CS 523 ( CS 423/EE 533) Computer Vision

1. CS 523 (CS 423/EE 533)ComputerVision Lecture 1 INTRODUCTION TO COMPUTER VISION

2. About the Course

3. Syllabus http://vvgl.ozyegin.edu.tr Objective Introduction to the theory, tools, and algorithms of 3D computer vision Instructor Assist. Prof. M. Furkan Kıraç E-mail: furkan.kirac@ozyegin.edu.tr Room: 219 Hours Wednesdays, 10:40-13:30, Room: 241 Grading Projects: 6x10% Final Exam: 40%

4. Grading • Short Projects:Late submissions are not accepted. Copying answers from others’ work is not permitted. • Final Exam:At least 3 of the 6 Short Projects must be turned in by the due date in order to qualify for the Final Exam. No make-up will be given for the Final Exam. Students can take the Bütünleme exam if they miss the Final Exam.

5. Recommended Books • Computer Vision: Algorithms and Applications, Richard Szeliski, Springer, 2010. • Computer Vision: A Modern Approach, David A. Forsyth and Jean Ponce, Prentice-Hall, 2002. • Introductory Techniques for 3D Computer Vision, Emanuele Trucco and Alessandro Verri, Prentice-Hall 1998.

6. OpenCV Resources • Learning OpenCV, Gary Bradski and Adrian Kaehler, O'Reilly, 2008. • OpenCV 2 Computer Vision Application Programming Cookbook, Robert Laganière, Packt Publishing, 2011. • Mastering OpenCV with Practical Computer Vision Projects, Daniel Lélis Baggio, et al., Packt Publishing, 2012.

8. Applications of Computer Vision

9. Image Stitching

10. Image Matching

11. Object Recognition

12. 3D Reconstruction

13. Interior Modeling

14. 3D Augmented Reality

15. 3D Camera Tracking

16. Stereo Conversion for 3DTV

17. Depth Estimation and View Interpolation for 3DTV

18. Human Tracking

19. License Plate Recognition

20. Human Pose Estimation

21. Course Outline

22. Topics to be covered • 3D geometry fundamentals • Transformations and projections • Camera calibration • Feature detection and matching • Image stitching • Single view geometry • Two view geometry • Multiple view geometry • Stereo vision and depth estimation • 3D structure from motion • 3D camera tracking

23. Relation to Other Fields

24. Computer Vision Figure from "Computer Vision: Algorithms and Applications,” Richard Szeliski, Springer, 2010.

25. Computer Graphics • Lights and materials • Shading • Texture mapping • Environment effects • Animation • 3D scene modeling • 3D character modeling • (OpenGL)

26. Computer Graphics

27. Image Processing Topics • Resampling • Enhancement • Noise filtering • Restoration • Reconstruction • Segmentation • Image compression • (MATLAB and OpenCV)

28. Image Processing

29. Video Processing Topics • Spatio-temporal sampling • Motion estimation • Frame-rate conversion • Multi-frame noise filtering • Multi-frame restoration • Super-resolution • Video compression • (MATLAB & OpenCV)

30. Video acquisition-display chain Capture Representation Coding Transmission Decoding Rendering

31. Human vs. Computer

32. Optical illusions

33. Actual vs. Perceived Intensity (Mach band effect)

34. Brightness Adaptation of the Eye

35. Optical illusions

37. Optical illusions

38. Why is Computer Vision Difficult?

39. Human perception

40. Human perception

41. Human Visual System

42. Human Eye

45. Photoreceptors: Rods & Cones

47. Rods vs. Cones • Rods • Perceive brightness only • Night vision • Cones • Perceive color • Day vision • Red, green, and blue cones

48. Cone Distribution Blue is less-focused 64% 32% 2%

49. Visual Threshold drop during Dark Adaptation

50. Spatial Resolution of the Human Eye • Photopic (bright-light) vision: • Approximately7 millioncones • Concentratedaround fovea • Scotopic (dim-light) vision • Approximately75-150 millionrods • Distributed over retina (HDTV: 1920x1080 = 2 millionpixels)