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Proceedings Paper

Color images segmentation using scale space filter and Markov random field
Author(s): Tai-Yuen Cheng; Chang-Lin Huang
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Paper Abstract

This paper presents a new hybrid method that combines the scale space filter (SSF) and Markov random field (MRF) for color image segmentation. Using the scale space filter, we separate the different scaled histogram to intervals corresponding to peaks and valleys. The basic construction of MRF is a joint probability given the original data. The original data is the image that we get from the source and the result is called the label image. Because the MRF needs the number of segments before it converges to the global minimum, we exploit the scale space filter to do coarse segmentation and then use MRF to do fine segmentation of the images. Finally, we compare the experimental results obtained from using SSF only, or combined with MRF using iterated conditional mode (ICM) and Gibbs sampling.

Paper Details

Date Published: 1 February 1992
PDF: 11 pages
Proc. SPIE 1607, Intelligent Robots and Computer Vision X: Algorithms and Techniques, (1 February 1992); doi: 10.1117/12.57072
Show Author Affiliations
Tai-Yuen Cheng, National Tsing Hua Univ. (Taiwan)
Chang-Lin Huang, National Tsing Hua Univ. (Taiwan)


Published in SPIE Proceedings Vol. 1607:
Intelligent Robots and Computer Vision X: Algorithms and Techniques
David P. Casasent, Editor(s)

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