Fundamentals of digital image processing
Fundamentals of digital image processing
Multiresolution Feature Extraction and Selection for Texture Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multichannel Texture Analysis Using Localized Spatial Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Periodicity, Directionality, and Randomness: Wold Features for Image Modeling and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Filtering for Texture Classification: A Comparative Study
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
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Texture synthesis-by-analysis with hard-limited Gaussian processes
IEEE Transactions on Image Processing
Rotation-invariant texture classification using a complete space-frequency model
IEEE Transactions on Image Processing
Statistical texture characterization from discrete wavelet representations
IEEE Transactions on Image Processing
Extended fractal analysis for texture classification and segmentation
IEEE Transactions on Image Processing
Reduced complexity modeling and reproduction of colored textures
IEEE Transactions on Image Processing
A multiresolution approach for texture synthesis using the circular harmonic functions
IEEE Transactions on Image Processing
Wavelet-based rotational invariant roughness features for texture classification and segmentation
IEEE Transactions on Image Processing
Texture classification using spectral histograms
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
Unsupervised texture segmentation of images using tuned matched Gabor filters
IEEE Transactions on Image Processing
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
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In this paper, we present a texture classification procedure that makes use of a blind deconvolution approach. Specifically, the texture is modeled as the output of a linear system driven by a binary excitation. We show that features computed from one-dimensional slices extracted from the two-dimensional autocorrelation function (ACF) of the binary excitation allows representing the texture for rotation-invariant classification purposes. The two-dimensional classification problem is thus reconduced to a more simple one-dimensional one, which leads to a significant reduction of the classification procedure computational complexity.