Paper Info
Title
A Modular NMF Matching Algorithm for Radiation Spectra
Abstract
In real-world object identification systems, the operational mission may change from day to day. For example, a target recognition system may be searching for heavy armor one day, and surface-to-air assets the next, or a radiation detection system may be interested in detecting medical isotopes in one instance, and special nuclear material in another. To accommodate this "mission of the day" type scenario, the underlying object database must be flexible and able to adjust to changing target sets. Traditional dimensionality reduction algorithms rely on a single basis set that is derived from the complete set of objects of interest, making missionspecific adjustment a significant task. In this work, we describe a method that uses many limited-size individual basis sets to represent objects of interest instead of a single unifying basis set. Thus, only the objects of interest for the mission at hand are used at any given time, and additional objects can be added to the system simply by training a basis for the new object. We demonstrate the modular identification system on the problem of identifying radioisotopes from their gamma ray spectra using nonnegative matrix factorization.
Year
DOI
Venue
2016
10.1109/CVPRW.2016.42
2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Keywords
Field
DocType
modular NMF matching algorithm,radiation spectra,object identification systems,operational mission,target recognition system,radiation detection system,medical isotopes detection,object database,mission of the day type scenario,modular identification system,gamma ray spectra,nonnegative matrix factorization,radioisotopes identification
Computer vision,Dimensionality reduction,Pattern recognition,Recognition system,Computer science,Identification system,Gamma ray spectra,Artificial intelligence,Non-negative matrix factorization,Modular design,Special nuclear material,Blossom algorithm
Conference
Volume
Issue
ISSN
2016
1
2160-7508
ISBN
Citations 
PageRank 
978-1-5090-1438-5
0
0.34
References 
Authors
4
2
Name
Order
Citations
PageRank
Melissa L. Koudelka1775.49
Daniel J. Dorsey200.34