Optimal Gabor filters for texture segmentation Export

@article{citeulike:2804746, abstract = {Texture segmentation involves subdividing an image into differently textured regions. Many texture segmentation schemes are based on a filter-bank model, where the filters, called Gabor filters, are derived from Gabor elementary functions. The goal is to transform texture differences into detectable filter-output discontinuities at texture boundaries. By locating these discontinuities, one can segment the image into differently textured regions. Distinct discontinuities occur, however, only if the Gabor filter parameters are suitably chosen. Some previous analysis has shown how to design filters for discriminating simple textures. Designing filters for more general natural textures, though, has largely been done ad hoc. We have devised a more rigorously based method for designing Gabor filters. It assumes that an image contains two different textures and that prototype samples of the textures are given a priori. We argue that Gabor filter outputs can be modeled as Rician random variables (often approximated well as Gaussian rv's) and develop a decision-theoretic algorithm for selecting optimal filter parameters. To improve segmentations for difficult texture pairs, we also propose a multiple-filter segmentation scheme, motivated by the Rician model. Experimental results indicate that our method is superior to previous methods in providing useful Gabor filters for a wide range of texture pairs}, author = {Dunn, D. and Higgins, W. E.}, booktitle = {Image Processing, IEEE Transactions on}, citeulike-article-id = {2804746}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/83.392336}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=392336}, doi = {10.1109/83.392336}, journal = {Image Processing, IEEE Transactions on}, keywords = {filters, gabor, segmentation, texture}, number = {7}, pages = {947--964}, posted-at = {2008-05-16 10:21:19}, priority = {2}, title = {Optimal Gabor filters for texture segmentation}, url = {http://dx.doi.org/10.1109/83.392336}, volume = {4}, year = {1995} }

TY - JOUR ID - citeulike:2804746 L3 - citeulike-article-id:2804746 N2 - Texture segmentation involves subdividing an image into differently textured regions. Many texture segmentation schemes are based on a filter-bank model, where the filters, called Gabor filters, are derived from Gabor elementary functions. The goal is to transform texture differences into detectable filter-output discontinuities at texture boundaries. By locating these discontinuities, one can segment the image into differently textured regions. Distinct discontinuities occur, however, only if the Gabor filter parameters are suitably chosen. Some previous analysis has shown how to design filters for discriminating simple textures. Designing filters for more general natural textures, though, has largely been done ad hoc. We have devised a more rigorously based method for designing Gabor filters. It assumes that an image contains two different textures and that prototype samples of the textures are given a priori. We argue that Gabor filter outputs can be modeled as Rician random variables (often approximated well as Gaussian rv's) and develop a decision-theoretic algorithm for selecting optimal filter parameters. To improve segmentations for difficult texture pairs, we also propose a multiple-filter segmentation scheme, motivated by the Rician model. Experimental results indicate that our method is superior to previous methods in providing useful Gabor filters for a wide range of texture pairs IS - 7 JF - Image Processing, IEEE Transactions on EP - 964 TI - Optimal Gabor filters for texture segmentation VL - 4 T2 - Image Processing, IEEE Transactions on SP - 947 KW - filters KW - gabor KW - segmentation KW - texture AU - Dunn, D AU - Higgins, WE PY - 1995/// UR - http://dx.doi.org/10.1109/83.392336 ER -