Improved Segmentation Based on Probabilistic Labeling

⎯This paper presents an improved multi-object segmentation algorithm based on probabilistic labeling. First, a critical look is focused on utilizing vector calculus operator and combinational operator to rewrite Dirichlet integral into a matrix form, and boundary condition is defined to obtain the needed harmonic function. The only unique parameter β that dominantly affects the segmentation performance is characterized. According to the result, we propose an improved parameter that changes the value of β on the basis of pixel-by-pixel, rather than the use of a fixed constant β throughout the whole image. Furthermore, a pre-process involving the use of watershed analysis is applied to smooth the effect of high frequency components in the input image, so that better noise tolerance and more accurate object contours can be obtained. Index Terms image segmentation, graph theory, Dirichlet problem, harmonic function, watershed analysis