New ACO algorithm for image edge detection

Ant Colony Optimization (ACO) is a metaheuristic based on real behavior of ants to solve optimization problems. Edge detection plays an important role in image processing. It consists in detecting edges or contours in images which allows to extract information from them. An ACO algorithm to edge det...

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Autor principal: Martínez, C.A
Otros Autores: Buemi, M.E
Formato: Acta de conferencia Capítulo de libro
Lenguaje:Inglés
Publicado: Institution of Engineering and Technology 2014
Acceso en línea:Registro en Scopus
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245 1 0 |a New ACO algorithm for image edge detection 
260 |b Institution of Engineering and Technology  |c 2014 
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504 |a Ahmad, M., Choi, T., Local threshold and boolean function based edge detection (1999) IEEE Transactions on Consumer Electronics, 45 (3), pp. 674-679. , Aug 
504 |a Baterina, A., Oppus, C., Image edge detection using ant colony optimization (2010) WSEAS Transactions on Signal Processing, 6 (2), pp. 58-67. , Apr 
504 |a Canny, J., A computational approach to edge detection (1986) IEEE Transactions on Pattern Analysis and Machine Intelligence, 8 (6), pp. 679-698. , Jun 
504 |a Djemame, S., Batouche, M., Combining cellular automata and particle swarm optimization for edge detection (2012) International Journal of Computer Applications, 57 (14), pp. 16-22. , Nov 
504 |a Dorigo, M., Stützle, T., (2004) Ant Colony Optimization, , London, England, MIT Press 
504 |a Etemad, S., White, T., Ant ant-inspired algorithm for detection of image edge features (2011) Applied Soft Computing, 11, pp. 4883-4893. , Jun 
504 |a Glover, F., Laguna, M., (1998) Tabu Search, , Boston, USA, Kluwer Academic Publishers 
504 |a Gonzalez, R., Woods, R., (2008) Digital Image Processing, , (3rd Edition), NJ, USA, Prentice-Hall 
504 |a Jain, A., (1989) Fundamentals of Digital Image Processing, , NJ, USA, Prentice-Hall 
504 |a Jevtić, A., Andina, D., Adaptive artificial ant colonies for edge detection in digital images (2010) 36th Annual Conference on IEEE Industrial Electronics Society, Glendale, USA, pp. 2813-2816. , Nov 
504 |a Li, Y., Bai, B., Zhang, Y., An adaptive inmune genetic algorithm for edge detecion (2007) Proceedings of the 3rd International Conference on Intelligent Computing, pp. 565-571. , Qingdao, China, Aug 
504 |a Lim, J., (1990) Two-Dimensional Signal and Image Processing, , NJ, USA, Prentice-Hall 
504 |a Manish, T., Murugan, D., Ganesh Kumar, T., Edge detection by combined canny filter with scale multiplication & ant colony optimization (2012) Proceedings of the 2nd International Conference on Computational Science, Engineering and Information Technology, pp. 497-500. , Coimbatore, India, Oct 
504 |a Mills, P., Tsang, E., Zhang, Q., Ford, J., (2004) A Survey of AI-Based Meta-Heuristics for Dealing with Local Optima in Local Search, pp. 1-47. , Technical Report, CSM-416, University of Essex 
504 |a Nadernejad, E., Sharifzadeh, S., Edge detection techniques: Evaluations and comparisons (2008) Applied Mathematical Sciences, 2 (31), pp. 1507-1520 
504 |a Neupane, B., Aung, Z., Woon, W., A new image edge detection method using quality-based clustering (2012) Proceedings of the 10th LASTED International Conference on Visualization, Imaging and Image Processing, pp. 20-26. , Banff, Canada, Jul 
504 |a Tian, J., Yu, W., Xie, S., An ant colony optimization algorithm for image edge detection (2008) IEEE Congress on Evolutionary Computation, pp. 751-756. , Hong Kong, China, Jun 
504 |a Yigitbasi, E., Baykan, N., Edge detection using artificial bee colony algorithm (abc) (2013) International Journal of Information and Electronics Engineering, 3 (6), pp. 634-638. , NovA4 - 
520 3 |a Ant Colony Optimization (ACO) is a metaheuristic based on real behavior of ants to solve optimization problems. Edge detection plays an important role in image processing. It consists in detecting edges or contours in images which allows to extract information from them. An ACO algorithm to edge detection is proposed. Using heuristic and knowledge information and applying an improvement operator, binary images including edges detected are obtained. The algorithm was tested with several images (real and synthetic, with and without presence of noise). The results reached were competitive in terms of quality of images and required computational time. © The Institution of Engineering and Technology 2014.  |l eng 
593 |a Departamento de Informática, Universidad Nacional de Salta, Salta, Argentina 
593 |a Departamento de Computación, Universidad de Buenos Aires, Buenos Aires, Argentina 
690 1 0 |a ALGORITHMS 
690 1 0 |a ANT COLONY OPTIMIZATION 
690 1 0 |a ARTIFICIAL INTELLIGENCE 
690 1 0 |a IMAGE PROCESSING 
690 1 0 |a OPTIMIZATION 
690 1 0 |a PATTERN RECOGNITION 
690 1 0 |a ACO ALGORITHMS 
690 1 0 |a ANT COLONY OPTIMIZATION (ACO) 
690 1 0 |a COMPUTATIONAL TIME 
690 1 0 |a EXTRACT INFORMATIONS 
690 1 0 |a IMAGE EDGE DETECTION 
690 1 0 |a KNOWLEDGE INFORMATION 
690 1 0 |a METAHEURISTIC 
690 1 0 |a OPTIMIZATION PROBLEMS 
690 1 0 |a EDGE DETECTION 
700 1 |a Buemi, M.E. 
711 2 |d 10 November 2014 through 14 November 2014  |g Código de la conferencia: 115540 
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