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Computer Vision, CS766 Staff Instructor: Li Zhang lizh[email protected] TA: Yu-Chi Lai [email protected]

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  • Computer Vision, CS766StaffInstructor: Li [email protected]: Yu-Chi [email protected]

  • TodayIntroductionAdministrative StuffOverview of the Course

  • About MeLi Zhang ()Last name pronounced as Jung New FacultyPhD 2005, U of WashingtonResearch Scientist 06-07, Columbia UResearchVision and GraphicsTeachingCS766 Computer VisoinCS559 Computer Graphics

  • Previous Research Focus3D shape reconstructionFour examples of recovered 3D shapes of a moving face from six video streams

  • Previous Research Focus3D shape reconstructionApplicationLicensed by SONY for GamesUsed by VA Hospital for Prosthetics

  • Please tell me about you

  • PrerequisitesPrerequisitesthese are essential!Data structuresA good working knowledge of C and C++ programming(or willingness/time to pick it up quickly!) Linear algebra Vector calculus

    Course does not assume prior imaging experienceno image processing, graphics, etc.

  • Administrative Stuff4 programming projects 15%, 2-3 weeks each1 final project 40%, 5 weeks, open ended of your choosing, but needs project proposal after 1 weekprogress report after 3 weeksFinal presentation after 5 weeksComputer account: Everyone registered in this class will get a Computer Systems Lab account to do project assignments. Email list: [email protected]

  • Questions?

  • Every picture tells a storyGoal of computer vision is to write computer programs that can interpret images

  • Can computer match human perception? Yes and no (but mostly no!)computers can be better at easy things

  • Can computer match human perception? Yes and no (but mostly no!)computers can be better at easy thingshumans are much better at hard things

  • Computer Vision vs Human Vision Can do amazing things like:

    Recognize people and objectsNavigate through obstaclesUnderstand mood in the scene Imagine stories

    But still is not perfect:

    Suffers from IllusionsIgnores many detailsAmbiguous description of the worldDoesnt care about accuracy of worldSrinivasa Narasimhans slide

  • Computer vision vs Human VisionWhat we seeWhat a computer seesSrinivasa Narasimhans slide

  • Components of a computer vision systemLightingSceneCameraComputerSrinivasa Narasimhans slide

  • Topics Covered

  • Cameras and their opticsTodays Digital CamerasThe Camera ObscuraSrinivasa Narasimhans slide

  • Biological visionHuman EyeMosquito EyeSrinivasa Narasimhans slide

  • Project 1: High Dynamic Range ImagingCameras have limited dynamic rangeShree Nayars slide

  • Project 1: High Dynamic Range ImagingLow Dynamic Range ExposuresCombination Yields High Dynamic RangeShree Nayars slide

  • Image Processing Fourier TransformSampling, ConvolutionImage enhancement Feature detectionSrinivasa Narasimhans slide

  • Camera Projection

  • Image TransformationSteve Seitz and Chuck Dyer, View Morphing, SIGGRAPH 1996

  • Project 2: Panoramic ImagingInput images:Output Image:Steve Seitzs slide

  • Projective Geometry

  • Single View Metrologyhttps://research.microsoft.com/vision/cambridge/3d/3dart.htm

  • Single View Metrologyhttps://research.microsoft.com/vision/cambridge/3d/3dart.htm

  • Shading and Photometric Stereohttp://www.eecs.harvard.edu/~zickler/helmholtz.html

  • Texture ModelingrepeatedstochasticSemi-stochastic structuresradishesrocksyogurtAlexei Efros slide

  • Project 3: Texture SynthesisInputOutputImage Quilting, Efros and Freeman., SIGGRAPH 2002.

  • Project 3: Texture SynthesisGraphcut Textures, Kwatra et al., SIGGRAPH 2003. Input images:Output Image:

  • Multi-view GeometryBinocular Stereo (2 classes)Multiview Stereo (1 class)Structure from Motion (2 classes)http://phototour.cs.washington.edu/

  • Face Detection and Recognition

  • Project 4: EigenFacesFace detection and recognition

  • Motion EstimationHidden Dragon Crouching Tiger

  • Motion EstimationApplicationAndy Serkis, Gollum, Lord of the Rings

  • Segmentationhttp://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/

  • SegmentationApplicationMedical Image Processing

  • MattingInputMattingComposition

  • Light, Color, and Reflection

  • Capturing Light Field Camera Arrays, Graphics Lab, Stanford University

  • Capturing Light Field Applications

  • Structured Light and Ranging Scanninghttp://graphics.stanford.edu/projects/mich/

  • Structured Light and Ranging Scanninghttp://graphics.stanford.edu/projects/mich/

  • Structured Light and Ranging Scanninghttp://graphics.stanford.edu/projects/mich/

  • Novel Cameras and Displayshttp://www1.cs.columbia.edu/CAVE/projects/cc.htm

  • Course Infohttp://www.cs.wisc.edu/~cs766-1/

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