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Proceedings Paper

Human template estimation using a Gaussian processes algorithm
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Paper Abstract

In this paper we propose the use of a machine-learning algorithm based in Gaussian Processes to estimate a human observer linear template for the detection of a signal in a noisy background. Estimating a human observer template is not novel, however the use of a multi-kernel Gaussian Processes approach is. This model provides spatial smoothing by using a sparse kernel representation. For validation purposes, we train this model observer with the ground truth and the estimated template is actually the same as the statistically optimal detector. Next, we present the human observer template estimated for the detection of a signal on a different power-low background.

Paper Details

Date Published: 11 March 2014
PDF: 7 pages
Proc. SPIE 9037, Medical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment, 90370Y (11 March 2014); doi: 10.1117/12.2044442
Show Author Affiliations
Francesc Massanes, Illinois Institute of Technology (United States)
Jovan G. Brankov, Illinois Institute of Technology (United States)


Published in SPIE Proceedings Vol. 9037:
Medical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment
Claudia R. Mello-Thoms; Matthew A. Kupinski, Editor(s)

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