segmentation

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DefinitionMRF Components:• A set of sites: P={1,…,m} : each pixel is a site.• Neighborhood for each pixel N={N p | p P}• A set of random variables (random field), one for each site F={F p | p P} Denotes thelabel at each pixel.• Each random variable takes a value f p from the set of labels L={l 1 ,…,l k }• We have a joint event {F 1 =f 1 ,…, F m =f m } , or a configuration, abbreviated as F=f• The joint prob. Of such configuration: Pr(F=f) or Pr(f)From Slides by S. Seitz - University of WashingtonCS 534 – Stereo Imaging - 72
DefinitionMRF Components:• Pr(f i ) > 0 for all variables f i .• Markov Property: Each Random variable depends on other RVs onlythrough its neighbors. Pr(f p | f S-{p} )=Pr (f p |f Np ), p• So, we need to define a neighborhood system: N p (neighbors for site p).• No strict rules for neighborhood definition.Cliques for this neighborhoodFrom Slides by S. Seitz - University of WashingtonCS 534 – Stereo Imaging - 73
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Segmentácia farebnéhoobrazu
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Image segmentation
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Complete vs. partialsegmentationCom
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Gestalt (celostné) laws ofperceptu
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Gestalt laws• A series of factors
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Gestalt laws
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Consequence:Groupings by Invisible
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Image segmentationSo, all we have t
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Histogram-based segmentation• How
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Clustering-based segmentation• Ho
Page 21 and 22: Clustering-based segmentationK-mean
Page 23 and 24: Clustering-based segmentation• Cl
Page 25 and 26: Clustering-based segmentation• Ex
Page 27 and 28: Region growing• Region growing te
Page 29 and 30: Split-and-merge1. Start with the wh
Page 31 and 32: Split-and-merge
Page 33 and 34: Mean Shift Segmentationhttp://www.c
Page 35 and 36: Mean Shift SegmentationMean Shift S
Page 37 and 38: Mean Shift Segmentation Results:htt
Page 39 and 40: Graph Cut• First: select a region
Page 41 and 42: Graph Cut• What are graphs?Nodes
Page 43 and 44: Graph Cut• Go back to our selecte
Page 45 and 46: Graph Cut• We want highest sum of
Page 47 and 48: Graph Cut• We want highest sum of
Page 49 and 50: Graph-based segmentation• Let G =
Page 51 and 52: Graph-based segmentation• Minimal
Page 53 and 54: Graph-based segmentationA22 21 4 32
Page 55 and 56: • Result : IsolationGraph Cut
Page 57 and 58: he Pipeline• Input: Image• Outp
Page 59 and 60: Graph-based segmentation• Shi and
Page 61 and 62: Extensions: Edge Weights• How to
Page 63: Graph-based segmentation
Page 66 and 67: Data TermRForeground &BackgroundBac
Page 69 and 70: Moderately simple examples… GrabC
Page 71: Markov Random Fields (MRF)• A gra
Page 75 and 76: MRF Components:• Hammersley-Cliff
Page 77 and 78: Probabilistic interpretation• The
Page 79 and 80: Modeling image pixel labels as MRFM
Page 81 and 82: Model joint probability*( x ,*) arg
Page 83 and 84: Applying and learning MRFMRF-based
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segmentation

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