Multidimensional Orientation Estimation with Applications to Texture Analysis and Optical Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Log-Polar Wavelet Energy Signatures for Rotation and Scale Invariant Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Outex - New Framework for Empirical Evaluation of Texture Analysis Algorithms
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Rotation and scale invariant texture features using discrete wavelet packet transform
Pattern Recognition Letters
IEEE Transactions on Multimedia
IEEE Transactions on Image Processing
Rotation-invariant texture classification using a complete space-frequency model
IEEE Transactions on Image Processing
Wavelet-based rotational invariant roughness features for texture classification and segmentation
IEEE Transactions on Image Processing
Robust rotation-invariant texture classification using a model based approach
IEEE Transactions on Image Processing
Rotation-invariant multiresolution texture analysis using Radon and wavelet transforms
IEEE Transactions on Image Processing
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ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
A modified Gabor function for content based image retrieval
Pattern Recognition Letters
A novel extended local-binary-pattern operator for texture analysis
Information Sciences: an International Journal
An Alternative Approach to Computing Shape Orientation with an Application to Compound Shapes
International Journal of Computer Vision
Texture and Target Orientation Estimation from Phase Congruency
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Dominant local binary patterns for texture classification
IEEE Transactions on Image Processing
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Illumination invariant and rotational insensitive textural representation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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Proceedings of the 1st ACM International Health Informatics Symposium
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Estimation of the orientation of textured patterns via wavelet analysis
Pattern Recognition Letters
Computers in Biology and Medicine
Colour and rotation invariant textural features based on Markov random fields
Pattern Recognition Letters
DTW for matching radon features: a pattern recognition and retrieval method
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
Information Sciences: an International Journal
Cascaded classification of high resolution remote sensing images using multiple contexts
Information Sciences: an International Journal
Continuous rotation invariant local descriptors for texton dictionary-based texture classification
Computer Vision and Image Understanding
Multimedia Databases and Data Management: A Survey
International Journal of Multimedia Data Engineering & Management
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This paper presents a new approach to rotation invariant texture classification. The proposed approach benefits from the fact that most of the texture patterns either have directionality (anisotropic textures) or are not with a specific direction (isotropic textures). The wavelet energy features of the directional textures change significantly when the image is rotated. However, for the isotropic images, the wavelet features are not sensitive to rotation. Therefore, for the directional textures, it is essential to calculate the wavelet features along a specific direction. In the proposed approach, the Radon transform is first employed to detect the principal direction of the texture. Then, the texture is rotated to place its principal direction at 0 degrees. A wavelet transform is applied to the rotated image to extract texture features. This approach provides a features space with small intraclass variability and, therefore, good separation between different classes. The performance of the method is evaluated using three texture sets. Experimental results show the superiority of the proposed approach compared with some existing methods.