A model-based method for rotation invariant texture classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Generalized Gabor Scheme of Image Representation in Biological and Machine Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multichannel Texture Analysis Using Localized Spatial Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification of Rotated and Scaled Textured Images Using Gaussian Markov Random Field Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Minimax entropy principle and its application to texture modeling
Neural Computation
Rotation Invariant Texture Features and Their Use in Automatic Script Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Perceptual metrics for image database navigation
Perceptual metrics for image database navigation
IEEE Transactions on Image Processing
Rotation-invariant texture classification using a complete space-frequency model
IEEE Transactions on Image Processing
Novel approach for rotation invariant texture recognition
CI '07 Proceedings of the Third IASTED International Conference on Computational Intelligence
Automatic classification of granite tiles through colour and texture features
Expert Systems with Applications: An International Journal
A sequential machine vision procedure for assessing paper impurities
Computers in Industry
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In this paper we propose a method for rotation-invariant 2D texture classification. Energy-normalized texture features are obtained by multiscale and multichannel decomposition using Gabor and Gaussian filters. Rotation invariance is achieved by the Fourier expansion of these features with respect to orientation. Unlike most previously reported methods, the textures are modeled with nonparametric feature distributions. In the experiments involving two standard datasets, with the classifier trained on samples of only one rotation and tested for all the others, high recognition rates were obtained.