An efficient method for segmentation of images based on fractional calculus and natural selection
► Two new methods for segmentation of images based on DPSO and FODPSO were proposed. ► Those were used to overcome the disadvantages of other evolutionary methods. ► FODPSO is able to find better thresholds with more stability in less CPU time. Image segmentation has been widely used in document ima...
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| Published in | Expert systems with applications Vol. 39; no. 16; pp. 12407 - 12417 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
15.11.2012
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| Subjects | |
| Online Access | Get full text |
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| Abstract | ► Two new methods for segmentation of images based on DPSO and FODPSO were proposed. ► Those were used to overcome the disadvantages of other evolutionary methods. ► FODPSO is able to find better thresholds with more stability in less CPU time.
Image segmentation has been widely used in document image analysis for extraction of printed characters, map processing in order to find lines, legends, and characters, topological features extraction for extraction of geographical information, and quality inspection of materials where defective parts must be delineated among many other applications. In image analysis, the efficient segmentation of images into meaningful objects is important for classification and object recognition. This paper presents two novel methods for segmentation of images based on the Fractional-Order Darwinian Particle Swarm Optimization (FODPSO) and Darwinian Particle Swarm Optimization (DPSO) for determining the n-1 optimal n-level threshold on a given image. The efficiency of the proposed methods is compared with other well-known thresholding segmentation methods. Experimental results show that the proposed methods perform better than other methods when considering a number of different measures. |
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| AbstractList | Image segmentation has been widely used in document image analysis for extraction of printed characters, map processing in order to find lines, legends, and characters, topological features extraction for extraction of geographical information, and quality inspection of materials where defective parts must be delineated among many other applications. In image analysis, the efficient segmentation of images into meaningful objects is important for classification and object recognition. This paper presents two novel methods for segmentation of images based on the Fractional-Order Darwinian Particle Swarm Optimization (FODPSO) and Darwinian Particle Swarm Optimization (DPSO) for determining the n-1 optimal n-level threshold on a given image. The efficiency of the proposed methods is compared with other well-known thresholding segmentation methods. Experimental results show that the proposed methods perform better than other methods when considering a number of different measures. ► Two new methods for segmentation of images based on DPSO and FODPSO were proposed. ► Those were used to overcome the disadvantages of other evolutionary methods. ► FODPSO is able to find better thresholds with more stability in less CPU time. Image segmentation has been widely used in document image analysis for extraction of printed characters, map processing in order to find lines, legends, and characters, topological features extraction for extraction of geographical information, and quality inspection of materials where defective parts must be delineated among many other applications. In image analysis, the efficient segmentation of images into meaningful objects is important for classification and object recognition. This paper presents two novel methods for segmentation of images based on the Fractional-Order Darwinian Particle Swarm Optimization (FODPSO) and Darwinian Particle Swarm Optimization (DPSO) for determining the n-1 optimal n-level threshold on a given image. The efficiency of the proposed methods is compared with other well-known thresholding segmentation methods. Experimental results show that the proposed methods perform better than other methods when considering a number of different measures. |
| Author | Ghamisi, Pedram Couceiro, Micael S. Ferreira, Nuno M.F. Benediktsson, Jón Atli |
| Author_xml | – sequence: 1 givenname: Pedram surname: Ghamisi fullname: Ghamisi, Pedram email: p.ghamisi@gmail.com organization: Geodesy & Geomatics Engineering Faculty, K. N. Toosi University of Technology, Tehran, Iran – sequence: 2 givenname: Micael S. surname: Couceiro fullname: Couceiro, Micael S. email: micaelcouceiro@isr.uc.pt, micael@isec.pt organization: Institute of Systems and Robotics, University of Coimbra, Pólo II, 3030-290 Coimbra, Portugal – sequence: 3 givenname: Jón Atli surname: Benediktsson fullname: Benediktsson, Jón Atli email: benedikt@hi.is organization: Faculty of Electrical and Computer Engineering, University of Iceland, Saemundargotu 2, 101 Reykjavik, Iceland – sequence: 4 givenname: Nuno M.F. surname: Ferreira fullname: Ferreira, Nuno M.F. email: nunomig@isec.pt organization: RoboCorp at the Electrical Engineering Department, Engineering Institute of Coimbra, Rua Pedro Nunes – Quinta da Nora, 3030-199 Coimbra, Portugal |
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