Texture Segmentation using LBP embedded Region Competition
In this paper, we modify the region competition method to segment textures. First, local Binary pattern (LBP) histogram is adopted to capture the texture information. Then, considering the specific goal of texture segmentation, we propose new assumption about region competition and rewrite the energ...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2005 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:24316 |
| Acceso en línea: | https://ddd.uab.cat/record/24316 https://dx.doi.org/urn:doi:10.5565/rev/elcvia.83 |
| Access Level: | acceso abierto |
| Palabra clave: | Region competition Local binary pattern Competició de regió Model binari local Competición de región Modelo binario local |
| Sumario: | In this paper, we modify the region competition method to segment textures. First, local Binary pattern (LBP) histogram is adopted to capture the texture information. Then, considering the specific goal of texture segmentation, we propose new assumption about region competition and rewrite the energy function based on LBP histograms. We also develop the two-stage iterative algorithm to make our energy converge to a local minimum. Because of the fast LBP operator and nonparametric histogram model, we can simplify the step of parameter estimating, which is always the most time-consuming. Besides, LBP' s high performance for texture characterization helps to make our method more suitable for texture segmentation problem. Experiments show that the performance of our proposed method is promising, and a robust and fast segmentation of texture images is obtained. |
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