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Title: Pattern recognition with composite correlation filters designed with multi-object combinatorial optimization

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. Furthermore, 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.
 [1] ;  [2] ;  [2] ;  [3] ;  [4]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  2. Instituto Politecnico National - CITEDI, Tijuana (Mexico)
  3. CICESE, Ensenada (Mexico)
  4. Instituto Tecnologico de Tijuana, Tijuana (Mexico)
Publication Date:
OSTI Identifier:
Report Number(s):
Journal ID: ISSN 0030-4018
Grant/Contract Number:
Accepted Manuscript
Journal Name:
Optics Communications
Additional Journal Information:
Journal Volume: 338; Journal Issue: C; Journal ID: ISSN 0030-4018
Research Org:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org:
Country of Publication:
United States
42 ENGINEERING; object recognition; composite correlation filters; multi-objective evolutionary algorithm; combinatorial optimization