Grayscale Image Segmentation Based on Associative Memories
In this paper, a grayscale image segmentation algorithm based on Extended Associative Memories (EAM) is proposed. The algorithm is divided into three phases. First, the uniform distribution of the image pixel values is determined by means of the histogram technique. The result of this phase is a set...
| Autores: | , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2011 |
| País: | México |
| Institución: | Instituto Politécnico Nacional |
| Repositorio: | Redalyc-IPN |
| OAI Identifier: | oai:redalyc.org:61520938003 |
| Acceso en línea: | https://www.redalyc.org/articulo.oa?id=61520938003 |
| Access Level: | acceso abierto |
| Palabra clave: | Computación Image segmentation associative memories clustering techniques |
| Sumario: | In this paper, a grayscale image segmentation algorithm based on Extended Associative Memories (EAM) is proposed. The algorithm is divided into three phases. First, the uniform distribution of the image pixel values is determined by means of the histogram technique. The result of this phase is a set of regions (classes) where each one is grouped into a certain number of pixel values. Second, the EAM training phase is applied to the information obtained at the first phase. The result of the second phase is an associative network that contains the centroids group of each of the regions in which the image will be segmented. Finally, the centroid to which each pixel belongs is obtained using the EAM classification phase, and the image segmentation process is completed. A quantitative analysis and comparative performance for frequently-used image segmentation by the clustering method, the k-means, and the proposed algorithm when it uses prom and med operators are presented. |
|---|