A Self-Organizing Multisensor Fusion Classification Algorithm

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Author Mamlook, Rustom en_US
Available date 2009-11-25T13:03:56Z en_US
Publication Date 1996 en_US
Citation Engineering Journal of Qatar University, 1996, Vol. 9, Pages 133-146. en_US
URI http://hdl.handle.net/10576/7859 en_US
Abstract A self-organizing multisensor fusion algorithm to classify the inputs (data or images) into classes (targets, backgrounds) is presented. The algorithm forms clusters and is trained without supervision. The clustering is done on the basis of the statistical properties of the set of inputs. The algorithm is a self-organizing algorithm, since it has the ability to form and adjust the number of clusters without being given the correct number of clusters. This algorithm implements a clustering algorithm that is very similar to the simple sequential leader clustering algorithm and the Carpenter/Grossberg net algorithm (CGNA). The algorithm differs from CGNA in that (1) the data inputs and data pointers may take on real values, (2) it features an adaptive mechanism for selecting the number of clusters, and (3) it features an adaptive threshold. The algorithm does not require the number of classes been known apriori. The problem of threshold selection is considered and the convergence of the algorithm is shown. An example is given to show the application of the algorithm for multisensor fusion for classifying targets and backgrounds, and the results of using this algorithm is compared to the results of using K-nearest neighbor algorithm. en_US
Language en en_US
Publisher Qatar University en_US
Subject Engineering: Research & Technology en_US
Title A Self-Organizing Multisensor Fusion Classification Algorithm en_US
Type Article en_US
Pagination 133-146 en_US
Volume Number 9 en_US


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