An experiment on texture segmentation using modulated wavelets

A textured image is considered to be produced by a smooth signal, representing in part the illumination environment, modulated by some frequencies determined by surface characteristics of the textures. Through expansion of the smooth signal into wavelets, a textured image may be decomposed into modulated "wavelets" providing multiresolution information. It is shown that the corresponding wavelet coefficients can be obtained efficiently by using the standard wavelet transform while the associated h and g filters are modulated accordingly. A set of multichannel filters is designed through the use of a modulated "wavelet' multiresolution decomposition, providing both spatial frequency and orientation selectivity. Based on these multichannel amplitude responses as discriminating features, an image containing multiple textures can be effectively segmented. The potential of this approach is shown by experimental results.

[1]  Jian Fan,et al.  Texture Classification by Wavelet Packet Signatures , 1993, MVA.

[2]  Chee Sun Won,et al.  Unsupervised segmentation of noisy and textured images using Markov random fields , 1992, CVGIP Graph. Model. Image Process..

[3]  J. Daugman.  Two-dimensional spectral analysis of cortical receptive field profiles , 1980, Vision Research.

[4]  Alfredo Restrepo,et al.  Localized measurement of emergent image frequencies by Gabor wavelets , 1992, IEEE Trans. Inf. Theory.

[5]  Anil K. Jain,et al.  Unsupervised texture segmentation using Gabor filters , 1990, 1990 IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings.

[6]  Charles A. Bouman,et al.  Multiple Resolution Segmentation of Textured Images , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  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..

[8]  C.-C. Jay Kuo,et al.  A wavelet transform approach to texture analysis , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[9]  Gilbert Strang,et al.  Wavelets and Dilation Equations: A Brief Introduction , 1989, SIAM Rev..

[10]  John G. Daugman,et al.  Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression , 1988, IEEE Trans. Acoust. Speech Signal Process..

[11]  C.-C. Jay Kuo,et al.  Texture analysis and classification with tree-structured wavelet transform , 1993, IEEE Trans. Image Process..

[12]  Rama Chellappa,et al.  Multiresolution Gauss-Markov random field models for texture segmentation , 1997, IEEE Trans. Image Process..

[13]  Wilson S. Geisler,et al.  Multichannel Texture Analysis Using Localized Spatial Filters , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Bedrich J. Hosticka,et al.  Unsupervised texture segmentation of images using tuned matched Gabor filters , 1995, IEEE Trans. Image Process..

[15]  Michael Unser,et al.  Multiresolution Feature Extraction and Selection for Texture Segmentation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Jia-Lin Chen,et al.  Unsupervised texture segmentation using multichannel decomposition and hidden Markov models , 1995, IEEE Trans. Image Process..

[17]  Anil K. Jain,et al.  A multi-channel filtering approach to texture segmentation , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[18]  Mohan M. Trivedi,et al.  Circular-Mellin features for texture segmentation , 1995, IEEE Transactions on Image Processing.

[19]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  B. Julesz,et al.  Human factors and behavioral science: Textons, the fundamental elements in preattentive vision and perception of textures , 1983, The Bell System Technical Journal.

[21]  Rama Chellappa,et al.  Unsupervised Texture Segmentation Using Markov Random Field Models , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Michael Unser,et al.  Texture classification and segmentation using wavelet frames , 1995, IEEE Trans. Image Process..

[23]  Bayya Yegnanarayana,et al.  Segmentation of Gabor-filtered textures using deterministic relaxation , 1996, IEEE Trans. Image Process..

[24]  Dennis F. Dunn,et al.  Optimal Gabor filters for texture segmentation , 1995, IEEE Trans. Image Process..

[25]  J. Nachmias,et al.  Discrimination of simple and complex gratings , 1975, Vision Research.

[26]  Linda G. Shapiro,et al.  Computer and Robot Vision , 1991 .

[27]  J. Daugman.  Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[28]  S. Mallat.  Multiresolution approximations and wavelet orthonormal bases of L^2(R) , 1989 .

[29]  I. Daubechies.  Orthonormal bases of compactly supported wavelets , 1988 .

[30]  S.R. Yhann,et al.  Boundary localization in texture segmentation , 1995, IEEE Trans. Image Process..