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ISBI
2004
IEEE

Sequential vs Simultaneous Stochastic Segmentation

12 years 10 months ago
Sequential vs Simultaneous Stochastic Segmentation
In past work, the Metropolis Algorithm along with Gibbs Priors was used to successfully segment two-dimensional noisy gray images into a small finite number of labels. In applications where a clean signal is to be extracted from a noisy signal in real-time, the need for sequential segmentation arises. Here, we examine the success of using a column-sequential segmentation algorithm using the Metropolis Algorithm with Gibbs priors, and compare it to the column-simultaneous algorithm. What makes columnsequential algorithms harder than column-simultaneous algorithms is the lack of knowledge of pixel values to the right of the current column. Despite this difficulty, the column-sequential algorithm proposed here does relatively well. We conclude the paper with a discussion of methodologies that might further improve the quality of the column-sequential segmentation algorithms.
Eilat Vardi-Gonen, Gabor T. Herman
Added 20 Nov 2009
Updated 20 Nov 2009
Type Conference
Year 2004
Where ISBI
Authors Eilat Vardi-Gonen, Gabor T. Herman
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