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论文类型：期刊论文

发表时间：2018-01-01

发表刊物：COMMUNICATIONS IN STATISTICS-THEORY AND METHODS

收录刊物：SCIE、Scopus

卷号：47

期号：17

页面范围：4215-4228

ISSN号：0361-0926

关键字：Bayesian information criterion; image segmentation; model selection consistency; noisy image; robust estimation

摘要：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.