Introduction. Selim Aksoy. Bilkent University

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1 Introduction Selim Aksoy Department of Computer Engineering Bilkent University

2 What is computer vision? What does it mean, to see? The plain man's answer (and Aristotle's, too) would be, to know what is where by looking. -- David Marr, Vision (1982) 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). Adapted from Trevor Darrell, UC Berkeley, Alyosha Efros, Carnegie Mellon CS 484, Spring , Selim Aksoy 2

3 Why study computer vision? As image sources multiply, so do applications Relieve humans of boring, easy tasks Enhance human abilities: human-computer interaction, visualization Perception for robotics / autonomous agents Organize and give access to visual content Goals of vision research: Give machines the ability to understand scenes. Aid understanding and modeling of human vision. Automate visual operations. Adapted from Trevor Darrell, UC Berkeley CS 484, Spring , Selim Aksoy 3

4 Why study computer vision? Personal photo albums Movies, news, sports Surveillance and security Medical and scientific images CS 484, Spring , Selim Aksoy 4

5 Related disciplines Graphics Image processing Artificial intelligence Computer vision Algorithms Machine learning Cognitive science CS 484, Spring , Selim Aksoy 5

6 Applications Medical image analysis Security Biometrics Surveillance Tracking Target recognition Remote sensing Robotics Industrial inspection, quality control Document analysis Multimedia Assisted living Human-computer interfaces CS 484, Spring , Selim Aksoy 6

7 Medical image analysis CS 484, Spring , Selim Aksoy 7

8 Medical image analysis CS 484, Spring , Selim Aksoy 8

9 Medical image analysis CS 484, Spring , Selim Aksoy 9

10 Medical image analysis 3D imaging: MRI, CT Image guided surgery Grimson et al., MIT Adapted from CSE 455, U of Washington CS 484, Spring , Selim Aksoy 10

11 Medical image analysis Cancer detection and grading CS 484, Spring , Selim Aksoy 11

12 Medical image analysis Slice of lung Adapted from Linda Shapiro, U of Washington CS 484, Spring , Selim Aksoy 12

13 Medical image analysis CS 484, Spring , Selim Aksoy 13

14 Biometrics Adapted from Anil Jain, Michigan State CS 484, Spring , Selim Aksoy 14

15 Surveillance and tracking University of Central Florida, Computer Vision Lab CS 484, Spring , Selim Aksoy 15

16 Surveillance and tracking Adapted from Octavia Camps, Penn State CS 484, Spring , Selim Aksoy 16

17 Surveillance and tracking Adapted from Martial Hebert, CMU CS 484, Spring , Selim Aksoy 17

18 Surveillance and tracking Generating traffic patterns University of Central Florida, Computer Vision Lab CS 484, Spring , Selim Aksoy 18

19 Surveillance and tracking Tracking in UAV videos Adapted from Martial Hebert, CMU, and Masaharu Kobashi, U of Washington CS 484, Spring , Selim Aksoy 19

20 Smart cars Adapted from CSE 455, U of Washington CS 484, Spring , Selim Aksoy 20

21 Vehicle and pedestrian protection Lane departure warning, collision warning, traffic sign recognition, pedestrian recognition, blind spot warning CS 484, Spring , Selim Aksoy 21

22 Forest fire monitoring system Early warning of forest fires Adapted from Enis Cetin, Bilkent University CS 484, Spring , Selim Aksoy 22

23 Robotics Adapted from CSE 455, U of Washington CS 484, Spring , Selim Aksoy 23

24 Robotics Adapted from Steven Seitz, U of Washington CS 484, Spring , Selim Aksoy 24

25 Autonomous navigation CS 484, Spring , Selim Aksoy 25

26 Autonomous navigation Michigan State University General Dynamics Robotics Systems CS 484, Spring , Selim Aksoy 26

27 Face detection and recognition Adapted from CSE 455, U of Washington CS 484, Spring , Selim Aksoy 27

28 Industrial automation Automatic fruit sorting Color Vision Systems CS 484, Spring , Selim Aksoy 28

29 Industrial automation Industrial robotics; bin picking CS 484, Spring , Selim Aksoy 29

30 Postal service automation General Dynamics Robotics Systems CS 484, Spring , Selim Aksoy 30

31 Optical character recognition Digit recognition, AT&T labs License place recognition Adapted from Steven Seitz, U of Washington CS 484, Spring , Selim Aksoy 31

32 Document analysis Adapted from Shapiro and Stockman CS 484, Spring , Selim Aksoy 32

33 Document analysis Adapted from Linda Shapiro, U of Washington CS 484, Spring , Selim Aksoy 33

34 Sports video analysis Tennis review system CS 484, Spring , Selim Aksoy 34

35 Scene classification CS 484, Spring , Selim Aksoy 35

36 Object recognition Adapted from Rob Fergus, MIT CS 484, Spring , Selim Aksoy 36

37 Object recognition Lincoln, Microsoft Research kooaba Situated search Yeh et al., MIT Google Goggles Bing Vision CS 484, Spring , Selim Aksoy 37

38 Land cover classification CS 484, Spring , Selim Aksoy 38

39 Land cover classification CS 484, Spring , Selim Aksoy 39

40 Object recognition CS 484, Spring , Selim Aksoy 40

41 Object recognition Recognition of buildings and building groups CS 484, Spring , Selim Aksoy 41

42 Organizing image archives Adapted from Pinar Duygulu, Bilkent University CS 484, Spring , Selim Aksoy 42

43 Photo tourism: exploring photo collections Building 3D scene models from individual photos Adapted from Steven Seitz, U of Washington CS 484, Spring , Selim Aksoy 43

44 Photosynth CS 484, Spring , Selim Aksoy 44

45 Content-based retrieval Online shopping catalog search CS 484, Spring , Selim Aksoy 45

46 3D scanning and reconstruction Adapted from Linda Shapiro, U of Washington CS 484, Spring , Selim Aksoy 46

47 Earth viewers (3D modeling) CS 484, Spring , Selim Aksoy 47

48 Motion capture Adapted from Linda Shapiro, U of Washington CS 484, Spring , Selim Aksoy 48

49 Visual effects Adapted from CSE 455, U of Washington CS 484, Spring , Selim Aksoy 49

50 Motion capture Microsoft s XBox Kinect Adapted from CSE 455, U of Washington CS 484, Spring , Selim Aksoy 50

51 Mozaic Adapted from David Forsyth, UC Berkeley CS 484, Spring , Selim Aksoy 51

52 Mozaic Adapted from David Forsyth, UC Berkeley CS 484, Spring , Selim Aksoy 52

53 Critical issues What information should be extracted? How can it be extracted? How should it be represented? How can it be used to aid analysis and understanding? CS 484, Spring , Selim Aksoy 53

54 Perception and grouping Subjective contours CS 484, Spring , Selim Aksoy 54

55 Perception and grouping Adapted from Gonzales and Woods CS 484, Spring , Selim Aksoy 55

56 Perception and grouping Müller-Lyer Illusion Adapted from Alyosha Efros, Carnegie Mellon CS 484, Spring , Selim Aksoy 56

57

58 Copyright A.Kitaoka 2003 CS 484, Spring , Selim Aksoy 58

59 Perception and grouping Occlusion Adapted from Michael Black, Brown University CS 484, Spring , Selim Aksoy 59

60 What the computer gets CS 484, Spring , Selim Aksoy 60

61 Challenges 1: view point variation Michelangelo Adapted from L. Fei-Fei, R. Fergus, A. Torralba CS 484, Spring , Selim Aksoy 61

62 Challenges 2: illumination Adapted from Fei-Fei Li CS 484, Spring , Selim Aksoy 62

63 Challenges 3: occlusion Magritte, 1957 Adapted from L. Fei-Fei, R. Fergus, A. Torralba CS 484, Spring , Selim Aksoy 63

64 Challenges 4: scale Adapted from L. Fei-Fei, R. Fergus, A. Torralba CS 484, Spring , Selim Aksoy 64

65 Challenges 5: deformation Xu, Beihong 1943 Adapted from L. Fei-Fei, R. Fergus, A. Torralba CS 484, Spring , Selim Aksoy 65

66 Challenges 6: background clutter Adapted from Fei-Fei Li CS 484, Spring , Selim Aksoy 66

67 Challenges 7: intra-class variation Adapted from L. Fei-Fei, R. Fergus, A. Torralba CS 484, Spring , Selim Aksoy 67

68 Recognition How can different cues such as color, texture, shape, motion, etc., can be used for recognition? Which parts of image should be recognized together? How can objects be recognized without focusing on detail? How can objects with many free parameters be recognized? How do we structure very large model bases? CS 484, Spring , Selim Aksoy 68

69 Color Adapted from Martial Hebert, CMU CS 484, Spring , Selim Aksoy 69

70 Texture Adapted from David Forsyth, UC Berkeley CS 484, Spring , Selim Aksoy 70

71 Color, texture, and proximity Adapted from Fei-Fei Li CS 484, Spring , Selim Aksoy 71

72 Segmentation Original Images Color Regions Texture Regions Line Clusters Adapted from Linda Shapiro, U of Washington CS 484, Spring , Selim Aksoy 72

73 Segmentation Adapted from Jianbo Shi, U Penn CS 484, Spring , Selim Aksoy 73

74 Shape Recognized objects Model database Adapted from Enis Cetin, Bilkent University CS 484, Spring , Selim Aksoy 74

75 Motion Adapted from Michael Black, Brown University CS 484, Spring , Selim Aksoy 75

76 Detection Adapted from David Forsyth, UC Berkeley CS 484, Spring , Selim Aksoy 76

77 Detection Adapted from David Forsyth, UC Berkeley CS 484, Spring , Selim Aksoy 77

78 Detection Adapted from Michael Black, Brown University CS 484, Spring , Selim Aksoy 78

79 Recognition Adapted from Michael Black, Brown University CS 484, Spring , Selim Aksoy 79

80 Recognition Adapted from David Forsyth, UC Berkeley CS 484, Spring , Selim Aksoy 80

81 Parts and relations Adapted from Michael Black, Brown University CS 484, Spring , Selim Aksoy 81

82 Parts and relations Adapted from Michael Black, Brown University CS 484, Spring , Selim Aksoy 82

83 Context Adapted from Antonio Torralba, MIT CS 484, Spring , Selim Aksoy 83

84 Context Adapted from Antonio Torralba, MIT CS 484, Spring , Selim Aksoy 84

85 Context Adapted from Derek Hoiem, CMU CS 484, Spring , Selim Aksoy 85

86 Context Adapted from Derek Hoiem, CMU CS 484, Spring , Selim Aksoy 86

87 Stages of computer vision Low-level image image Mid-level image features / attributes Image analysis / image understanding High-level features making sense, recognition CS 484, Spring , Selim Aksoy 87

88 Low-level sharpening blurring Adapted from Linda Shapiro, U of Washington CS 484, Spring , Selim Aksoy 88

89 Low-level Canny original image Mid-level edge image ORT edge image data structure circular arcs and line segments Adapted from Linda Shapiro, U of Washington CS 484, Spring , Selim Aksoy 89

90 Mid-level K-means clustering (followed by connected component analysis) original color image regions of homogeneous color data structure Adapted from Linda Shapiro, U of Washington CS 484, Spring , Selim Aksoy 90

91 Low-level to high-level low-level edge image mid-level high-level consistent line clusters Adapted from Linda Shapiro, U of Washington CS 484, Spring , Selim Aksoy 91

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