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Fuzzy edge-symmetry features for improved intruder detection

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4 Author(s)
Srinivasa, N. ; HRL Labs., Malibu, CA, USA ; Medasani, S. ; Owechko, Y. ; Khosla, D.

The paper proposes a new set of fuzzy features based on symmetry of edges for improving the accuracy of detecting intruders. We show that the proposed fuzzy edge-symmetry feature-based classifier is comparable to the detection accuracy of a multi-scale wavelet feature system for intruder detection. We also present two approaches to fusing the results of classifiers trained independently on the edge-symmetry and wavelet features. Experimental results clearly indicate the improvement in system performance when the results of the two classifiers are fused.

Published in:

Fuzzy Systems, 2003. FUZZ '03. The 12th IEEE International Conference on  (Volume:2 )

Date of Conference:

25-28 May 2003