| Revista: | Computación y Sistemas |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000457240 |
| ISSN: | 1405-5546 |
| Autors: | Ojeda Magaña, Benjamin1 Quintanilla Domínguez, Joel2 Ruelas, Ruben1 Barrón Adame, Jose Miguel3 Barboza Niño, Luis Enrique1 Guzmán Cabrera, Rafael4 Andina, Diego5 |
| Institucions: | 1Universidad de Guadalajara, Centro Universitario de Ciencias Exactas e Ingenierías, Guadalajara, Jalisco. México 2Universidad Politécnica de Juventino Rosas, Juventino Rosas, Guanajuato. México 3Universidad Tecnológica Suroeste de Guanajuato, Valle de Santiago, Guanajuato. México 4Universidad de Guanajuato, División de Ingenierías, Salamanca, Guanajuato. México 5Universidad Politécnica de Madrid, Escuela Técnica Superior de Ingenieros de Telecomunicación, Madrid. España |
| Any: | 2019 |
| Període: | Oct-Dic |
| Volum: | 23 |
| Número: | 4 |
| Paginació: | 1393-1415 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Aplicado, descriptivo |
| Resumen en inglés | In this work, an alternative new methodology to segment regions in an image is proposed. The method takes the advantages offered by hybrid algorithms, as the Possibilistic Fuzzy c-Means (PFCM) clustering algorithm, which has the qualities of both the Fuzzy c-Means (FCM) and the Possibilistic c-Means (PCM). The method is called sub-segmentation, and it consists of finding some clusters in an image through the segmentation of the image and, within these clusters, the less representative pixels or atypical pixels. These elements very frequently represent the zones of interest during image analysis. Three different cases are used in order to illustrate the method. The first one is an image of a drop of milk, where the generality of the method is tested in a simple but representative image. The second case corresponds to digital mammograms, where the potentiality of the method is tested in a critical application, such as anomalies identification in mammograms for cancer detection. The last case gives an idea of its range of applications, as the method is applied to an industrial case of classification of wood boards according to their quality. As can be seen from the three cases used in this work, the results are very interesting and promising |
| Disciplines | Ciencias de la computación |
| Paraules clau: | Inteligencia artificial, Procesamiento de datos, Programación, Reconocimiento de patrones, Procesamiento de imágenes, Algoritmos de agrupamiento, Detección de fallas, Diagnóstico |
| Keyword: | Artificial intelligence, Data processing, Programming, Pattern recognition, Image processing, Clustering algorithms, Fault detection, Diagnosis |
| Text complet: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
