Comparison of texture features based on Gabor filters
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Abstract
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…
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Keywords
- Artificial intelligence
- Pattern recognition (psychology)
- Gabor filter
- Robustness (evolution)
- Feature extraction
- Image texture
- Computer vision
- Gabor transform
UN Sustainable Development Goals
- Reduced inequalities
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