Paper Info
Title
One-Class Multiple-Look Fusion: A Theoretical Comparison of Different Approaches with Examples from Infrared Video
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.
Year
DOI
Venue
2013
10.1109/CVPRW.2013.58
Computer Vision and Pattern Recognition Workshops
Keywords
Field
DocType
sequential probability ratio test,dempster-shafer theory,one-class multiple-look fusion,different approaches,infrared video,increased computing power,video imagery,theoretical comparison,bayesian fusion,consistent probabilistic framework,false alarm error,infrared video classification,multiple look,multiple-look fusion,radar imaging,image sensors,probabilistic logic,solid modeling,probability,optical fibers,uncertainty,pattern recognition,image fusion,image classification,statistical analysis
False alarm,Image fusion,Computer science,Fusion,Artificial intelligence,Contextual image classification,Sequential probability ratio test,Radar,Computer vision,Image sensor,Pattern recognition,Machine learning,Theory of evidence
Conference
Volume
Issue
ISSN
2013
1
2160-7508
Citations 
PageRank 
References 
0
0.34
4
Authors
1
Name
Order
Citations
PageRank
Mark W. Koch19210.60