Name
Abstract | ||
|---|---|---|
The periodicity of a texture is one of its important visual characteristics. The inertias of co-occurrence matrices of the texture have been often used to detect the visual periodicity. However, it is time-consuming to explicitly construct these matrices. In this paper, we propose the distance matching function to avoid constructing the matrices due to our new interpretation of an inertia. For a texture of size m n, the inertias of all co-occurrence matrices can be obtained in OÖmn log mnÜ time by simultaneously evaluating the function at all displacement vectors. This is a significant im- provement over the previous method using the co-occurrence matrices, that requires OÖm2n2Ü time. " 1999 Elsevier Science B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1016/S0167-8655(98)00140-8 | Pattern Recognition Letters |
Keywords | Field | DocType |
co-occurrence matrix,inertia,patterns,fast determination,texture,distance matching function,textural periodicity,periodicity,co occurrence matrix | Pattern recognition,Co-occurrence matrix,Matrix (mathematics),Algorithm,Artificial intelligence,Inertia,Mathematics | Journal |
Volume | Issue | ISSN |
20 | 2 | Pattern Recognition Letters |
Citations | PageRank | References |
13 | 0.91 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gyuhwan Oh | 1 | 38 | 3.50 |
| Seungyong Lee | 2 | 2130 | 157.29 |
| Sung Yong Shin | 3 | 1904 | 168.33 |