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Introduction to Computer Vision Course Overview

Explore theory, tools, and algorithms of computer vision. Learn about image processing, video analysis, and applications like object recognition. Topics include filters, feature detection, 3D reconstruction, and more.

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Introduction to Computer Vision Course Overview

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  1. CS 423 (CS 423/CS 523)Computer Vision 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 computer vision Instructor Assist. Prof. M. Furkan Kıraç E-mail: furkan.kirac@ozyegin.edu.tr Room: 219 Hours Mondays, 9:40-12:30, Room: 246 Grading Projects: 4x15% Midterm Exam: 40%

  4. Grading • Projects:Late submissions are not accepted. Copying answers from others’ work is not permitted. • Midterm Exam:At least 3 of the 4 Projects must be turned in by the due date in order to qualify for the Final Exam. No Composite Exam (Bütünleme Sınavı), as there is no 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 • OpenCV Computer Vision Application Programming Cookbook Second Editon, Robert Laganière, Packt Publishing, 2014. • Learning OpenCV, Gary Bradski and Adrian Kaehler, O'Reilly, 2008. • Mastering OpenCV with Practical Computer Vision Projects, Daniel Lélis Baggio, et al., Packt Publishing, 2012.

  7. Applications of Computer Vision

  8. Image Stitching

  9. Image Matching

  10. Object Recognition

  11. 3D Reconstruction

  12. Interior Modeling

  13. 3D Augmented Reality

  14. 3D Camera Tracking

  15. Stereo Conversion for 3DTV

  16. Depth Estimation and View Interpolation for 3DTV

  17. Human Tracking

  18. License Plate Recognition

  19. Human Pose Estimation

  20. Course Outline

  21. Topics to be covered... • Linear Filters, Frequency Domain • Filtering, Edge and Boundary Detection • Feature Detection • Fitting, Alignment • Histograms • Covariance, Principle Component Analysis (PCA) • Face Detection and PCA • Optical Flow and Motion • Tracking and Mean-Shift • Randomized Decision Trees, Pose Estimation • Bag of Features • Context, Two-View Geometry Summary

  22. Relation to Other Fields

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

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

  25. Computer Graphics

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

  27. Image Processing

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

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

  30. Human vs. Computer

  31. Optical illusions

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

  33. Brightness Adaptation of the Eye

  34. Optical illusions

  35. Optical illusions

  36. Why is Computer Vision Difficult?

  37. Human perception

  38. Human perception

  39. Human Visual System

  40. Human Eye

  41. Photoreceptors: Rods & Cones

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

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

  44. Visual Threshold drop during Dark Adaptation

  45. Spatial Resolution of the Human Eye • Photopic (bright-light) vision: • Approximately 7 million cones • Concentrated around fovea • Scotopic (dim-light) vision • Approximately 75-150 million rods • Distributed over retina (HDTV: 1920x1080 = 2 million pixels)

  46. Frequency Responses of Cones • Same amount of energy produces different sensations of brightness at different wavelengths • Green wavelength contributes most to the perceived brightness.

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