期刊
OPTICS COMMUNICATIONS
卷 338, 期 -, 页码 77-89出版社
ELSEVIER
DOI: 10.1016/j.optcom.2014.10.038
关键词
Object recognition; Composite correlation filters; Multi-objective evolutionary algorithm; Combinatorial optimization
类别
资金
- Consejo Nacional de Ciencia y Tecnologia (CONACYT) [130504]
- Secretaria de Investigacion y Posgrado - Instituto Politecnico Nacional (SIP-IPN) [SIP20140678]
- U.S. Department of Energy by Lawrence Livermore National Laboratory [DE-AC52-07NA27344]
Composite correlation filters are used for solving a wide variety of pattern recognition problems. These filters are given by a combination of several training templates chosen by a designer in an ad hoc manner. In this work, we present a new approach for the design of composite filters based on multi-objective combinatorial optimization. Given a vast search space of training templates, an iterative algorithm is used to synthesize a filter with an optimized performance in terms of several competing criteria. Moreover, by employing a suggested binary-search procedure a filter bank with a minimum number of filters can be constructed, for a prespecified trade-off of performance metrics. Computer simulation results obtained with the proposed method in recognizing geometrically distorted versions of a target in cluttered and noisy scenes are discussed and compared in terms of recognition performance and complexity with existing state-of-the-art filters. (C) 2014 Elsevier B.V. All rights reserved.
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