Object Boundary Detection Using Active Contour Model via Multiswarm PSO with Fuzzy-Rule Based Adaptation of Inertia Factor
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, ap...
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| Format: | Article |
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Wiley
2016-01-01
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| Series: | Advances in Fuzzy Systems |
| Online Access: | http://dx.doi.org/10.1155/2016/6179576 |
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| author | Ajay Khunteta D. Ghosh |
| author_facet | Ajay Khunteta D. Ghosh |
| author_sort | Ajay Khunteta |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-66bfcca5f778439c84a6fd4f98392baf |
| institution | Kabale University |
| issn | 1687-7101 1687-711X |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Fuzzy Systems |
| spelling | doaj-art-66bfcca5f778439c84a6fd4f98392baf2025-02-03T05:51:25ZengWileyAdvances in Fuzzy Systems1687-71011687-711X2016-01-01201610.1155/2016/61795766179576Object Boundary Detection Using Active Contour Model via Multiswarm PSO with Fuzzy-Rule Based Adaptation of Inertia FactorAjay Khunteta0D. Ghosh1Department of Electronics & Communication Engineering, Indian Institute of Technology Roorkee, Uttarakhand 247667, IndiaDepartment of Electronics & Communication Engineering, Indian Institute of Technology Roorkee, Uttarakhand 247667, IndiaActive 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.http://dx.doi.org/10.1155/2016/6179576 |
| spellingShingle | Ajay Khunteta D. Ghosh Object Boundary Detection Using Active Contour Model via Multiswarm PSO with Fuzzy-Rule Based Adaptation of Inertia Factor Advances in Fuzzy Systems |
| title | Object Boundary Detection Using Active Contour Model via Multiswarm PSO with Fuzzy-Rule Based Adaptation of Inertia Factor |
| title_full | Object Boundary Detection Using Active Contour Model via Multiswarm PSO with Fuzzy-Rule Based Adaptation of Inertia Factor |
| title_fullStr | Object Boundary Detection Using Active Contour Model via Multiswarm PSO with Fuzzy-Rule Based Adaptation of Inertia Factor |
| title_full_unstemmed | Object Boundary Detection Using Active Contour Model via Multiswarm PSO with Fuzzy-Rule Based Adaptation of Inertia Factor |
| title_short | Object Boundary Detection Using Active Contour Model via Multiswarm PSO with Fuzzy-Rule Based Adaptation of Inertia Factor |
| title_sort | object boundary detection using active contour model via multiswarm pso with fuzzy rule based adaptation of inertia factor |
| url | http://dx.doi.org/10.1155/2016/6179576 |
| work_keys_str_mv | AT ajaykhunteta objectboundarydetectionusingactivecontourmodelviamultiswarmpsowithfuzzyrulebasedadaptationofinertiafactor AT dghosh objectboundarydetectionusingactivecontourmodelviamultiswarmpsowithfuzzyrulebasedadaptationofinertiafactor |