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Components of a computer vision system Lighting Scene Camera Computer Scene Interpretation Srinivasa Narasimhans slide

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Computer vision vs Human Vision What we seeWhat a computer sees Srinivasa Narasimhans slide

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A little story about Computer Vision In 1966, Marvin Minsky at MIT asked his undergraduate student Gerald Jay Sussman to spend the summer linking a camera to a computer and getting the computer to describe what it saw. We now know that the problem is slightly more difficult than that. (Szeliski 2009, Computer Vision)

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A little story about Computer Vision In 1966, Marvin Minsky at MIT asked his undergraduate student Gerald Jay Sussman to spend the summer linking a camera to a computer and getting the computer to describe what it saw. We now know that the problem is slightly more difficult than that. (Szeliski 2009, Computer Vision) Founder, MIT AI project

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A little story about Computer Vision In 1966, Marvin Minsky at MIT asked his undergraduate student Gerald Jay Sussman to spend the summer linking a camera to a computer and getting the computer to describe what it saw. We now know that the problem is slightly more difficult than that. (Szeliski 2009, Computer Vision) Founder, MIT AI project Professor of Electrical Engineering, MIT

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A little story about Computer Vision In 1966, Marvin Minsky at MIT asked his undergraduate student Gerald Jay Sussman to spend the summer linking a camera to a computer and getting the computer to describe what it saw. We now know that the problem is slightly more difficult than that. (Szeliski 2009, Computer Vision) Image Understanding

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A little story about Computer Vision In 1966, Marvin Minsky at MIT asked his undergraduate student Gerald Jay Sussman to spend the summer linking a camera to a computer and getting the computer to describe what it saw. We now know that the problem is slightly more difficult than that. (Szeliski 2009, Computer Vision) Image Understanding Image Sensing

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Continue on CAPTCHA CAPTCHA stands for "Completely Automated Public Turing test to Tell Computers and Humans Apart". Picture of a CAPTCHA in use at Yahoo. http://www.cs.sfu.ca/~mori/research/gimpy/

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Breaking a Visual CAPTCHA http://www.cs.sfu.ca/~mori/research/gimpy/ On EZ-Gimpy: a success rate of 176/191=92%! Other examples http://www.cs.sfu.ca/~mori/research/gimpy/ez /

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Breaking a Visual CAPTCHA http://www.cs.sfu.ca/~mori/research/gimpy/ On more difficult Gimpy: a success rate of 33%! Other examples http://www.cs.sfu.ca/~mori/research/gimpy/ha rd/

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Breaking a Visual CAPTCHA YAHOOs current CAPTCHA format http://en.wikipedia.org/wiki/CAPTCHA

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Face Detection and Recognition Applications: Security, Law Enforcement, Surveillance

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Face Detection and Recognition Smart cameras: auto focus, red eye removal, auto color correction

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Face Detection and Tracking

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Lexus LS600 Driver Monitor System

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General Motion Tracking Hidden Dragon Crouching Tiger

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General Motion Tracking Application Andy Serkis, Gollum, Lord of the Rings

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Segmentation http://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/

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Segmentation using Graph Cuts Application Medical Image Processing

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Segmentation using Graph Cuts Input Matting: Soft Segmentation Composition

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Segmentation using Graph Cuts State-of-the-art Tool (videosnapcut.mp4) http://juew.org/projects/SnapCut/snapcut.htm

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From 2D to 3D http://www.eecs.harvard.edu/~zickler/helmholtz.html

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Projective Geometry

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Single View Metrology http://research.microsoft.com/vision/cambridg e/3d/default.htm

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Single View Metrology http://research.microsoft.com/vision/cambridg e/3d/default.htm

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Stereo scene point optical center image plane

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Stereo Basic Principle: Triangulation Gives reconstruction as intersection of two rays Requires Camera positions point correspondence

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Using 3D structure to organize photos http://phototour.cs.washington.edu/

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Using 3D structure to organize photos http://photosynth.net/

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Reconstructing detailed 3D models example input imagerendered model

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Reconstructing detailed 3D models example input imagerendered model

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Reconstructing detailed 3D models example input imagerendered model http://grail.cs.washington.edu/ projects/mvscpc/

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Reconstructing detailed 3D models example input imagerendered model

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Reconstructing detailed 3D models example input imagerendered model

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Application: View morphing

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From Static Statues to Dynamic Targets http://research.microsoft.com/~larryz/videoviewinterpolation.htm || MSR Image based Reality Project

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Video Projectors Color Cameras Black & White Cameras Spacetime Face Capture System

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System in Action

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Input Videos (640 480, 60fps)

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Spacetime Stereo Reconstruction

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Applications Entertainment: Games & Movies Medical Practice: Prosthetics

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Computational Photography High Dynamic Range Conventional ImageHigh Dynamic Range Image Nayar et al 2002

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Computational Photography High Dynamic Range High Dynamic Range Image Nayar et al 2002 Sensor Optics Modulator Assorted-pixel camera

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Computational Photography High Dynamic Range Digital Gain Adjustment Handheld camera

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Computational Photography High Dynamic Range High Dynamic Range Image Zhang et al 2010 Handheld camera

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Summary Recognize things Reconstruct 3D structures Enhance Photography

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If you are interested in, Courses: CS766 Computer Vision CS638 Special Topics Computational Photography CS638 Special Topics Computational Methods in Medical Image Analysis Faculty: Chuck Dyer, Vikas Singh, Li Zhang Major Conferences: Computer Vision and Pattern Recognition (CVPR) International Conference on Computer Vision (ICCV) European Conference on Computer Vision (ECCV) ACM SIGGRAPH Conference (SIGGRAPH)