Image Pattern Recognition with Separable Trade-Off Correlation Filters

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.

[1]  D Casasent,et al.  Unified synthetic discriminant function computational formulation. , 1984, Applied optics.

[2]  W H Lee,et al.  Binary computer-generated holograms. , 1979, Applied optics.

[3]  A. B. Vander Lugt,et al.  Signal detection by complex spatial filtering , 1964, IEEE Trans. Inf. Theory.

[4]  B V Kumar,et al.  Tutorial survey of composite filter designs for optical correlators. , 1992, Applied optics.

[5]  Juan Campos,et al.  Filter design of composite trade-off filter with support regions to obtain invariant pattern recognition with defocused images , 2003 .

[6]  Sanjit K. Mitra,et al.  Computer-aided design of separable two-dimensional digital filters , 1977 .

[7]  P. Réfrégier.  Filter design for optical pattern recognition: multicriteria optimization approach. , 1990, Optics letters.

[8]  J. Horner,et al.  Phase-only matched filtering. , 1984, Applied optics.

[9]  D Casasent,et al.  Advanced distortion-invariant minimum average correlation energy (MACE) filters. , 1992, Applied optics.

[10]  Abhijit Mahalanobis,et al.  Correlation pattern recognition in compressed images , 2001, Other Conferences.

[11]  Z. Q. Wang,et al.  Amplitude-compensated matched filtering. , 1988, Applied optics.

[12]  B. Kumar,et al.  Performance measures for correlation filters. , 1990, Applied optics.

[13]  Frank E. McFadden.  Optimal separable correlation filters , 2002, SPIE Defense + Commercial Sensing.

[14]  Ph. Réfrégier.  Pattern recognition methods for optical correlation , 1991 .