Color images segmentation using scale space filter and Markov random field

Tai-Yuen Cheng; Chang-Lin Huang

doi:10.1117/12.57072

1 February 1992 Color images segmentation using scale space filter and Markov random field

Tai-Yuen Cheng, Chang-Lin Huang

Proceedings Volume 1607, Intelligent Robots and Computer Vision X: Algorithms and Techniques; (1992) https://doi.org/10.1117/12.57072
Event: Robotics '91, 1991, Boston, MA, United States

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.

Citation Download Citation

Tai-Yuen Cheng and Chang-Lin Huang "Color images segmentation using scale space filter and Markov random field", Proc. SPIE 1607, Intelligent Robots and Computer Vision X: Algorithms and Techniques, (1 February 1992); https://doi.org/10.1117/12.57072

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