segmentation

More documents

Recommendations

Info

Using MRFs• How to model different problems?• Given observations y, and the parameters of the MRF, how to infer the hiddenvariables, x?• How to learn the parameters of the MRF?
Modeling image pixel labels as MRFMRF-based <strong>segmentation</strong>real image( x, y )ii1label image( x, x )ijSlides by R. Huang – Rutgers University
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 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: Probabilistic interpretation• The
Page 81 and 82: Model joint probability*( x ,*) arg
Page 83 and 84: Applying and learning MRFMRF-based
show all

segmentation

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?