A model-based method for rotation invariant texture classification
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rotation-invariant texture classification using modified Gabor filters
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 1)-Volume 1 - Volume 1
Radon Transform Orientation Estimation for Rotation Invariant Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture classification using ridgelet transform
Pattern Recognition Letters
Texture classification using Gabor wavelets based rotation invariant features
Pattern Recognition Letters
Comparison and fusion of multiresolution features for texture classification
Pattern Recognition Letters
Rotation-invariant multiresolution texture analysis using Radon and wavelet transforms
IEEE Transactions on Image Processing
Invariant pattern recognition using the RFM descriptor
Pattern Recognition
Rotation-invariant texture features from the steered Hermite transform
Pattern Recognition Letters
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Some recent rotation invariant texture analysis approaches such as multiresolution approaches yield high correct classification percentages, but present insufficient noise tolerance. This paper describes a new method for rotation invariant texture analysis. In the proposed method, Radon transform is utilized to project a texture image onto projection space to convert a rotation of the original texture image to a translation of the projection in the angle variable, and then Radon projection correlation distance is introduced. A k-nearest neighbors' classifier with Radon projection correlation distances is employed to implement texture classification and orientation estimation. Theoretical and experimental results show the high classification accuracy of this approach as a result of using the Radon projection correlation distance instead of repetitious usage of discrete transforms. It is also shown that the proposed method presents high noise tolerance and yields high accuracy in orientation estimation in comparison with Khouzani's method.