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Proceedings Paper

Fuzzy logic information fusion for object recognition
Author(s): Byron H. Chen; Ching-Fang Lin; Stelios C.A. Thomopoulos
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Paper Abstract

Object recognition is often performed in an environment full of uncertainties. Typical factors are the imprecision introduced by the limitations of image processing algorithms, the misinterpretation of the feature vector due to noise or occlusion, and the infinite variability of the object features due to continuous environment change as well as countermeasures. An integrated approach (statistical methods, multisensor fusion, and fuzzy logic) for automatic object recognition if presented in this paper. A fuzzy scene representation is proposed to cope with uncertainties. The features of the object and the background are obtained from both a priori knowledge and the data collected by a multisensor suite and then reconstructed for object recognition. A recognition scheme, based on fuzzy logic, has been developed to merge the information from multiple sources of differing resolution and confidence into a combined assessment of the object identity. It has been found that the fuzzy logic based information fusion architecture provides a platform to accommodate the output of different types of image/signal processing algorithms. In addition, it allows the input of the temporal scene development through an expanded feature vector. Details of the fuzzy scene representation and the recognition process are discussed. Experimental results are presented to show the potential of the approach.

Paper Details

Date Published: 13 June 1995
PDF: 10 pages
Proc. SPIE 2493, Applications of Fuzzy Logic Technology II, (13 June 1995); doi: 10.1117/12.211804
Show Author Affiliations
Byron H. Chen, ORINCON Corp. (United States)
Ching-Fang Lin, American GNC Corp. (United States)
Stelios C.A. Thomopoulos, The Pennsylvania State Univ. (United States)


Published in SPIE Proceedings Vol. 2493:
Applications of Fuzzy Logic Technology II
Bruno Bosacchi; James C. Bezdek, Editor(s)

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