Image pattern recognition with separable trade-off correlation filters

  • Authors:
  • César San Martín;Asticio Vargas;Juan Campos;Sergio Torres

  • Affiliations:
  • Department of Electrical Engineering, University of La Frontera, Temuco, Chile;Department of Science Physics, University of La Frontera, Temuco, Chile;Department of Physics, University of Barcelona, Bellaterra, Barcelona, España;Department of Electrical Engineering, University of Concepción, Concepción, Chile

  • Venue:
  • ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
  • Year:
  • 2005

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Abstract

In this paper, a method to design separable trade-off correlation filters for optical pattern recognition is developed. The proposed method not only is able to include the information about de desirable peak correlation value but also is able to minimize both the average correlation energy and the effect of additive noise on the correlation output. These optimization criteria are achieved by employing multiple training objects. The main advantage of the method is based on using multiple information for improving the optical pattern recognition work on images with various objects. The separable Trade-off filter is experimentally tested by using both digital and optical pattern recognition.