Statistical spectral analysis: a nonprobabilistic theory
Statistical spectral analysis: a nonprobabilistic theory
The ubiquitous Kronecker product
Journal of Computational and Applied Mathematics - Special issue on numerical analysis 2000 Vol. III: linear algebra
Unsupervised Texture Segmentation Using Feature Distributions
ICIAP '97 Proceedings of the 9th International Conference on Image Analysis and Processing-Volume I - Volume I
A framework for texture analysis based on spatial filtering
A framework for texture analysis based on spatial filtering
Multiscale Blob Features for Gray Scale, Rotation and Spatial Scale Invariant Texture Classification
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Description of interest regions with local binary patterns
Pattern Recognition
Dominant local binary patterns for texture classification
IEEE Transactions on Image Processing
Evaluation of the Texture Analysis Using Spectral Correlation Function
Fundamenta Informaticae
Local binary patterns variants as texture descriptors for medical image analysis
Artificial Intelligence in Medicine
Noise- and compression-robust biological features for texture classification
The Visual Computer: International Journal of Computer Graphics
A completed modeling of local binary pattern operator for texture classification
IEEE Transactions on Image Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Survey on LBP based texture descriptors for image classification
Expert Systems with Applications: An International Journal
Region covariance: a fast descriptor for detection and classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Optimal Spatial Adaptation for Patch-Based Image Denoising
IEEE Transactions on Image Processing
Texture Analysis and Classification With Linear Regression Model Based on Wavelet Transform
IEEE Transactions on Image Processing
Computational Perceptual Features for Texture Representation and Retrieval
IEEE Transactions on Image Processing
Efficient Texture Image Retrieval Using Copulas in a Bayesian Framework
IEEE Transactions on Image Processing
Texture Classification Using Dominant Neighborhood Structure
IEEE Transactions on Image Processing
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Utilization of cyclostationarity is a fresh paradigm in texture classification. This paper employs the Strip Spectral Correlation Analyzer (SSCA) as the new and superior method of such a category. The SSCA has been much more computational efficient than the other spectral correlation estimators, such as the FFT-Accumulated Method (FAM) or Direct Frequency Smoothing (DFS). Further, for comparable efficacy of the cyclostationary based analyzers, two new algorithms for implementation of both SSCA and FAM are proposed. The algorithms are fast, parallel, and linear-algebraic based, which brings many advantages in computational competence, feature generation flexibility, simplicity, and hardware implementation. SSCA as the unused promising texture analyzer and the new FAM implementation are compared with other state of the art methods in the case of classification accuracy, noise resistance and feature efficiency.