A model-based method for rotation invariant texture classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatial frequency channels and perceptual grouping in texture segregation
Computer Vision, Graphics, and Image Processing - Special issue on human and machine vission, part II
Texture Features for Browsing and Retrieval of Image Data
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
Log-Polar Wavelet Energy Signatures for Rotation and Scale Invariant Texture Classification
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
Extraction of noise robust rotation invariant texture features via multichannel filtering
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 3 - Volume 3
Modeling spatial and temporal textures
Modeling spatial and temporal textures
Rotation-invariant and scale-invariant Gabor features for texture image retrieval
Image and Vision Computing
Rotation Invariant Image Classification Based on MPEG-7 Homogeneous Texture Descriptor
SNPD '07 Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 03
Rotation-invariant texture features from the steered Hermite transform
Pattern Recognition Letters
Noise robust rotation invariant features for texture classification
Pattern Recognition
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In this paper, we propose a method that can be used for image texture recognition in the presence of concurrent rotation and scale changes with tunable directional bandpass Gabor filter banks. The method relies on the analysis of the frequency spectra of the image textures, and from which the rotation and scale changes are estimated using a new spectral shift measure. Tunable Gabor filter banks are designed based on the spectral shift measure. Spectral features obtained from applying the tuned Gabor filter bank are used in a novel search strategy to achieve texture recognition. The proposed method is compared with a non-tunable Gabor filter bank and the improvement in recognition performance is demonstrated through the experimental results on 112 Brodatz textures.