Int J Performability Eng ›› 2017, Vol. 13 ›› Issue (7): 1132-1139.doi: 10.23940/ijpe.17.07.p16.11321139

• Original articles • Previous Articles     Next Articles

Image Objects Segmentation and Tracking based on Genetic Algorithm Optimized Local Level Set Method with Shape Prior

Aixia Wang, Jingjiao Li*, Zhenni Li, and Aiyun Yan   

  1. College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China

Abstract: The shape prior based level set method is widely used to segment and track objects’ contours in images and video sequences. However, such type of method is very slow and easy to fall into local minimum and obtain a wrong matching result. To overcome these issues, this paper presents a novel dynamic local level set method with shape prior. To speed up the local level set method, a genetic algorithm is used to dynamically choose the local region. Secondly, the genetic algorithm is also used to help the evolution process jump out of the local optimum when embedding shape prior into the level set function. The main the main strategy is using genetic algorithm to pre-choose shape priors and estimate the parameters. The experimental results prove the effectiveness and efficiency of the proposed method.

Submitted on September 15, 2017; Revised on October 20, 2017; Accepted on October 27, 2017
References: 11