Ch 10 Image Segmentation Ideally, partition an image into regions corresponding to real world objects. CSE 803 Fall 2012.

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Presentation transcript:

Ch 10 Image Segmentation Ideally, partition an image into regions corresponding to real world objects. CSE 803 Fall 2012

Goals of segmentation CSE 803 Fall 2012

Segments formed by K-means CSE 803 Fall 2012

Segmentation attempted via contour/boundary detection CSE 803 Fall 2012

Clustering versus region-growing CSE 803 Fall 2012

Clustering versus region-growing CSE 803 Fall 2012

K-means clustering as before: vectors can contain color+texture CSE 803 Fall 2012

K-means CSE 803 Fall 2012

Histograms can show modes CSE 803 Fall 2012

Otsu’s method assumes K=2 Otsu’s method assumes K=2. It searches for the threshold t that optimizes the intra class variance. CSE 803 Fall 2012

Ohlander bifurcated the histogram recursively CSE 803 Fall 2012

Recursive histogram-controlled segmentation CSE 803 Fall 2012

URL’s of other work Segmentation lecture http://robots.stanford.edu/cs223b/index.html Tutorial on graph cut method and assessment of segmentation http://www.cis.upenn.edu/~jshi/GraphTutorial/ CSE 803 Fall 2012

Segmentation via region-growing (aggregation) Pixels, or patches, at the lowest level are combined when similar in a hierarchical fashion CSE 803 Fall 2012

Decision: combine neighbors? Neighboring pixel or region CSE 803 Fall 2012

Aggregation decision CSE 803 Fall 2012

Representation of regions CSE 803 Fall 2012

Chain codes for boundaries CSE 803 Fall 2012

Quad trees divide into quadrants M=mixed; E=empty; F=full CSE 803 Fall 2012

Can segment 3D images also Oct trees subdivide into 8 octants Same coding: M, E, F used Software available for doing 3D image processing and differential equations using octree representation. Can achieve large compression factor. CSE 803 Fall 2012

More URLs of other work Mean shift description http://cmp.felk.cvut.cz/cmp/courses/ZS1/slidy/meanShiftSeg.pdf CSE 803 Fall 2012