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

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Presentation on theme: "Ch 10 Image Segmentation Ideally, partition an image into regions corresponding to real world objects. CSE 803 Fall 2012."— Presentation transcript:

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

2 Goals of segmentation CSE 803 Fall 2012

3 Segments formed by K-means
CSE 803 Fall 2012

4 Segmentation attempted via contour/boundary detection
CSE 803 Fall 2012

5 Clustering versus region-growing
CSE 803 Fall 2012

6 Clustering versus region-growing
CSE 803 Fall 2012

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

8 K-means CSE 803 Fall 2012

9 Histograms can show modes
CSE 803 Fall 2012

10 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

11 Ohlander bifurcated the histogram recursively
CSE 803 Fall 2012

12 Recursive histogram-controlled segmentation
CSE 803 Fall 2012

13 URL’s of other work Segmentation lecture Tutorial on graph cut method and assessment of segmentation CSE 803 Fall 2012

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

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

16 Aggregation decision CSE 803 Fall 2012

17 Representation of regions
CSE 803 Fall 2012

18 Chain codes for boundaries
CSE 803 Fall 2012

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

20 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

21 More URLs of other work Mean shift description CSE 803 Fall 2012

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