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Introduction - Bilkent University Computer Engineering Department
Introduction Selim Aksoy Department of Computer Engineering Bilkent University [email protected] 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 2010 ©2010, Selim Aksoy 2 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 2010 ©2010, Selim Aksoy 3 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. CS 484, Spring 2010 ©2010, Selim Aksoy Adapted from CSE 455, U of Washington 4 Applications Medical image analysis Security Biometrics Surveillance Tracking Target recognition Remote sensing Robotics CS 484, Spring 2010 Industrial inspection, quality control Document analysis Multimedia Assisted living Human-computer interfaces … ©2010, Selim Aksoy 5 Medical image analysis http://www.clarontech.com CS 484, Spring 2010 ©2010, Selim Aksoy 6 Medical image analysis http://www.clarontech.com CS 484, Spring 2010 ©2010, Selim Aksoy 7 Medical image analysis http://www.clarontech.com CS 484, Spring 2010 ©2010, Selim Aksoy 8 Medical image analysis 3D imaging: MRI, CT CS 484, Spring 2010 Image guided surgery Grimson et al., MIT ©2010, Selim Aksoy Adapted from CSE 455, U of Washington 9 Medical image analysis Cancer detection and grading CS 484, Spring 2010 ©2010, Selim Aksoy 10 Medical image analysis Slice of lung CS 484, Spring 2010 ©2010, Selim Aksoy Adapted from Linda Shapiro, U of Washington 11 Medical image analysis CS 484, Spring 2010 ©2010, Selim Aksoy 12 Biometrics Adapted from Anil Jain, Michigan State CS 484, Spring 2010 ©2010, Selim Aksoy 13 Biometrics Adapted from Anil Jain, Michigan State CS 484, Spring 2010 ©2010, Selim Aksoy 14 Surveillance and tracking CS 484, Spring 2010 University of Central Florida, Computer Vision Lab ©2010, Selim Aksoy 15 Surveillance and tracking Adapted from Octavia Camps, Penn State CS 484, Spring 2010 ©2010, Selim Aksoy 16 Surveillance and tracking Adapted from Martial Hebert, CMU CS 484, Spring 2010 ©2010, Selim Aksoy 17 Surveillance and tracking Generating traffic patterns University of Central Florida, Computer Vision Lab CS 484, Spring 2010 ©2010, Selim Aksoy 18 Surveillance and tracking Tracking in UAV videos Adapted from Martial Hebert, CMU, and Masaharu Kobashi, U of Washington CS 484, Spring 2010 ©2010, Selim Aksoy 19 Vehicle and pedestrian protection Lane departure warning, collision warning, traffic sign recognition, pedestrian recognition, blind spot warning CS 484, Spring 2010 ©2010, Selim Aksoy http://www.mobileye-vision.com 20 Smart cars CS 484, Spring 2010 ©2010, Selim Aksoy Adapted from CSE 455, U of Washington 21 Forest fire monitoring system Early warning of forest fires CS 484, Spring 2010 ©2010, Selim Aksoy Adapted from Enis Cetin, Bilkent University 22 Land cover classification CS 484, Spring 2010 ©2010, Selim Aksoy 23 Land cover classification CS 484, Spring 2010 ©2010, Selim Aksoy 24 Object recognition CS 484, Spring 2010 ©2010, Selim Aksoy 25 Object recognition Recognition of buildings and building groups CS 484, Spring 2010 ©2010, Selim Aksoy 26 Content-based retrieval Finding similar regions: airports CS 484, Spring 2010 ©2010, Selim Aksoy 27 Robotics CS 484, Spring 2010 ©2010, Selim Aksoy Adapted from CSE 455, U of Washington 28 Robotics Adapted from Steven Seitz, U of Washington CS 484, Spring 2010 ©2010, Selim Aksoy 29 Autonomous navigation Michigan State University CS 484, Spring 2010 General Dynamics Robotics Systems http://www.gdrs.com ©2010, Selim Aksoy 30 Industrial automation Automatic fruit sorting Color Vision Systems http://www.cvs.com.au CS 484, Spring 2010 ©2010, Selim Aksoy 31 Industrial automation Industrial robotics; bin picking http://www.braintech.com CS 484, Spring 2010 ©2010, Selim Aksoy 32 Postal service automation General Dynamics Robotics Systems http://www.gdrs.com CS 484, Spring 2010 ©2010, Selim Aksoy 33 Optical character recognition Digit recognition, AT&T labs License place recognition http://www.research.att.com/~yann Adapted from Steven Seitz, U of Washington CS 484, Spring 2010 ©2010, Selim Aksoy 34 Document analysis Adapted from Shapiro and Stockman CS 484, Spring 2010 ©2010, Selim Aksoy 35 Document analysis CS 484, Spring 2010 ©2010, Selim Aksoy Adapted from Linda Shapiro, U of Washington 36 Sports video analysis Tennis review system CS 484, Spring 2010 ©2010, Selim Aksoy http://www.hawkeyeinnovations.co.uk 37 Scene classification CS 484, Spring 2010 ©2010, Selim Aksoy 38 Organizing image archives Adapted from Pinar Duygulu, Bilkent University CS 484, Spring 2010 ©2010, Selim Aksoy 39 Photo tourism: exploring photo collections Building 3D scene models from individual photos CS 484, Spring 2010 ©2010, Selim Aksoy Adapted from Steven Seitz, U of Washington 40 Content-based retrieval CS 484, Spring 2010 ©2010, Selim Aksoy 41 Content-based retrieval CS 484, Spring 2010 ©2010, Selim Aksoy 42 Content-based retrieval Online shopping catalog search http://www.like.com CS 484, Spring 2010 ©2010, Selim Aksoy 43 Face detection and recognition CS 484, Spring 2010 ©2010, Selim Aksoy Adapted from CSE 455, U of Washington 44 Object recognition CS 484, Spring 2010 ©2010, Selim Aksoy Adapted from Rob Fergus, MIT 45 3D scanning Adapted from Linda Shapiro, U of Washington CS 484, Spring 2010 ©2010, Selim Aksoy 46 3D reconstruction Adapted from David Forsyth, UC Berkeley CS 484, Spring 2010 ©2010, Selim Aksoy 47 3D reconstruction Adapted from David Forsyth, UC Berkeley CS 484, Spring 2010 ©2010, Selim Aksoy 48 Motion capture CS 484, Spring 2010 ©2010, Selim Aksoy Adapted from Linda Shapiro, U of Washington 49 Visual effects CS 484, Spring 2010 ©2010, Selim Aksoy Adapted from CSE 455, U of Washington 50 Mozaic Adapted from David Forsyth, UC Berkeley CS 484, Spring 2010 ©2010, Selim Aksoy 51 Mozaic Adapted from David Forsyth, UC Berkeley CS 484, Spring 2010 ©2010, Selim Aksoy 52 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 2010 ©2010, Selim Aksoy 53 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 2010 ©2010, Selim Aksoy 54 Perception and grouping Subjective contours CS 484, Spring 2010 ©2010, Selim Aksoy 55 Perception and grouping Subjective contours Adapted from Michael Black, Brown University CS 484, Spring 2010 ©2010, Selim Aksoy 56 Perception and grouping Adapted from Gonzales and Woods CS 484, Spring 2010 ©2010, Selim Aksoy 57 Perception and grouping Adapted from Gonzales and Woods CS 484, Spring 2010 ©2010, Selim Aksoy 58 CS 484, Spring 2010 ©2010, Selim Aksoy Copyright A.Kitaoka 2003 60 Perception and grouping Occlusion Adapted from Michael Black, Brown University CS 484, Spring 2010 ©2010, Selim Aksoy 61 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 2010 ©2010, Selim Aksoy 62 Challenges 1: view point variation Michelangelo 1475-1564 Adapted from L. Fei-Fei, R. Fergus, A. Torralba CS 484, Spring 2010 ©2010, Selim Aksoy 63 Challenges 2: illumination CS 484, Spring 2010 ©2010, Selim Aksoy Adapted from L. Fei-Fei, R. Fergus, A. Torralba 64 Challenges 3: occlusion Magritte, 1957 Adapted from L. Fei-Fei, R. Fergus, A. Torralba CS 484, Spring 2010 ©2010, Selim Aksoy 65 Challenges 4: scale Adapted from L. Fei-Fei, R. Fergus, A. Torralba CS 484, Spring 2010 ©2010, Selim Aksoy 66 Challenges 5: deformation Xu, Beihong 1943 CS 484, Spring 2010 ©2010, Selim Aksoy Adapted from L. Fei-Fei, R. Fergus, A. Torralba 67 Challenges 6: background clutter Klimt, 1913 Adapted from L. Fei-Fei, R. Fergus, A. Torralba CS 484, Spring 2010 ©2010, Selim Aksoy 68 Challenges 7: intra-class variation CS 484, Spring 2010 ©2010, Selim Aksoy Adapted from L. Fei-Fei, R. Fergus, A. Torralba 69 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 2010 ©2010, Selim Aksoy 70 Color Adapted from Martial Hebert, CMU CS 484, Spring 2010 ©2010, Selim Aksoy 71 Texture CS 484, Spring 2010 ©2010, Selim Aksoy Adapted from David Forsyth, UC Berkeley 72 Segmentation Original Images Color Regions Texture Regions Line Clusters Adapted from Linda Shapiro, U of Washington CS 484, Spring 2010 ©2010, Selim Aksoy 73 Segmentation CS 484, Spring 2010 ©2010, Selim Aksoy Adapted from Jianbo Shi, U Penn 74 Shape Recognized objects Adapted from Enis Cetin, Bilkent University Model database CS 484, Spring 2010 ©2010, Selim Aksoy 75 Motion Adapted from Michael Black, Brown University CS 484, Spring 2010 ©2010, Selim Aksoy 76 Recognition Adapted from Michael Black, Brown University CS 484, Spring 2010 ©2010, Selim Aksoy 77 Recognition Adapted from Michael Black, Brown University CS 484, Spring 2010 ©2010, Selim Aksoy 78 Recognition Adapted from Michael Black, Brown University CS 484, Spring 2010 ©2010, Selim Aksoy 79 Recognition Adapted from Michael Black, Brown University CS 484, Spring 2010 ©2010, Selim Aksoy 80 Recognition Adapted from Michael Black, Brown University CS 484, Spring 2010 ©2010, Selim Aksoy 81 Recognition CS 484, Spring 2010 ©2010, Selim Aksoy 82 Recognition Adapted from David Forsyth, UC Berkeley CS 484, Spring 2010 ©2010, Selim Aksoy 83 Detection Adapted from David Forsyth, UC Berkeley CS 484, Spring 2010 ©2010, Selim Aksoy 84 Detection Adapted from David Forsyth, UC Berkeley CS 484, Spring 2010 ©2010, Selim Aksoy 85 Detection Adapted from Michael Black, Brown University CS 484, Spring 2010 ©2010, Selim Aksoy 86 Parts and relations Adapted from Michael Black, Brown University CS 484, Spring 2010 ©2010, Selim Aksoy 87 Parts and relations Adapted from Michael Black, Brown University CS 484, Spring 2010 ©2010, Selim Aksoy 88 Context Adapted from Antonio Torralba, MIT CS 484, Spring 2010 ©2010, Selim Aksoy 89 Context Adapted from Antonio Torralba, MIT CS 484, Spring 2010 ©2010, Selim Aksoy 90 Context Adapted from Derek Hoiem, CMU CS 484, Spring 2010 ©2010, Selim Aksoy 91 Context Adapted from Derek Hoiem, CMU CS 484, Spring 2010 ©2010, Selim Aksoy 92 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 2010 ©2010, Selim Aksoy 93 Low-level sharpening blurring Adapted from Linda Shapiro, U of Washington CS 484, Spring 2010 ©2010, Selim Aksoy 94 Low-level Canny original image Mid-level edge image ORT data structure edge image CS 484, Spring 2010 circular arcs and line segments ©2010, Selim Aksoy Adapted from Linda Shapiro, U of Washington 95 Mid-level K-means clustering (followed by connected component analysis) regions of homogeneous color original color image data structure Adapted from Linda Shapiro, U of Washington CS 484, Spring 2010 ©2010, Selim Aksoy 96 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 2010 ©2010, Selim Aksoy 97
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