Symmetric mean and directional contour pattern for texture classification
Abstract In this letter, we propose a simple yet effective texture descriptor, symmetric mean and directional contour pattern (SMDCP), for texture classification. In particular, first the robust symmetric mean pattern (RSMP) that extracts the sign and amplitude information of the local difference th...
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Wiley
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/dd3047bcb2a54f94a8be08c3b5a7ca67 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | Abstract In this letter, we propose a simple yet effective texture descriptor, symmetric mean and directional contour pattern (SMDCP), for texture classification. In particular, first the robust symmetric mean pattern (RSMP) that extracts the sign and amplitude information of the local difference through the neighbourhood average in a new scheme of encoding to further enhance the robustness to noise is constructed. Then a local directional and contour pattern (LDCP) to represent the contour information and direction information of adjacent sampling points is extracted. By concatenating the RSMP and LDCP, a robust and effective texture descriptor (SMDCP) for classification is built. Experimental results reveal that the proposed method obtains significant performance and high discrimination in comparison with 10 representative approaches. |
|---|