Boundary extraction through gradient-based evolutionary algorithm
Boundary extraction is an important procedure associated with recognition and interpretation tasks in digital image processing and computer vision. Most of the segmentation techniques are based on the detection of the local gradient, and then their application in noisy images is unstable and unrelia...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Postgraduate Office, School of Computer Science, Universidad Nacional de La Plata
2003-04-01
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| Series: | Journal of Computer Science and Technology |
| Subjects: | |
| Online Access: | https://journal.info.unlp.edu.ar/JCST/article/view/946 |
| _version_ | 1818827219478249472 |
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| author | Román Katz Claudio Delrieux |
| author_facet | Román Katz Claudio Delrieux |
| author_sort | Román Katz |
| collection | DOAJ |
| description | Boundary extraction is an important procedure associated with recognition and interpretation tasks in digital image processing and computer vision. Most of the segmentation techniques are based on the detection of the local gradient, and then their application in noisy images is unstable and unreliable. Therefore global mechanisms are required, so that they can avoid falling into spurious solutions due to the noise. In this paper we present a gradient-based evolutionary algorithm as a heuristic mechanism to achieve boundary extraction in noisy digital images. Evolutionary algorithms explore the combinatory space of possible solutions by means of a process of selection of the best solutions (generated by mutation and crossover), followed by the evaluation of the new solutions (fitness) and the selection of a new set of solutions. Each possible solution is in our case a contour, whose fitness measures the variation of intensity accumulated along it. This process is repeated from a first approximation of the solution (the initial population)either a certain number of generations or until some appropriate halting criterion is reached. The uniform exploration of the space of solutions and the local minima avoidance induce to form better solutions through the gradual evolution of the populations. |
| first_indexed | 2024-12-19T00:40:05Z |
| format | Article |
| id | doaj.art-3af8d2c8b9f64341af9e8f91eb3a5f97 |
| institution | Directory Open Access Journal |
| issn | 1666-6046 1666-6038 |
| language | English |
| last_indexed | 2024-12-19T00:40:05Z |
| publishDate | 2003-04-01 |
| publisher | Postgraduate Office, School of Computer Science, Universidad Nacional de La Plata |
| record_format | Article |
| series | Journal of Computer Science and Technology |
| spelling | doaj.art-3af8d2c8b9f64341af9e8f91eb3a5f972022-12-21T20:44:37ZengPostgraduate Office, School of Computer Science, Universidad Nacional de La PlataJournal of Computer Science and Technology1666-60461666-60382003-04-01301712639Boundary extraction through gradient-based evolutionary algorithmRomán Katz0Claudio Delrieux1Departamento de Ingenieria Electrica y de Computadoras, Universidad Nacional del Sur, Bahia Blanca, ARGENTINADepartamento de Ingenieria Electrica y de Computadoras, Universidad Nacional del Sur, Bahia Blanca, ARGENTINABoundary extraction is an important procedure associated with recognition and interpretation tasks in digital image processing and computer vision. Most of the segmentation techniques are based on the detection of the local gradient, and then their application in noisy images is unstable and unreliable. Therefore global mechanisms are required, so that they can avoid falling into spurious solutions due to the noise. In this paper we present a gradient-based evolutionary algorithm as a heuristic mechanism to achieve boundary extraction in noisy digital images. Evolutionary algorithms explore the combinatory space of possible solutions by means of a process of selection of the best solutions (generated by mutation and crossover), followed by the evaluation of the new solutions (fitness) and the selection of a new set of solutions. Each possible solution is in our case a contour, whose fitness measures the variation of intensity accumulated along it. This process is repeated from a first approximation of the solution (the initial population)either a certain number of generations or until some appropriate halting criterion is reached. The uniform exploration of the space of solutions and the local minima avoidance induce to form better solutions through the gradual evolution of the populations.https://journal.info.unlp.edu.ar/JCST/article/view/946boundary extractionpattern recognitionimage processingevolutionary algorithmsmetaheuristics |
| spellingShingle | Román Katz Claudio Delrieux Boundary extraction through gradient-based evolutionary algorithm Journal of Computer Science and Technology boundary extraction pattern recognition image processing evolutionary algorithms metaheuristics |
| title | Boundary extraction through gradient-based evolutionary algorithm |
| title_full | Boundary extraction through gradient-based evolutionary algorithm |
| title_fullStr | Boundary extraction through gradient-based evolutionary algorithm |
| title_full_unstemmed | Boundary extraction through gradient-based evolutionary algorithm |
| title_short | Boundary extraction through gradient-based evolutionary algorithm |
| title_sort | boundary extraction through gradient based evolutionary algorithm |
| topic | boundary extraction pattern recognition image processing evolutionary algorithms metaheuristics |
| url | https://journal.info.unlp.edu.ar/JCST/article/view/946 |
| work_keys_str_mv | AT romankatz boundaryextractionthroughgradientbasedevolutionaryalgorithm AT claudiodelrieux boundaryextractionthroughgradientbasedevolutionaryalgorithm |