Introduction. Selim Aksoy. Bilkent University

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1 Introduction Selim Aksoy Department of Computer Engineering Bilkent University

2 What is computer vision? Analysis of digital images by a computer. Stockman and Shapiro: making useful decisions about real physical objects and scenes based on sensed images. Trucco and Verri: computing properties of the 3D world from one or more digital images. Ballard and Brown: construction of explicit, meaningful description of physical objects from images. Forsyth and Ponce: extracting descriptions of the world from pictures or sequences of pictures. CS 484, Spring , Selim Aksoy 2

3 Why study computer vision? Possibility of building intelligent machines is fascinating. Capability of understanding the visual world is a prerequisite for such machines. Much of the human brain is dedicated to vision. Humans solve many visual problems effortlessly, yet we have little understanding of visual cognition. CS 484, Spring , Selim Aksoy 3

4 Why study computer vision? Fast growing collections and many useful applications. Goals of vision research: Give machines the ability to understand scenes. Aid understanding and modeling of human vision. Automate visual operations. Adapted from CSE 455, U of Washington CS 484, Spring , Selim Aksoy 4

5 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 5

6 Medical image analysis 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 3D imaging: MRI, CT Image guided surgery Grimson et al., MIT Adapted from CSE 455, U of Washington CS 484, Spring , Selim Aksoy 9

10 Medical image analysis Cancer detection and grading CS 484, Spring , Selim Aksoy 10

11 Medical image analysis Slice of lung Adapted from Linda Shapiro, U of Washington CS 484, Spring , Selim Aksoy 11

12 Medical image analysis CS 484, Spring , Selim Aksoy 12

13 Biometrics Adapted from Anil Jain, Michigan State 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 Vehicle and pedestrian protection Lane departure warning, collision warning, traffic sign recognition, pedestrian recognition, blind spot warning CS 484, Spring , Selim Aksoy 20

21 Smart cars Adapted from CSE 455, U of Washington 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 Land cover classification CS 484, Spring , Selim Aksoy 23

24 Land cover classification CS 484, Spring , Selim Aksoy 24

25 Object recognition CS 484, Spring , Selim Aksoy 25

26 Object recognition Recognition of buildings and building groups CS 484, Spring , Selim Aksoy 26

27 Content-based retrieval Finding similar regions: airports CS 484, Spring , Selim Aksoy 27

28 Robotics Adapted from CSE 455, U of Washington CS 484, Spring , Selim Aksoy 28

29 Robotics Adapted from Steven Seitz, U of Washington CS 484, Spring , Selim Aksoy 29

30 Autonomous navigation Michigan State University General Dynamics Robotics Systems CS 484, Spring , Selim Aksoy 30

31 Industrial automation Automatic fruit sorting Color Vision Systems CS 484, Spring , Selim Aksoy 31

32 Industrial automation Industrial robotics; bin picking CS 484, Spring , Selim Aksoy 32

33 Postal service automation General Dynamics Robotics Systems CS 484, Spring , Selim Aksoy 33

34 Optical character recognition Digit recognition, AT&T labs License place recognition Adapted from Steven Seitz, U of Washington CS 484, Spring , Selim Aksoy 34

35 Document analysis Adapted from Shapiro and Stockman CS 484, Spring , Selim Aksoy 35

36 Document analysis Adapted from Linda Shapiro, U of Washington CS 484, Spring , Selim Aksoy 36

37 Sports video analysis Tennis review system CS 484, Spring , Selim Aksoy 37

38 Scene classification CS 484, Spring , Selim Aksoy 38

39 Organizing image archives Adapted from Pinar Duygulu, Bilkent University CS 484, Spring , Selim Aksoy 39

40 Photo tourism: exploring photo collections Building 3D scene models from individual photos Adapted from Steven Seitz, U of Washington CS 484, Spring , Selim Aksoy 40

41 Content-based retrieval CS 484, Spring , Selim Aksoy 41

42 Content-based retrieval CS 484, Spring , Selim Aksoy 42

43 Content-based retrieval Online shopping catalog search CS 484, Spring , Selim Aksoy 43

44 Face detection and recognition Adapted from CSE 455, U of Washington CS 484, Spring , Selim Aksoy 44

45 Object recognition Adapted from Rob Fergus, MIT CS 484, Spring , Selim Aksoy 45

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

47 3D reconstruction Adapted from David Forsyth, UC Berkeley CS 484, Spring , Selim Aksoy 47

48 3D reconstruction Adapted from David Forsyth, UC Berkeley CS 484, Spring , Selim Aksoy 48

49 Motion capture Adapted from Linda Shapiro, U of Washington CS 484, Spring , Selim Aksoy 49

50 Visual effects 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 Challenge What do you see in the picture? A hand holding a man A hand holding a shiny sphere An Escher drawing Adapted from Octavia Camps, Penn State CS 484, Spring , Selim Aksoy 54

55 Perception and grouping Subjective contours CS 484, Spring , Selim Aksoy 55

56 Perception and grouping Subjective contours Adapted from Michael Black, Brown University CS 484, Spring , Selim Aksoy 56

57 Perception and grouping Adapted from Gonzales and Woods CS 484, Spring , Selim Aksoy 57

58 Perception and grouping Adapted from Gonzales and Woods CS 484, Spring , Selim Aksoy 58

59

60 Copyright A.Kitaoka 2003 CS 484, Spring , Selim Aksoy 60

61 Perception and grouping Occlusion Adapted from Michael Black, Brown University CS 484, Spring , Selim Aksoy 61

62 Perception and grouping The shape of junctions constrains the possible interpretations of the scene. Ambiguous: paint and surface boundaries can be confused. Adapted from Michael Black, Brown University CS 484, Spring , Selim Aksoy 62

63 Challenges 1: view point variation Michelangelo Adapted from L. Fei-Fei, R. Fergus, A. Torralba CS 484, Spring , Selim Aksoy 63

64 Challenges 2: illumination Adapted from L. Fei-Fei, R. Fergus, A. Torralba CS 484, Spring , Selim Aksoy 64

65 Challenges 3: occlusion Magritte, 1957 Adapted from L. Fei-Fei, R. Fergus, A. Torralba CS 484, Spring , Selim Aksoy 65

66 Challenges 4: scale Adapted from L. Fei-Fei, R. Fergus, A. Torralba CS 484, Spring , Selim Aksoy 66

67 Challenges 5: deformation Xu, Beihong 1943 Adapted from L. Fei-Fei, R. Fergus, A. Torralba CS 484, Spring , Selim Aksoy 67

68 Challenges 6: background clutter Klimt, 1913 Adapted from L. Fei-Fei, R. Fergus, A. Torralba CS 484, Spring , Selim Aksoy 68

69 Challenges 7: intra-class variation Adapted from L. Fei-Fei, R. Fergus, A. Torralba CS 484, Spring , Selim Aksoy 69

70 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 70

71 Color Adapted from Martial Hebert, CMU CS 484, Spring , Selim Aksoy 71

72 Texture Adapted from David Forsyth, UC Berkeley CS 484, Spring , Selim Aksoy 72

73 Segmentation Original Images Color Regions Texture Regions Line Clusters Adapted from Linda Shapiro, U of Washington CS 484, Spring , Selim Aksoy 73

74 Segmentation Adapted from Jianbo Shi, U Penn CS 484, Spring , Selim Aksoy 74

75 Shape Recognized objects Model database Adapted from Enis Cetin, Bilkent University CS 484, Spring , Selim Aksoy 75

76 Motion Adapted from Michael Black, Brown University CS 484, Spring , Selim Aksoy 76

77 Recognition Adapted from Michael Black, Brown University CS 484, Spring , Selim Aksoy 77

78 Recognition 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 Michael Black, Brown University CS 484, Spring , Selim Aksoy 80

81 Recognition Adapted from Michael Black, Brown University CS 484, Spring , Selim Aksoy 81

82 Recognition CS 484, Spring , Selim Aksoy 82

83 Recognition Adapted from David Forsyth, UC Berkeley CS 484, Spring , Selim Aksoy 83

84 Detection Adapted from David Forsyth, UC Berkeley CS 484, Spring , Selim Aksoy 84

85 Detection Adapted from David Forsyth, UC Berkeley CS 484, Spring , Selim Aksoy 85

86 Detection Adapted from Michael Black, Brown University CS 484, Spring , Selim Aksoy 86

87 Parts and relations Adapted from Michael Black, Brown University CS 484, Spring , Selim Aksoy 87

88 Parts and relations Adapted from Michael Black, Brown University CS 484, Spring , Selim Aksoy 88

89 Context Adapted from Antonio Torralba, MIT CS 484, Spring , Selim Aksoy 89

90 Context Adapted from Antonio Torralba, MIT CS 484, Spring , Selim Aksoy 90

91 Context Adapted from Derek Hoiem, CMU CS 484, Spring , Selim Aksoy 91

92 Context Adapted from Derek Hoiem, CMU CS 484, Spring , Selim Aksoy 92

93 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 93

94 Low-level sharpening blurring Adapted from Linda Shapiro, U of Washington CS 484, Spring , Selim Aksoy 94

95 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 95

96 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 96

97 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 97

Introduction. Selim Aksoy. Bilkent University

Introduction. Selim Aksoy. Bilkent University Introduction Selim Aksoy Department of Computer Engineering Bilkent University saksoy@cs.bilkent.edu.tr What is computer vision? Analysis of digital images by a computer. Stockman and Shapiro: making useful

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