Title: Shadow regions detection algorithm by adaptive narrowband two-phase Chan-Vese model
Author: WANG Xingmei; YIN Guisheng; LIU Guangyu; LIU Zhipeng; WANG Xiaowei
Affiliation: College of Computer Science and Technology, Harbin Engineering University et al.
Abstract: An adaptive narrowband two-phase Chan-Vese (ANBCV) model is proposed for improving the shadow regions detection performance of sonar images. In the first noise smoothing step, the anisotropic second-order neighborhood MRF(Markov Random Field, MRF) is used to describe the image texture feature parameters. Then, initial two-class segmentation is processed with the block mode /c-means clustering algorithm, to estimate the approximate position of the shadow regions. On this basis, the zero level set function is adaptively initialized by the approximate position of shadow regions. ANBCV model is provided to complete local optimization for eliminating the image global interference and obtaining more accurate results. Experimental results show that the new algorithm can efficiently remove partial noise, increase detection speed and accuracy, and with less human intervention.