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Unsupervised texture segmentation using Gabor filters
Abstract
A texture segmentation algorithm inspired by the multichannel filtering theory for visual information processing in the early stages of the human visual system is presented. The channels are characterized by a bank of Gabor filters that nearly uniformly covers the spatial-frequency domain. A systematic filter selection scheme based on reconstruction of the input image from the filtered images is proposed. Texture features are obtained by subjecting each (selected) filtered image to a nonlinear transformation and computing a measure of energy in a window around each pixel. An unsupervised square-error clustering algorithm is then used to integrate the feature images and produce a segmentation. A simple procedure to incorporate spatial adjacency information in the clustering process is proposed. Experiments on images with natural textures as well as artificial textures with identical second and third-order statistics are reported. The algorithm appears to perform as predicted by preattentive texture discrimination by a human.>
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Daugman, J. G. (1985) · Journal of the Optical Society of America A · doi:10.1364/josaa.2.001160 · Cited by 3,219
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Metadata
- Authors
- Jain, A. K., & Farrokhnia, F.
- Venue
- 1990 IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings
- Publisher
- IEEE
- Published
- 2002-12-04
- Pages
- 14-19
- ISSN
- 1062-922X (ISSN-L)
- OpenAlex
- W2129749203
- Cited by
- 253
- References
- 1
- FWCI
- 4.63
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Jain, A. K., & Farrokhnia, F. (2002). Unsupervised texture segmentation using Gabor filters. 1990 IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings. https://doi.org/10.1109/icsmc.1990.142050
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