Abstract
We present a novel statistical and variational approach to image segmentation based on a new algorithm named region competition. This algorithm is derived by minimizing a generalized Bayes/MDL (Minimum Description Length) criterion using the variational principle. We show that existing techniques in early vision such as, snake/balloon models, region growing, and Bayes/MDL are addressing different aspects of the same problem and they can be unified within a common statistical framework which combines their advantages. We analyze how to optimize the precision of the resulting boundary location by studying the statistical properties of the region competition algorithm and discuss what are good initial conditions for the algorithm. Our method is generalized to color and texture segmentation and is demonstrated on grey level images, color images and texture images.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Keywords
Minimum description lengthBayes' theoremArtificial intelligenceSegmentationImage segmentationComputer sciencePattern recognition (psychology)Image (mathematics)Boundary (topology)Competition (biology)MathematicsBayesian probabilityAlgorithm
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Publication Info
- Year
- 2002
- Type
- article
- Pages
- 416-423
- Citations
- 195
- Access
- Closed
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Cite This
Shaoyang Zhu,
T.S. Lee,
Alan Yuille
(2002).
Region competition: unifying snakes, region growing, energy/Bayes/MDL for multi-band image segmentation.
, 416-423.
https://doi.org/10.1109/iccv.1995.466909
Identifiers
- DOI
- 10.1109/iccv.1995.466909