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Introduction to Computer Vision

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Title: Introduction to Computer Vision


1
Introduction to Computer Vision
  • CS223B, Winter 2005

2
Richard Szeliski Guest Lecturer
  • Ph. D., Carnegie Mellon, 1988
  • Researcher, Cambridge ResearchLab at DEC,
    1990-1995
  • Senior Researcher, InteractiveVisual Media
    Group, Microsoft, 1995-
  • Research interests
  • computer vision (stereo, motion),computer
    graphics (image-based rendering), parallel
    programming

3
What is Computer Vision?
4
What is Computer Vision?
  • Image Understanding (AI, behavior)
  • A sensor modality for robotics
  • Computer emulation of human vision
  • Inverse of Computer Graphics

5
Intersection of Vision and Graphics
6
Computer Vision TruccoVerri98
7
Image-Based Modeling
8
Related disciplines
  • Image Processing
  • Scientific / medical imaging
  • Pattern Recognition
  • Computer Graphics
  • Learning
  • Artificial Intelligence
  • Visual Neuroscience
  • Applied Mathematics

9
Mathematics
  • What kinds of mathematics are used?
  • Signal and image processing
  • Euclidean and projective geometry
  • Vector calculus
  • Partial differential equations
  • Optimization
  • Probabilistic estimation

10
Syllabus
  • What we will be covering in this course

11
Syllabus
  • Image Transforms / Representations
  • filters, pyramids, steerable filters
  • warping and resampling
  • blending
  • image statistics, denoising/coding
  • edge and feature detection

12
Image Pyramid
Lowpass Images
  • Bandpass Images

13
Pyramid Blending
14
Parametric (global) warping
  • Examples of parametric warps

aspect
rotation
translation
perspective
cylindrical
affine
15
Syllabus
  • Optical Flow
  • least squares regression
  • iterative, coarse-to-fine
  • parametric
  • robust flow and mixture models
  • layers, EM

16
Image Morphing
17
Syllabus
  • Projective geometry
  • points, lines, planes, transforms
  • Camera calibration and pose
  • point matching and tracking
  • lens distortion
  • Image registration
  • mosaics

18
Panoramic Mosaics

19
Syllabus
  • 3D structure from motion
  • two frame techniques
  • factorization of shape and motion
  • bundle adjustment

20
3D Shape Reconstruction
Debevec, Taylor, and Malik, SIGGRAPH 1996
21
Face Modeling
22
Syllabus
  • Stereo
  • correspondence
  • local methods
  • global optimization

23
View Morphing
  • Morph between pair of images using epipolar
    geometry Seitz Dyer, SIGGRAPH96

24
Z-keying mix live and synthetic
  • Takeo Kanade, CMU (Stereo Machine)

25
Virtualized RealityTM
  • Takeo Kanade, CMU
  • collect video from 50 stream
  • reconstruct 3D model sequenceshttp//www.cs
    .cmu.edu/afs/cs/project/VirtualizedR/www/Virtualiz
    edR.html

26
Virtualized RealityTM
  • Takeo Kanade, CMU
  • generate new video
  • steerable version used for SuperBowl XXVeye
    vision system

27
Syllabus
  • Tracking
  • eigen-tracking
  • on-line EM
  • Kalman filter
  • particle filtering
  • appearance models

28
Syllabus
  • Recognition
  • subspaces and local invariance features
  • face recognition
  • color histograms
  • textures
  • Image editing
  • segmentation
  • curve tracking

29
Edge detection and editing
Elder, J. H. and R. M. Goldberg. "Image Editing
in the Contour Domain," Proc. IEEE Computer
Vision and Pattern Recognition, pp. 374-381,
June, 1998.
30
Image Enhancement
  • High dynamic range photographyDebevec et
    al.97 Mitsunaga Nayar99
  • combine several different exposures together

31
Syllabus
  • Image-based rendering
  • Lightfields and Lumigraphs
  • concentric mosaics
  • layered models
  • video-based rendering

32
Concentric Mosaics
  • Interpolate between several panoramas to give a
    3D depth effect
  • Shum He, SIGGRAPH99

33
Applications
  • Geometric reconstruction modeling, forensics,
    special effects (ILM, RealVis,2D3)
  • Image and video editing (Avid, Adobe)
  • Webcasting and Indexing Digital Video (Virage)
  • Scientific / medical applications (GE)

34
Applications
  • Tracking and surveillance (Sarnoff)
  • Fingerprint recognition (Digital Persona)
  • Biometrics / iris scans (Iridian Technologies)
  • Vehicle safety (MobilEye)
  • Drowning people (VisionIQ Inc)
  • Optical motion capture (Vicon)

35
Projects
  • Lets look at what students have done in previous
    years
  • Stanford  http//www.stanford.edu/class/cs223b/win
    ter01-02/projects.html
  • CMU  http//www-2.cs.cmu.edu/ph/869/www/869.html
  • UW http//www.cs.washington.edu/education/courses/
    cse590ss/01wi/
  • GA Tech  http//www.cc.gatech.edu/classes/AY2002/c
    s4480_spring/
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