Fast image segmentation algorithm based on region competition with level set

Abstract

An adaptive variational image segmentation model based on Bayesian and region competition was presented. Level set method was used to describe the plane curves and partitioned regions, and the energy function was obtained based on Bayesian region statistical information. Then, a new fast partial different equation for curve evolution was deduced to implement unambiguous image segmentation by region competition. The model can extract multi-class objects simultaneously with fast evolving speed and high segmentation precision. Also, it is easy to integrate other image information such as texture and shape into this model. Besides, the energy function and curve evolution equations are independent so that we can choose different probability functions to describe various types of images. The experimental results show that it is a fast, effective and novel image segmentation algorithm.