Transcript of Components of a computer vision system Lighting Scene Camera Computer Scene Interpretation Srinivasa...
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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)