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Comparison of texture features based on Gabor filters
Open accessAbstract
Texture features that are based on the local power spectrum obtained by a bank of Gabor filters are compared. The features differ in the type of nonlinear post-processing which is applied to the local power spectrum. The following features are considered: Gabor energy, complex moments, and grating cell operator features. The capability of the corresponding operators to produce distinct feature vector clusters for different textures is compared using two methods: the Fisher (1923) criterion and the classification result comparison. Both methods give consistent results. The grating cell operator gives the best discrimination and segmentation results. The texture detection capabilities of the operators and their robustness to nontexture features are also compared. The grating cell operator is the only one that selectively responds only to texture and does not give false response to nontexture features such as object contours.
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Cites
A comparative study of texture measures with classification based on featured distributions
Ojala, T., Pietikäinen, M., & Harwood, D. (1996) · Pattern Recognition · doi:10.1016/0031-3203(95)00067-4 · Cited by 6,882
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
- Grigorescu, S. E., Petkov, N., & Kruizinga, P.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2002-10
- Volume
- 11
- Issue
- 10
- Pages
- 1160-1167
- ISSN
- 1057-7149 (ISSN-L)
- OpenAlex
- W2096127742
- Cited by
- 742
- References
- 2
- FWCI
- 16.47
- Type
- journal article
- Language
- en
- Open access
- green
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