Release Time:2019-03-12  Hits:

Indexed by: Journal Article

Date of Publication: 2018-01-01

Journal: COMMUNICATIONS IN STATISTICS-THEORY AND METHODS

Included Journals: Scopus、SCIE

Volume: 47

Issue: 17

Page Number: 4215-4228

ISSN: 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.