segmentation

More documents

Recommendations

Info

Clustering-based segmentation• K-means variants:• Different ways to initialize the means.• Different stopping criteria.• Dynamic methods for determining the rightnumber of clusters (K) for a given image.• Problem: histogram-based and clusteringbasedsegmentation can produce messyregions.• How can these be fixed?
Clustering-based segmentation• Expectation-Maximization (EM) algorithm can be usedas a probabilistic clustering method where each clusteris modeled using a Gaussian.• The clusters are updated iteratively by computing theparameters of the Gaussians.Example from the UC Berkeley’s Blobworld system.
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: Clustering-based segmentation• Cl
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 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
show all

segmentation

Create successful ePaper yourself

Delete template?

Save as template?