Abstract
In texture classification and segmentation, the objective is to partition the given image into a set of homogeneous textured regions. This chapter presents schemes for texture classification and segmentation using features computed from Gabor-filtered images. The texture feature set is derived by filtering the image through a bank of modified Gabor kernels. The particular set of filters forms a multiresolution decomposition of the image. Although there are several viable options, including orthogonal wavelet transforms, Gabor wavelets are chosen for their desirable properties: Gabor functions achieve the theoretical minimum space frequency bandwidth product; a narrow-band Gabor function closely approximates an analytic function; the magnitude response of a Gabor function in the frequency domain is well behaved, having no side lobes; and Gabor functions appear to share many properties with the human visual system. © 2005 Elsevier Inc. All rights reserved.
| Original language | English |
|---|---|
| Title of host publication | Handbook of Image and Video Processing, Second Edition |
| Publisher | Elsevier |
| Pages | 455-470 |
| ISBN (Print) | 9780121197926 |
| DOIs | |
| Publication status | Published - 1 Jan 2005 |
| Externally published | Yes |
Bibliographical note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].Funding
This work was supported in part by grants from National Science Foundation (NSF) (94-1130, 97-04795, EIA-0080134), the Office of Naval Research (ONR/AASERT) (N0014-98-0515, N00014-01-0391). This work was performed in part under the auspices of the U.S. Department of Energy by University of California, Lawrence Livermore National Laboratory under Contract W-7405-Eng-48. The authors would also like to thank Sitaram Bhagavathy for carefully proofreading the manuscript.