Paper Info
Title
Object Boundary Detection Using Active Contour Model via Multiswarm PSO with Fuzzy-Rule Based Adaptation of Inertia Factor.
Abstract
Active contour models, colloquially known as snakes, are quite popular for several applications such as object boundary detection, image segmentation, object tracking, and classification via energy minimization. While energy minimization may be accomplished using traditional optimization methods, approaches based on nature-inspired evolutionary algorithms have been developed in recent years. One such evolutionary algorithm that has been used extensively in active contours is the particle swarm optimization PSO. However, conventional PSO converges slowly and gets trapped in local minimum easily which results in inaccurate detection of concavities in the object boundary. This is taken care of by using proposed multiswarm PSO in which a swarm is set for every control point in the snake and then all the swarms search for their best points simultaneously through information sharing among them. The performance of the multiswarm PSO-based search process is further enhanced by using dynamic adaptation of the inertia factor. In this paper, we propose using a set of fuzzy rules to adjust the inertia weight on the basis of the current normalized snake energy and the current value of inertia. Experimental results demonstrate the effectiveness of the proposed method compared to conventional approaches.
Year
DOI
Venue
2016
10.1155/2016/6179576
Adv. Fuzzy Systems
Field
DocType
Volume
Active contour model,Particle swarm optimization,Mathematical optimization,Evolutionary algorithm,Swarm behaviour,Fuzzy logic,Image segmentation,Video tracking,Artificial intelligence,Mathematics,Machine learning,Fuzzy rule
Journal
2016
ISSN
Citations 
PageRank 
1687-7101
0
0.34
References 
Authors
21
2
Name
Order
Citations
PageRank
Ajay Khunteta100.68
Debashis Ghosh249649.16