Name
Abstract | ||
|---|---|---|
The problem of detecting texture boundaries without assuming any knowledge on the number of regions or the types of textures is considered. Texture boundaries are often regarded as better features than intensity edges, because a large class of images can be considered a composite of several different texture regions. An algorithm is developed that detects texture boundaries at reasonably high resolution without assuming any prior knowledge on the texture composition of the image. The algorithm utilizes the long correlation texture model with a small number of parameters to characterize textures. The parameters of the model are estimated by a least-squares method in the frequency domain. The existence and the location of texture boundary is estimated by the maximum-likelihood method. The algorithm is applied to several different images, and its performance is shown by examples. Experimental results show that the algorithm successfully detects texture boundaries without knowing the number of types of textures in the image. |
Year | DOI | Venue |
|---|---|---|
1989 | 10.1109/34.23113 | Pattern Analysis and Machine Intelligence, IEEE Transactions |
Keywords | Field | DocType |
detects texture boundary,texture boundary detection,least-squares method,texture composition,different texture region,prior knowledge,maximum-likelihood method,texture boundary,different image,small number,long correlation texture model,long correlation model,frequency domain,parameter estimation,image analysis,pattern recognition,high resolution,maximum likelihood method,least squares method,image segmentation,least square method,layout,face detection,frequency domain analysis,image resolution | Frequency domain,Least squares,Computer vision,Texture compression,Pattern recognition,Image texture,Bidirectional texture function,Computer science,Image processing,Artificial intelligence,Estimation theory,Texture filtering | Journal |
Volume | Issue | ISSN |
11 | 1 | 0162-8828 |
Citations | PageRank | References |
21 | 10.13 | 6 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rangasami L. Kashyap | 1 | 21 | 10.13 |
| Kie-Bum Eom | 2 | 92 | 35.29 |