Abstract
We present a new method for computing optimized channels for channelized quadratic observers (CQO) that is feasible for highdimensional image data. The method for calculating channels is applicable in general and optimal for Gaussian distributed image data. Gradientbased algorithms for determining the channels are presented for five different informationbased figures of merit (FOMs). Analytic solutions for the optimum channels for each of the five FOMs are derived for the case of equal mean data for both classes. The optimum channels for three of the FOMs under the equal mean condition are shown to be the same. This result is critical since some of the FOMs are much easier to compute. Implementing the CQO requires a set of channels and the first and secondorder statistics of channelized image data from both classes. The dimensionality reduction from M measurements to L channels is a critical advantage of CQO since estimating image statistics from channelized data requires smaller sample sizes and inverting a smaller covariance matrix is easier. In a simulation study we compare the performance of ideal and Hotelling observers to CQO. The optimal CQO channels are calculated using both eigenanalysis and a new gradientbased algorithm for maximizing Jeffrey's divergence (J). Optimal channel selection without eigenanalysis makes the JCQO on largedimensional image data feasible.
Original language  English (US) 

Pages (fromto)  549565 
Number of pages  17 
Journal  Journal of the Optical Society of America A: Optics and Image Science, and Vision 
Volume  32 
Issue number  4 
DOIs  
State  Published  2015 
ASJC Scopus subject areas
 Electronic, Optical and Magnetic Materials
 Atomic and Molecular Physics, and Optics
 Computer Vision and Pattern Recognition
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Method for optimizing channelized quadratic observers for binary classification of largedimensional image datasets
Kupinski, M. K. (Contributor) & Clarkson, E. (Contributor), figshare, 2015
DOI: 10.6084/m9.figshare.c.3753806.v1, https://figshare.com/collections/Method_for_optimizing_channelized_quadratic_observers_for_binary_classification_of_largedimensional_image_datasets/3753806/1
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Method for optimizing channelized quadratic observers for binary classification of largedimensional image datasets
Kupinski, M. K. (Contributor) & Clarkson, E. (Contributor), figshare, 2015
DOI: 10.6084/m9.figshare.c.3753806, https://figshare.com/collections/Method_for_optimizing_channelized_quadratic_observers_for_binary_classification_of_largedimensional_image_datasets/3753806
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