Active contours with a novel distribution metric for complex object segmentation

In this paper, we present the efficient region-based active contours with a novel distribution metric for complex object segmentation problems. Unlike most conventional approaches, we model the regional statistics using probability distribution function and propose a simple but effective distribution metric to drive the active contours. Subsequently, the proposed approach speeds up the segmentation process without initializing the zero level set in terms of a sign distance function (SDF) and re-initializing it periodically during the evolution as used in the traditional methods. Some challenging synthetic and real-world images are utilized to evaluate the proposed segmentation algorithm. The experiments show its promising result in comparison with the existing methods.