Affiliations: School of Automation, Xi’an University of Posts and Telecommunications, Xi’an, Shaanxi, China
Correspondence:
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Corresponding author: Hong Zhang, School of Automation, Xi’an University of Posts and Telecommunications, Xi’an, Shaanxi, China. E-mail: [email protected].
Abstract: In image thresholding segmentation, gray level of pixels is the basic element to describe images. Besides, the gradient information of pixels is also a key feature to represent image space distribution. Therefore, the co-occurrence probability of gray and gradient of pixels is an effective information to describe image. In this paper, gray-gradient asymmetrical co-occurrence matrix is constructed, uniformity probability of image region is produced, and a minimum square distance criterion function based on gray-gradient co-occurrence matrix is proposed to measure the deviation between original and binary images. Comparing with gray-gray asymmetrical co-occurrence matrix and relative entropy-based symmetrical co-occurrence matrix method, the proposed method can obtain more complete segmentation results, especially for small-size object extraction. The peak signal to noise ratio probability also shows the better segmentation performance of our proposed method.
