Texture discrimination by Gabor functions
Biological Cybernetics
Fundamentals of digital image processing
Fundamentals of digital image processing
Discrete-time signal processing
Discrete-time signal processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multichannel Texture Analysis Using Localized Spatial Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear modeling of multidimensional non-Gaussian processes using cumulants
Multidimensional Systems and Signal Processing
Computational vision
Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling
International Journal of Computer Vision
Texture Classification Using Noncausal Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Bibliography on cyclostationarity
Signal Processing
Higher-order statistics based blind estimation of non-Gaussian bidimensional moving average models
Signal Processing - Fractional calculus applications in signals and systems
Wavelet and curvelet moments for image classification: Application to aggregate mixture grading
Pattern Recognition Letters
Image and Vision Computing
A flexible visual inspection system based on neural networks
International Journal of Systems Science - Innovative Production Machines and Systems, Guest Editors: Duc-Truong Pham, Anthony Soroka and Eldaw Eldukhri
Evaluation of the Texture Analysis Using Spectral Correlation Function
Fundamenta Informaticae
Generalized discriminant analysis: a matrix exponential approach
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evaluation of the Texture Analysis Using Spectral Correlation Function
Fundamenta Informaticae
Noise robust rotation invariant features for texture classification
Pattern Recognition
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The problem of the detection and classification of deterministic objects and random textures in a noisy scene is discussed. An energy detector is developed in the cumulant domain by exploiting the noise insensitivity of higher order statistics. An efficient implementation of this detector is described, using matched filtering. Its performance is analyzed using asymptotic distributions in a binary hypothesis-testing framework. The object and texture discriminant functions are minimum distance classifiers in the cumulant domain and can be efficiently implemented using a bank of matched filters. They are immune to additive Gaussian noise and insensitive to object shifts. Important extensions, which can handle object rotation and scaling, are also discussed. An alternative texture classifier is derived from a ML viewpoint and is statistically efficient at the expense of complexity. The application of these algorithms to the texture-modeling problem is indicated, and consistent parameter estimates are obtained.