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Computación y Sistemas

Print version ISSN 1405-5546

Comp. y Sist. vol.6 n.3 México Jan./Mar. 2003




Noisy Binary Texture Recognition Using the Coordinated Cluster Transform


Reconocimiento de Texturas Binarias Ruidosas Usando la Transformada de Cúmulos Coordinados


Evguenii Kurmyshev, Francisco Cuevas and Raúl Sánchez


Centro de Investigaciones en Óptica, A.C. Apartado Postal 1–948 León Guanajuato, México. E–mails: ; ;


Article received on May 6, 2002
Accepted on March 14, 2003



In this paper a technique using the coordinated cluster representation (CCR) is examined for recognition of binary computer generated and natural texture images corrupted by additive noise. A normalized local property histogram of the CCR is used as a unique feature vector. The ability of the descriptor to capture spatial statistical features of an image is exploited. The evaluation criteria is the recognition performance using a simple minimum distance classifier for recognition purposes. The experimental results indicate that the proposed technique is efficient for recognition of textures deteriorated by high level additive noise. Textures under test run through periodic up to random ones.

Keywords: Pattern recognition, binary texture analysis, image representation, coordinated clusters.



En este artículo se estudia una técnica, basada en la representación de imágenes por cúmulos coordinados (RICC), para el reconocimiento de imágenes binarias tanto de texturas naturales como aquellas generadas por computadora, las cuales fueron corrompidas por un ruido aditivo. El histograma normalizado de RICC es usado como vector único de características de la imagen. Se explota la habilidad del descriptor de captar las características estadísticas espaciales de una imagen. Como un criterio de evaluación usamos la eficiencia de reconocimiento usando un clasificador simple de distancia mínima para el reconocimiento. Se muestra que la técnica propuesta es eficiente para el reconocimiento de texturas deterioradas por el ruido aditivo. Se prueba la eficiencia del método en un rango amplio de texturas, siendo éstas desde puramente periódicas hasta completamente aleatorias.

Palabras clave: Reconocimiento de patrones, análisis de texturas binarias, representación de imágenes, cúmulos coordinados.





This work was supported by CONACYT under the grant No. 31168–A. Francisco Cuevas thanks the Centro de Investigaciones en Óptica, A. C, CONACYT, and Centro de Investigación en Computación of the Instituto Politécnico Nacional of México for the support.



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