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...
Guardado en:
| Autores principales: | , |
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| Formato: | Articulo |
| Lenguaje: | Inglés |
| Publicado: |
2003
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| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/9451 http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Apr03-2.pdf |
| Aporte de: |
| id |
I19-R120-10915-9451 |
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| record_format |
dspace |
| institution |
Universidad Nacional de La Plata |
| institution_str |
I-19 |
| repository_str |
R-120 |
| collection |
SEDICI (UNLP) |
| language |
Inglés |
| topic |
Ciencias Informáticas evolutionary algorithms PATTERN RECOGNITION Image processing software Heuristic methods |
| spellingShingle |
Ciencias Informáticas evolutionary algorithms PATTERN RECOGNITION Image processing software Heuristic methods Katz, Román Delrieux, Claudio Boundary extraction through gradient-based evolutionary algorithm |
| topic_facet |
Ciencias Informáticas evolutionary algorithms PATTERN RECOGNITION Image processing software Heuristic methods |
| 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. |
| format |
Articulo Articulo |
| author |
Katz, Román Delrieux, Claudio |
| author_facet |
Katz, Román Delrieux, Claudio |
| author_sort |
Katz, Román |
| title |
Boundary extraction through gradient-based evolutionary algorithm |
| title_short |
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_sort |
boundary extraction through gradient-based evolutionary algorithm |
| publishDate |
2003 |
| url |
http://sedici.unlp.edu.ar/handle/10915/9451 http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Apr03-2.pdf |
| work_keys_str_mv |
AT katzroman boundaryextractionthroughgradientbasedevolutionaryalgorithm AT delrieuxclaudio boundaryextractionthroughgradientbasedevolutionaryalgorithm |
| bdutipo_str |
Repositorios |
| _version_ |
1764820491455954945 |