Gabor wavelets for texture edge extraction

Textures in images have a natural order, both in orientation and multiple narrow-band frequency, which requires the user to employ multichannel local spatial/frequency filtering and orientation selectivity, and to have a multiscale characteristic. Each channel covers one part of a whole frequency domain, which indicates different information for the different texton. Gabor filter, as a near orthogonal wavelet used in this paper, has orientation selectivity, multiscale property, linear phase, and good localization both in spatial and frequency domains, which are suitable for texture analysis. Gabor filters are employed for clustering the similarity of the same type of textons. Gaussian filters are also used for detection of normal image edges. Then hybrid texture and nontexture gradient measurement is based on fusion of the difference of amplitude of the filter responses between Gabor and Gaussian filters at neighboring pixels by mainly using average squared gradient. Normalization, based on the noise response and based on maximum response, is computed.

[1]  Josef Kittler,et al.  On local linear transform and Gabor filter representation of texture , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol. III. Conference C: Image, Speech and Signal Analysis,.

[2]  Josef Bigün,et al.  N-folded Symmetries by Complex Moments in Gabor Space and their Application to Unsupervised Texture Segmentation , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  A. Rosenfeld,et al.  Mosaic Models for Textures , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Tai Sing Lee,et al.  Texture Segmentation by Minimizing Vector-Valued Energy Functionals: The Coupled-Membrane Model , 1992, ECCV.

[5]  William E. Higgins,et al.  Texture Segmentation using 2-D Gabor Elementary Functions , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  King-Sun Fu,et al.  Syntactic Pattern Recognition And Applications , 1968 .

[7]  C.-C.J. Kuo,et al.  Texture classification with tree-structured wavelet transform , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems.

[8]  Rangasami L. Kashyap,et al.  Synthesis and Estimation of Random Fields Using Long-Correlation Models , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  P Perona,et al.  Preattentive texture discrimination with early vision mechanisms. , 1990, Journal of the Optical Society of America. A, Optics and image science.

[10]  Wolfgang Förstner,et al.  A Framework for Low Level Feature Extraction , 1994, ECCV.

[11]  A. Kundu,et al.  Rotation and Gray Scale Transform Invariant Texture Identification using Wavelet Decomposition and Hidden Markov Model , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Ibrahim M. Elfadel,et al.  Gibbs Random Fields, Cooccurrences, and Texture Modeling , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  M. Hassner,et al.  The use of Markov Random Fields as models of texture , 1980 .

[14]  M.,et al.  Statistical and Structural Approaches to Texture , 2022 .

[15]  Rama Chellappa,et al.  A unified approach to boundary perception: edges, textures, and illusory contours , 1993, IEEE Trans. Neural Networks.

[16]  Alan C. Bovik,et al.  Shape-from-texture by wavelet-based measurement of local spectral moments , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.