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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yong Huang Kap Luk Chan Zhongyang Huang |
| Copyright Year | 2000 |
| Description | Author affiliation: Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore (Yong Huang) |
| Abstract | This paper presents an adaptive texture model for texture classification. In this model, a texture is considered containing both structural and stochastic components. These two components are indeterministic and deterministic parts as in the Wold texture model that are represented by Gaussian Markov random field (GMRF) model and multichannel filtering model based on Gabor function (Gabor model), respectively. According to the different ratio of composition from each component in the texture model, an adaptive factor was proposed for the new adaptive model. Experiments demonstrated that the new adaptive model can better represent a wide variety of textures and hence can lead to better classification results. |
| Starting Page | 893 |
| Ending Page | 896 |
| File Size | 398234 |
| Page Count | 4 |
| File Format | |
| ISBN | 0769507506 |
| ISSN | 10514651 |
| DOI | 10.1109/ICPR.2000.903687 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2000-09-03 |
| Publisher Place | Spain |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Stochastic processes Feature extraction Gabor filters Markov random fields Data mining Frequency synthesizers Image analysis Image texture analysis Information analysis Frequency domain analysis |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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