Stochastic segmentation of severely degraded images using Gibbs random fields

This paper deals with segmentation of noisy images using Gibbs random field (GRF) with an emphasis on modeling of the region process. For noisy image segmentation using the multi-level logistic (MLL) model with the second-order neighborhood system, which is commonly used in image processing, the segmentation performance is degraded significantly in case of low signal to noise ratio. By comparison with the Ising model that explains the magnetic properties of ferromagnetic material, it is evident that the characteristics of the region process modeled using the MLL model with the second-order neighborhood system are different in nature from the expected characteristics of a region. To solve this problem we added the term of the magnetic energy associated with the magnetic field of a spin system (or image) to the energy function of GRF. Using the modified model for the region process, the result of image segmentation was improved and did not depend on the cooling schedule in simulated annealing.
Publisher
OPTICAL SOC JAPAN
Issue Date
1996
Language
ENG
Keywords

MARKOV RANDOM-FIELDS; TEXTURED IMAGES; UNSUPERVISED SEGMENTATION; PARAMETER-ESTIMATION; STATISTICAL-ANALYSIS; MODELS; NOISY; DISTRIBUTIONS; RESTORATION; RELAXATION

Citation

OPTICAL REVIEW, v.3, no.3, pp.184 - 191

ISSN
1340-6000
URI
http://hdl.handle.net/10203/75019
Appears in Collection
CS-Journal Papers(저널논문)
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