segmentation

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Image Segmentation and Graph Cuts• Image Segmentation• Graph Cuts
he Pipeline• Input: Image• Output: Segments• Each iteration cuts into 2 piecesYesAssignW(i,j)Solve forminimumpenaltyCut into 2Subdivide?Subdivide?NoNoYes
Page 1 and 2:
Segmentácia farebnéhoobrazu
Page 3 and 4:
Image segmentation
Page 5 and 6: Complete vs. partialsegmentationCom
Page 7 and 8: Gestalt (celostné) laws ofperceptu
Page 9 and 10: Gestalt laws• A series of factors
Page 11 and 12: Gestalt laws
Page 13 and 14: Consequence:Groupings by Invisible
Page 15 and 16: Image segmentationSo, all we have t
Page 17 and 18: Histogram-based segmentation• How
Page 19 and 20: 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: • Result : IsolationGraph Cut
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 and 72: Markov Random Fields (MRF)• A gra
Page 73 and 74: DefinitionMRF Components:• Pr(f i
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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