Name
Abstract | ||
|---|---|---|
Texture is an important spatial feature useful for identifying objects or regions of interest in an image. This paper presents a new set of textural measures derived from the texture spectrum. The proposed features extract textural information of an image with a more complete respect of texture characteristics (in all the eight directions instead of only one displacement vector used in the co-occurrence matrix approach). Some of Brodatz's natural texture images have been used for evaluating the performance of the features in the characterization and discrimination of the texture aspects of images. Some of the traditional texture features, extracted from the co-occurrence matrix, have also been used for comparison with the new measures in the discrimination of different lithological units of an airborne synthetic aperture radar image. Promising results are obtained and presented in this paper. |
Year | DOI | Venue |
|---|---|---|
1991 | 10.1016/0031-3203(91)90052-7 | Pattern Recognition |
Keywords | Field | DocType |
pattern recognition,texture spectrum,texture features,image processing,texture analysis,spectrum | Computer vision,Texture compression,Synthetic aperture radar image,Pattern recognition,Matrix (mathematics),Image texture,Image processing,Artificial intelligence,Spectrum analysis,Mathematics,Texture filtering,Displacement (vector) | Journal |
Volume | Issue | ISSN |
24 | 5 | Pattern Recognition |
Citations | PageRank | References |
50 | 5.57 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dong-Chen He | 1 | 189 | 21.81 |
| Li Wang | 2 | 120 | 16.84 |