Shading

  19. Cameras and their Optics Today’s Digital Cameras The Camera Obscura

  20. Biological Cameras Mosquito Eye Human Eye

  21. Optical Flow

  22. Tracking

  23. Binocular Stereo

  24. Range Scanning and Structured Light

  25. Range Scanning and Structured Light

  26. Microsoft Kinect IR LED Emitter IR Camera RGB Camera

  27. Statistical Techniques Least Squares Fitting

  28. Face detection

  29. Face Recognition • Principle Components Analysis (PCA) • Face Recognition

  30. Some Recent Trends in Vision Novel Cameras and Displays *** Topics change every year

  31. Advanced Related Courses at CMU • Graduate Level Computer Vision (Hebert, Fall) • Computational Photography (Efros, Fall) • Physics-based methods in Comp Vision (Narasimhan) • Learning-based methods in Comp. Vision (Efros) • Geometry-based methods in Comp. Vision (Hebert)

  32. Course Logistics

  33. Text and Reading • Class Notes (required) • Text, Robot Vision, B.K.P.Horn, MIT Press • (recommended) • Supplementary Material (papers, tutorials)

  34. Course Schedule 1/17/2012: Introduction and Course Fundamentals 1/19/2012: Matlab Review PART 1 : Signal and Image Processing 1/24/2012 1D Signal Processing 1/26/2012: 2D Image Processing [Project 1 OUT] 1/31/2012: Image Pyramids and Sampling 2/2/2012: Edge Detection 2/7/2012: Hough Transform PART 2: Physics of the World 2/9/2012: Surface appearance and BRDF 2/14/2012: Photometric Stereo [Project 1 DUE, Project 2 OUT] 2/16/2012: Shape from Shading 2/21/2012: Direct and Global Illumination PART 4 : 3D Geometry 2/23/2012: Image Formation and Projection 2/28/2012: Motion and Optical Flow 3/1/2012: Lucas Kanade Tracking [Project 2 DUE Project 3 OUT] 3/6/2012: Midterm Review 3/8/2012: Midterm Exam 3/20/2012:Binocular Stereo 1 3/22/2012: Binocular Stereo 2 [Project 3 DUE, Project 4 OUT] 3/27/2012: Structured Light and Range Imaging

  35. Course Schedule PART 4 : Statistical Techniques 3/29/2012: Feature Detection 1 4/03/2012: Classification 1 4/05/2012: Classification 2 4/10/2012: Principle Components Analysis [Project 4 DUE] 4/12/2012: Applications of PCA [Project 5 OUT] [Grad project description due] PART 6: Trends and Challenges in Vision Research 4/17/2012: Image Based Rendering 4/24/2012: Novel Cameras and Displays 4/26/2012: Optical Illusions 5/1/2012: Open challenges in vision research [Project 5 DUE] 5/3/2012: Project presentations by undergraduate students 5/8/2012: Project presentations by graduate students [Grad Project 6 DUE] 5/13/2012: Final Grades Due *** Use as a guide…changes possible

  36. Prerequisites • Basic Linear Algebra, Probability, Calculus Required • Basic Data structures/Programming knowledge • No Prior knowledge of Computer Vision Required

  37. Grading • FIVE Projects – 90 % (15%, 15%, 20%, 20%, 20%) • ONE Midterm – 10 % • ONE Extra Project for Graduate Students – 20 % • Most projects include analytic and programming parts. • All projects must be done individually. • Programming Environment – Matlab. • Projects due before midnight on due-date. • Written parts due in class on the due-date. • 3 Late Days for the semester. No more extensions. • Class attendance – 5 % extra credit

  38. Narasimhan: Smith Hall 223, By Appointment • Email: srinivas@cs.cmu.edu • Supreeth Achar: Wednesdays 6:00pm – 8:00pm • Email: supreeth@cmu.edu • Gunhee Kim: Thursdays, Thursdays 6:00pm – 8:00pm • Email: gunhee@cs.cmu.edu • Technical Questions: Post on bboard. TAs will answer. Office Hours

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