Multi-object segmentation using probabilistic labeling

This paper presents an improved multi-object segmentation algorithm. 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, from which a set of probabilistic values for each pixel are calculated and the maximum is used to label the pixel accordingly. The only unique parameter that dominantly affects the segmentation performance is characterized, and the result of which is used to derive a formula that adjusts the value of the unique parameter according to intensity difference between neighboring pixels. 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.