Image Modeling: A Mathematical Framework for Segmentation and Object Detection.
Final technical rept. 1 Oct 83-30 Apr 87,
STATISTICAL CONSULTING ASSOCIATES INC PROVIDENCE RI
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Decision rules for segmenting scenes and for detecting the presence of distinguished objects in digital images can be based on classical principles of statistical principles of statistical inference if appropriate mathematical image models are developed for observable pictures. The main goal of this research was to devise and analyze alternation image models for digitized FLIR images. The work has been done in close cooperation with the Advanced Modeling Team of the U.S. Army Night Vision and Electro-Optics Laboratory, Ft. Belvoir, Virginia. This report concentrates of hierarchical Markov Random Field models and their application to restoration and segmentation of FLIR images. Keywords Image processing Bayesian methods Infrared images. Author