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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