Journal Article10.1109/34.142910
Object and texture classification using higher order statistics
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TL;DR: The problem of the detection and classification of deterministic objects and random textures in a noisy scene is discussed and an energy detector is developed in the cumulant domain by exploiting the noise insensitivity of higher order statistics.
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Abstract: 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. >
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186
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Optimal linear-quadratic systems for detection and estimation
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A unifying maximum-likelihood view of cumulant and polyspectral measures for non-Gaussian signal classification and estimation
TL;DR: A unifying view of cumulant and polyspectral discriminant measures utilizes these lags and provides a common framework for development and performance analysis of novel and existing estimation and classification algorithms.
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Characterization and estimation of two-dimensional ARMA models
TL;DR: A class of finite-order two-dimensional autoregressive moving average (ARMA) is introduced that can represent any process with rational spectral density and has the noncausal and semicausal Markov property without imposing any specific boundary conditions.
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An estimation-theoretic approach to terrain image segmentation
TL;DR: A method for modeling images of natural terrain is developed and applied to the segmentation of aerial photographic data with an underlying stochastic structure based on linear filtering concepts providing a means of modeling the terrain in local areas of the image.
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