Location: EU Supported » PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning » Machine Learning Summer School 2009 - Cambridge

Computer Vision

author:Andrew Blake, Microsoft Research
published: Nov. 2, 2009,   recorded: September 2009,   views: 366
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Slides

Slides
0:00 Markov Models in Computer Vision
1:21 Seeing as an intelligent behaviour
14:17 An account of how vision might work - 1
15:20 Machines that see
18:00 “Particles” for head location
19:06 Tracking against clutter 2
24:37 Visual ambiguity in the location of a head
26:10 What is hard about vision --ambiguity
27:30 ... more ambiguity
28:30 Fusion of cues: sound and visual appearance
31:38 Learning by example
35:21 An account of how vision might work - 2
35:39 Segmentation (Berkeley)
39:07 Better defined problem: foreground segmentation
45:29 Mixture of Gaussian colour priors
46:09 Segmentation in camouflage
46:30 Need for spatial priors
47:00 Connecting up pixels
51:58 1st order with stochastic observations –Hidden Markov Model
54:14 Temporal HMMs in vision
55:33 2D Markov model?
63:09 2D Markov Model 1st order Example
65:39 2D Markov simulation (Swensden Wang MCMC)
66:45 2D Hidden Markov Model
72:40 Simple segmentation ---Ising prior
76:41 Segmentation artefacts ---Ising prior

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