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.
Financed by Fondo Nacional de Desarrollo Científico y Tecnológico Proyecto Fondecyt No 1010532 and No 1040946; Dirección General de Enseñanza Superior del Ministerio de Educación y Cultura Proyecto No BFM2003-06273-C02-01 and Proyecto de Investigación Conjunta dentro del Programa de Cooperación Científica con Iberoamérica DURSI-CONICYT ACI2003-51.S.T acknowledges support by grant Milenio ICM P02-049.
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Martín, C.S., Vargas, A., Campos, J., Torres, S. (2005). Image Pattern Recognition with Separable Trade-Off Correlation Filters. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2005. Lecture Notes in Computer Science, vol 3708. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11558484_21
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DOI: https://doi.org/10.1007/11558484_21
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