Neighbouring Color Dependence Matrix for Image Analysis : Application to homogeneous and heterogeneous areas detection and characterization
A new method for color texture characterization and color texture region detection is presented. This method, which we will name NCDM (Neighbouring Color Dependence Matrices), is the extension to color textures of the NGLDM (Neighbouring Gray Level Dependence Matrices) introduced by Sun et al. [1] a...
| Autores: | , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2008 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:24592 |
| Acceso en línea: | https://ddd.uab.cat/record/24592 https://dx.doi.org/urn:doi:10.5565/rev/elcvia.149 |
| Access Level: | acceso abierto |
| Palabra clave: | Espai de color Textura de color Segmentació d'imatge de color Color space Color Texture Anisotropy NGLDM Color Image Segmentation Chi-square Test |
| Sumario: | A new method for color texture characterization and color texture region detection is presented. This method, which we will name NCDM (Neighbouring Color Dependence Matrices), is the extension to color textures of the NGLDM (Neighbouring Gray Level Dependence Matrices) introduced by Sun et al. [1] and completed by Berry et al. [2]. This approach consists in estimating the dependences of colors between a pixel and its neighbours. We propose two steps: a color areas classification in two classes followed by the characterization of the detected areas. In the first step, we compute the NCDM with an isotropic neighbourhood. The structure of the isotropic NCD distribution allow us to separate the pixels of a color composite image into two classes, which correspond respectively to homogeneous and heterogeneous regions in the image. We then consider that the heterogeneous regions are potentially textured regions and in the second step we propose to compute the NCDM with anisotropic neighbourhoods corresponding to the eight principal directions. To seek the dominant directions in a color texture, a measure of spatial dependence between a pixel and its neighbours is computed by way of a chi-square test. This measure is based on the fit of the NGLD and NCD distribution with a binomial model under independence hypothesis. The variations of the colors are computed in uniform perceptual color spaces. We have chosen the color space "L1 norm" introduced by Angulo and Serra. |
|---|