Pattern recognition with composite correlation filters designed with multi-objective combinatorial optimization

Victor H. Diaz-Ramirez, Andres Cuevas, Vitaly Kober, Leonardo Trujillo, Abdul Awwal

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

© Elsevier B.V. All rights reserved. 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.
Original languageAmerican English
Pages (from-to)77-89
Number of pages13
JournalOptics Communications
DOIs
StatePublished - 1 Mar 2015

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