Advanced Computer Vision

1. Advanced Computer Vision Devi Parikh Electrical and Computer Engineering

2. Plan for today • Topic overview • Introductions • Course overview: • Logistics • Requirements • Please interrupt at any time with questions or comments

3. Computer Vision Automatic understanding of images and video Computing properties of the 3D world from visual data (measurement) Algorithms and representations to allow a machine to recognize objects, people, scenes, and activities. (perception and interpretation) Algorithms to mine, search, and interact with visual data (search and organization) Kristen Grauman

4. What does recognition involve? Fei-Fei Li

5. Detection: are there people?

6. Activity: What are they doing?

7. Object categorization mountain tree building banner street lamp vendor people

8. Instance recognition Potala Palace A particular sign

9. Scene and context categorization • outdoor • city • …

10. Attribute recognition gray made of fabric crowded flat

11. Why recognition? Recognition a fundamental part of perception e.g., robots, autonomous agents Organize and give access to visual content Connect to information Detect trends and themes Where are we now? Kristen Grauman

12. We’ve come a long way…

13. We’ve come a long way…

14. We’ve come a long way…

15. Posing visual queries Yeh et al., MIT Belhumeur et al. Kooaba, Bay & Quack et al. Kristen Grauman

16. Exploring community photo collections Snavely et al. Kristen Grauman Simon & Seitz

17. Autonomous agents able to detect objects Kristen Grauman http://www.darpa.mil/grandchallenge/gallery.asp

18. We’ve come a long way… Fischler and Elschlager, 1973

19. We’ve come a long way…

20. We’ve come a long way… Dollar et al., BMVC 2009

21. Still a long way to go… Dollar et al., BMVC 2009

22. Dollar et al., BMVC 2009

23. Dollar et al., BMVC 2009

24. Challenges

25. Intra-class appearance Challenges: robustness Illumination Object pose Clutter Occlusions Viewpoint Kristen Grauman

26. Challenges: context and human experience Context cues Kristen Grauman

27. Challenges: context and human experience Function Dynamics Context cues Kristen Grauman Video credit: J. Davis

28. Challenges: scale, efficiency • Half of the cerebral cortex in primates is devoted to processing visual information • ~20 hours of video added to YouTube per minute • ~5,000 new tagged photos added to Flickr per minute • Thousands to millions of pixels in an image • 30+ degrees of freedom in the pose of articulated objects (humans) • 3,000-30,000 human recognizable object categories Kristen Grauman

29. Challenges: learning with minimal supervision More Less Unlabeled, multiple objects Cropped to object, parts and classes labeled Classes labeled, some clutter Kristen Grauman

30. Slide from Pietro Perona, 2004 Object Recognition workshop

31. Slide from Pietro Perona, 2004 Object Recognition workshop

32. What kinds of things work best today? Reading license plates, zip codes, checks Frontal face detection Recognizing flat, textured objects (like books, CD covers, posters) Fingerprint recognition Kristen Grauman

33. Inputs in 1963… L. G. Roberts, Machine Perception of Three Dimensional Solids, Ph.D. thesis, MIT Department of Electrical Engineering, 1963. Kristen Grauman

34. Personal photo albums Movies, news, sports Medical and scientific images Surveillance and security … and inputs today Slide credit; L. Lazebnik

35. … and inputs today 916,271 titles 10 mil. videos, 65,000 added daily Images on the Web Movies, news, sports 350 mil. photos, 1 mil. added daily 1.6 bil. images indexed as of summer 2005 Satellite imagery City streets Understand and organize and index all this data!! Slide credit; L. Lazebnik

36. Introductions • What is your name? • Which program are you in? How far along? • What is your research area and current project about? • Take a minute to explain it to us • In a way that we can all follow • Have you taken a computer vision course before? Machine learning or pattern recognition? • What are you hoping to get out of this class?

37. This course ECE 5984 TR 3:30 pm to 4:45 pm Hutcheson (HUTCH) 207 Office hours: by appointment (email) Course webpage: http://filebox.ece.vt.edu/~S14ECE5984/ (Google me  My homepage  Teaching)

38. This course Focus on current research in computer vision High-level recognition problems, innovative applications.

39. Goals Understand state-of-the-art approaches Analyze and critique current approaches Identify interesting research questions Present clearly and methodically

40. Expectations Discussions will center on recent papers in the field [15%] Paper reviews each class [25%] Can have 3 late days over the course of the semester Presentations (2-3 times) [25%] Papers and background reading Experiments Project [35%] No “Assignments”, Exams, etc.

41. Prerequisites Course in computer vision Courses in machine learning is a plus

42. Paper reviews For each class Review one paper in detail Review one paper at a high-level (Reduced from last time I offered this course) Email me reviews by noon (12:00 pm) the day of the class Skip reviews the classes you are presenting.

43. Paper review guidelines One page Detailed review: Brief (2-3 sentences) summary Main contribution Strengths? Weaknesses? How convincing are the experiments? Suggestions to improve them? Extensions? Applications? Additional comments, unclear points High-level review: Problem being addressed High-level intuition/idea of approach Relationships observed between the papers we are reading Will pick on students in class during discussions Write in your own words Write well, proof read

44. Paper presentation guidelines Papers Experiments

45. Papers Read selected papers in topic area and look at background papers as necessary Well-organized talk, 45 minutes What to cover? Topic overview, motivation For selected papers: Problem overview, motivation Algorithm explanation, technical details Experimental set up, results Strengths, weaknesses, extensions Any commonalities, important differences between techniques covered in the papers. See class webpage for more details.

46. Experiments Implement/download code for a main idea in the paper and evaluate it: Experiment with different types of training/testing data sets Evaluate sensitivity to important parameter settings Show an example to analyze a strength/weakness of the approach Show qualitative and quantitative results

47. Tips Look up papers and authors. Their webpage may have data, code, slides, videos, etc. Make sure talk flows well and makes sense as a whole. Cite ALL sources. Don’t forget the high-level picture. Give a very clear and well-organized and thought out talk. Will interrupt if something is not clear

48. Tips Make sure you are saying everything we need to know to understand what you are saying. Make sure you know what you are talking about. Think about your audience. Make your talks visual (images, video, not lots of text).

49. Projects Possibilities: Extension of a technique studied in class Analysis and empirical evaluation of an existing technique Comparison between two approaches Design and evaluate a novel approach Be creative! Can work with a partner Talk to me if you need help with ideas

50. Project timeline Project proposals (1 page) [10%] March 6th Mid-semester presentations (10 minutes) [20%] March 27th and April 1st Final presentations (20 minutes) [35%] April 24th to May 6th Project reports (4 pages) [35%] May 12th Could serve as a first draft of a conference submission!