One-Class Multiple-Look Fusion: A Theoretical Comparison of Different Approaches with Examples from Infrared Video

Author

Koch, Mark W

Author_Institution

Sandia Nat. Labs., Albuquerque, NM, USA

fYear

2013

fDate

23-28 June 2013

Firstpage

342

Lastpage

347

Abstract

Multiple-look fusion is quickly becoming more important in statistical pattern recognition. With increased computing power and memory one can make many measurements on an object of interest using, for example, video imagery or radar. By obtaining more views of an object, a system can make decisions with lower missed detection and false alarm errors. There are many approaches for combining information from multiple looks and we mathematically compare and contrast the sequential probability ratio test, Bayesian fusion, and Dempster-Shafer theory of evidence. Using a consistent probabilistic framework we demonstrate the differences and similarities between the approaches and show results for an application in infrared video classification.

Keywords

Bayes methods; image classification; image fusion; image sensors; inference mechanisms; infrared imaging; probability; statistical analysis; uncertainty handling; Bayesian fusion; Dempster-Shafer theory of evidence; false alarm error; infrared video classification; object measurement; one-class multiple-look fusion; probabilistic framework; radar imaging; sequential probability ratio test; statistical pattern recognition; Bayes methods; Optical fibers; Pattern recognition; Probabilistic logic; Solid modeling; Uncertainty; Vehicles; Infrared video; Multilook fusion; One class classifier;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Vision and Pattern Recognition Workshops (CVPRW), 2013 IEEE Conference on

Conference_Location

Portland, OR

Type

conf

DOI

10.1109/CVPRW.2013.58

Filename

6595897