Outline

1. Outline • Syllabus • Introduction • Computational Paradigms for Vision • Appearance-based computer vision • Physics-based computer vision

2. Class Materials • In this class most of the time we will discuss papers from the literature • At the beginning I will give a general introduction based on chapters from different books • There is no required textbook for this class Computer Vision

3. Vision • Vision • The process of acquiring knowledge about the environmental objects and events by extracting information from the light they emit or reflect • Vision is a very complicated process, involving different processes such as memory • Vision is the most useful source for information as about 50% of the human brain is devoted to visual processing Computer Vision

4. Vision – cont. • Vision has been studied from many different perspectives • Computational vision • Emphasis on approaches that are biologically plausible • Computer vision • Emphasis on algorithms to solve particular problems • Statistical vision • Emphasis on developing and analyzing mathematical and statistical models Computer Vision

5. Darwin X Computer Vision Source: NewScientist

6. Computer Vision • Computer vision tries to automate the vision process by building devices that simulate the human vision process • Note that devices that solve part of the problems can be very useful Computer Vision

7. Motivation Examples • Computer vision techniques can provide novel opportunities and improve performance of existing systems (sometimes significantly) • Hopefully the following examples will convince you Computer Vision

8. Human Computer Interfaces • Mouse gestures • Allow one to control programs more easily by drawing commands using mouse • Some of the 80 gestures recognized by strokeit (http://www.tcbmi.com/strokeit/) Computer Vision

9. Mouse Gestures • In Photoshop, for example, you can • In a web browser, you can Computer Vision

10. Human-Computer Interactions Computer Vision

11. 3D Hand Mouse Computer Vision

12. HandiEye Computer Vision

13. Sign Language Recognition Computer Vision

14. ALVINN Computer Vision

15. Computer Vision

16. RALPH Computer Vision

17. Applications– continued Computer Vision

18. DARPA Grant Challenge • http://www.darpa.mil/grandchallenge/gcorg/index.html Computer Vision

19. DARPA Grant Challenge Computer Vision

20. Introduction – cont. • Honda ASIMO • http://world.honda.com/ASIMO/ Computer Vision

21. Automated Map Updating Computer Vision

22. Automated Map Updating Computer Vision

23. 3D Urban Models Computer Vision

24. Image-Guided Neurosurgery Computer Vision

25. Intracardiac Surgical Planning Computer Vision

26. Medical Image Analysis Computer Vision

27. Detection and Recognition Computer Vision

28. Detection and Recognition of Text in Natural Scenes Computer Vision

29. Detection and Recognition of Text in Natural Scenes Computer Vision

30. Text Detection and Recognition in Images and Videos Computer Vision

31. Driver Monitoring System Computer Vision

32. Face Recognition http://www.a4vision.com Computer Vision

33. Intelligent Transportation Systems http://dfwtraffic.dot.state.tx.us/dal-cam-nf.asp Computer Vision

34. Handwritten Address Interpretation System • HWAI -http://www.cedar.buffalo.edu/HWAI/ • The HWAI (Handwritten Address Interpretation) System was developed at Center of Excellence for Document Analysis and Recognition (CEDAR) at University at Buffalo, The State University of New York. It resulted from many years of research at CEDAR on the problems of Address Block location, Handwritten Digit/Character/Word Recognition, Database Compression, Information Retrieval, Real-Time Image Processing, and Loosely-Coupled Multiprocessing. • The following presentation is based on the demonstration pages at HWAI Computer Vision

35. Handwritten Address Interpretation System – cont. • Step 1: Digitization Computer Vision

36. Handwritten Address Interpretation System – Cont. • Step 2: Address Block Location Computer Vision

37. Handwritten Address Interpretation System – Cont. • Step 3: Address Extraction Computer Vision

38. Handwritten Address Interpretation System – Cont. • Step 4: Binarization Computer Vision

39. Handwritten Address Interpretation System – Cont. • Step 5: Line Separation Computer Vision

40. Handwritten Address Interpretation System – Cont. • Step 6: Address Parsing Computer Vision

41. Handwritten Address Interpretation System – Cont. • Step 7: Recognition • (a) State Abbreviation Recognition Computer Vision

42. Handwritten Address Interpretation System – Cont. • Step 7: Recognition • (b) ZIP Code Recognition Computer Vision

43. Handwritten Address Interpretation System – Cont. • Step 7: Recognition • (c) Street Number Recognition Computer Vision

44. Handwritten Address Interpretation System – Cont. • Step 8: Street Name Recognition Computer Vision

45. Handwritten Address Interpretation System – Cont. • Step 9: Delivery Point Codes Computer Vision

46. Handwritten Address Interpretation System – Cont. • Step 10: Bar coding Computer Vision

47. Military Applications • Unmanned Aerial Vehicles Computer Vision

48. Automated Global Monitoring Computer Vision

49. Approaches to Computer Vision • Vision is a complicated computational process • Try to simulate the human vision system • Try to build mathematical formulations of the environment (to be perceived) and then perform inference • Try to invent approximate but efficient short cuts to the general vision problem Computer Vision

50. Neuroanatomy of the Brain Computer Vision