This document provides an overview of an advanced seminar on visual object recognition. The seminar will be held on Mondays from 1-3pm in room 106 of the Dan David classrooms at Tel Aviv University, lasting 13 weeks. Grading will be based on a class presentation or project (80%) and class participation (20%). The seminar will cover topics like street scene understanding, face detection, image categorization, and pose estimation. It will also discuss challenges in visual object recognition like viewpoint variation, illumination changes, occlusion, scale changes, and intra-class variation.

1. Advanced seminar: Visual Object Recognition Dr. Lior Wolf School of Computer Science , Tel Aviv University

2. Credit: www . scholastic . com

3. Administrivia Mondays 13:00-15:00, unless.. Dan David classrooms, room 106 A 13 weeks seminar, unless.. Grading: 80% class presentation or class project 20% active participation in class Office hours: Wednesdays 13:00-15:00

4. Example tasks: Street scene understanding Face detection Face identification Image categorization Pose estimation Motion and behavior analysis

5. Street scene understanding Watch Out! Probably Hanging Out Credit: Stan Bileschi, CBCL

6. Face detection Credit: Intel Technology Journal, Volume 09, Issue 01

7. Face identification Credit: www . security - lab . com

8. Image categorization Images courtesy of Mobileye

9. Pose estimation Credit: Thomas Izo, CSAIL

10. Motion recognition Ballet turn: Input video: Output: The “Birmingham Royal Ballet” Credit: Eli Shechtman and Michal Irani

11. Basic image processing: Basic image operators Template matching Image transformations

12. An image is an array of pixels Credit: H. Joel Trussell, Eli Saber, and Michael Vrhel

13. Pixels can be manipulated I I>100 Conv2(I,[-1 1]) For more on image convolution, please google: “ image convolution gamedev”

14. Template matching Credit: F. Patin, gamedev.net

15. Fourier Transform Credit: R. Fisher, S. Perkins, A. Walker and E. Wolfart.

16. Major scheme: Image representation: features Object learning: classification Credit: B. Heisele, Y. Ivanov, T. Poggio

17. Image features “ The most important element” (?) Example: David Lowe’s SIFT descriptor Credit: David Lowe

18. Image features “ The most important element” (?) Example: David Lowe’s SIFT descriptor Credit: David Lowe

19. Classification Credit: B. Heisele, Y. Ivanov, T. Poggio

20. Challenges: Too many to list..

21. Challenges 1: view point variation Michelangelo 1475-1564 slide by Fei Fei, Fergus & Torralba

22. Challenges 2: illumination slide credit: S. Ullman

23. Challenges 3: occlusion Magritte, 1957 slide by Fei Fei, Fergus & Torralba

24. Challenges 4: scale slide by Fei Fei, Fergus & Torralba

25. Challenges 5: deformation Xu, Beihong 1943 slide by Fei Fei, Fergus & Torralba

26. Challenges 6: background clutter Klimt, 1913 slide by Fei Fei, Fergus & Torralba

27. Challenges 7: object intra-class variation slide by Fei-Fei, Fergus & Torralba

28. Challenges 8: local ambiguity slide by Fei-Fei, Fergus & Torralba

29. Challenges 9: the world behind the image Credit: A. Efros