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Indexed by:期刊论文

Date of Publication:2018-01-01

Journal:COMMUNICATIONS IN STATISTICS-THEORY AND METHODS

Included Journals:SCIE、Scopus

Volume:47

Issue:17

Page Number:4215-4228

ISSN No.:0361-0926

Key Words:Bayesian information criterion; image segmentation; model selection consistency; noisy image; robust estimation

Abstract:Image segmentation plays an important role in image processing before image recognition or compression. Many segmentation solutions follow the information theoretic criteria and often have excellent results; however, they are not robust to reduce the noise effect in contaminated image data. To guarantee the optimal segmentation with possible noise, a robust Bayesian information criterion is proposed to segment a grayscale image and it is less sensitive to noise. The asymptotic properties are also studied. Monte Carlo numerical experiments along with a brain magnetic resonance image are conducted to evaluate the performance of the new method.