Pulse-coupled neural network based on an adaptive Gabor filter for pavement crack segmentation
This article proposes a Pulse-Coupled Neural Network based on an adaptive Gabor filter for the segmentation of cracks in the pavement in digital images. By estimating the noise in the image, the parameters of the filter that convolves the neurons of the model are adjusted. As a result iterations wer...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | México |
| Institución: | UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO |
| Repositorio: | Journal of Applied Research and Technology |
| Idioma: | inglés |
| OAI Identifier: | oai:ojs2.localhost:article/1837 |
| Acceso en línea: | https://jart.icat.unam.mx/index.php/jart/article/view/1837 |
| Access Level: | acceso abierto |
| Palabra clave: | Pulse-coupled neural network adaptive Gabor filter parallel processing crack segmentation |
| Sumario: | This article proposes a Pulse-Coupled Neural Network based on an adaptive Gabor filter for the segmentation of cracks in the pavement in digital images. By estimating the noise in the image, the parameters of the filter that convolves the neurons of the model are adjusted. As a result iterations were reduced to 2%with ? 90% precision. The algorithm was parallelized on the GPU and the processing time was reduced to n/NM regardless of the M and N dimensions of theimage. |
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