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CS 496: Computer Vision

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Title: CS 496: Computer Vision


1
CS 496 Computer Vision
Thanks to Chris Bregler
2
CS 496 Computer Vision
  • Personnel
  • Instructor Szymon Rusinkiewicz smr_at_cs.princeton
    .edu
  • TA Wagner Corrêa wtcorrea_at_cs.princeton.edu
  • Email to both cs496_at_princeton.edu
  • Course web page http//www.cs.princeton.edu/cour
    ses/cs496/

3
What is Computer Vision?
  • Input images or video
  • Output description of the world

4
What is Computer Vision?
  • Input images or video
  • Output description of the world
  • Many levels of description

5
Low-Level or Early Vision
  • Considers local properties of an image

Theres an edge!
6
Mid-Level Vision
  • Grouping and segmentation

Theres an object and a background!
7
High-Level Vision
  • Recognition

Its a chair!
8
Big Question 1 Who Cares?
  • Applications of computer vision
  • In AI vision serves as the input stage
  • In medicine understanding human vision
  • In engineering model extraction

9
Vision and Other Fields
Computer Vision
10
Big Question 2 Does It Work?
  • Situation much the same as AI
  • Some fundamental algorithms
  • Large collection of hacks / heuristics
  • Vision is hard!
  • Especially at high level, physiology unknown
  • Requires integrating many different methods
  • Requires reasoning and understandingAI
    completeness

11
Computer and Human Vision
  • Emulating effects of human vision
  • Understanding physiology of human vision

12
Image Formation
  • Human lens forms image on retina,sensors (rods
    and cones) respond to light
  • Computer lens system forms image,sensors (CCD,
    CMOS) respond to light

13
Low-Level Vision
Hubel
14
Low-Level Vision
  • Retinal ganglion cells
  • Lateral Geniculate Nucleus function unknown
    (visual adaptation?)
  • Primary Visual Cortex
  • Simple cells orientational sensitivity
  • Complex cells directional sensitivity
  • Further processing
  • Temporal cortex what is the object?
  • Parietal cortex where is the object? How do I
    get it?

15
Low-Level Vision
  • Net effect low-level human visioncan be
    (partially) modeled as a set ofmultiresolution,
    oriented filters

16
Low-Level Depth Cues
  • Focus
  • Vergence
  • Stereo
  • Not as important as popularly believed

17
Low-Level Computer Vision
  • Filters and filter banks
  • Implemented via convolution
  • Detection of edges, corners, and other local
    features
  • Can include multiple orientations
  • Can include multiple scales filter pyramids
  • Applications
  • First stage of segmentation
  • Texture recognition / classification
  • Texture synthesis

18
Texture Analysis / Synthesis
Multiresolution Oriented Filter Bank
OriginalImage
Image Pyramid
19
Texture Analysis / Synthesis
Original Texture
Synthesized Texture
Heeger and Bergen
20
Low-Level Computer Vision
  • Optical flow
  • Detecting frame-to-frame motion
  • Local operator looking for gradients
  • Applications
  • First stage of tracking

21
Optical Flow
Image 1
Optical FlowField
Image 2
22
Low-Level Computer Vision
  • Shape from X
  • Stereo
  • Motion
  • Shading
  • Texture foreshortening

23
3D Reconstruction
24
Mid-Level Vision
  • Physiology unclear
  • Observations by Gestalt psychologists
  • Proximity
  • Similarity
  • Common fate
  • Common region
  • Parallelism
  • Closure
  • Symmetry
  • Continuity
  • Familiar configuration

Wertheimer
25
Grouping Cues
26
Grouping Cues
27
Grouping Cues
28
Grouping Cues
29
Mid-Level Computer Vision
  • Techniques
  • Clustering based on similarity
  • Limited work on other principles
  • Applications
  • Segmentation / grouping
  • Tracking

30
Snakes Active Contours

Contour Evolution forSegmenting an Artery
31
Histograms
Birchfeld
32
Expectation Maximization (EM)
Color Segmentation
33
Bayesian Methods
  • Prior probability
  • Expected distribution of models
  • Conditional probability P(AB)
  • Probability of observation Agiven model B

34
Bayesian Methods
  • Prior probability
  • Expected distribution of models
  • Conditional probability P(AB)
  • Probability of observation Agiven model B
  • Bayess Rule P(BA) P(AB) ? P(B) / P(A)
  • Probability of model B given observation A

Thomas Bayes (c. 1702-1761)
35
Bayesian Methods
black pixels
black pixels
36
High-Level Vision
  • Human mechanisms ???

37
High-Level Vision
  • Computational mechanisms
  • Bayesian networks
  • Templates
  • Linear subspace methods
  • Kinematic models

38
Template-Based Methods
Cootes et al.
39
Linear Subspaces
40
Principal Components Analysis (PCA)
Data
New Basis Vectors
PCA
Kirby et al.
41
Kinematic Models
  • Optical Flow/Feature tracking no constraints
  • Layered Motion rigid constraints
  • Articulated kinematic chain constraints
  • Nonrigid implicit / learned constraints

42
Real-world Applications
Osuna et al

43
Real-world Applications
Osuna et al

44
Course Outline
  • Image formation and capture
  • Filtering and feature detection
  • Optical flow and tracking
  • Projective geometry
  • Shape from X
  • Segmentation and clustering
  • Recognition
  • Applications 3D scanning image-based rendering

45
3D Scanning
46
Image-Based Modeling and Rendering
Debevec et al.
Manex
47
Course Mechanics
  • 60 4 written / programming assignments
  • 30 Final group project
  • 10 In-class participation (includes attendance,
    project presentation, etc.)

48
Course Mechanics
  • Book Computer Vision A Modern ApproachDavid
    Forsyth and Jean Ponce
  • Papers
  • All online available from class webpage

49
CS 496 Computer Vision
  • Personnel
  • Instructor Szymon Rusinkiewicz smr_at_cs.princeton
    .edu
  • TA Wagner Corrêa wtcorrea_at_cs.princeton.edu
  • Email to both cs496_at_princeton.edu
  • Course web page http//www.cs.princeton.edu/cour
    ses/cs496/
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