Dual nonlinear correlation applied to textured and color object recognition

Elisabet Perez; Maria Sagrario Millan Garcia-Verela; Katarzyna Chalasinska-Macukow

doi:10.1117/12.324130

7 September 1998 Dual nonlinear correlation applied to textured and color object recognition

Elisabet Perez, Maria Sagrario Millan Garcia-Verela, Katarzyna Chalasinska-Macukow

Proceedings Volume 3409, Electronic Imaging: Processing, Printing, and Publishing in Color; (1998) https://doi.org/10.1117/12.324130
Event: SYBEN-Broadband European Networks and Electronic Image Capture and Publishing, 1998, Zurich, Switzerland

Abstract

Dual nonlinear correlation (DNC) is a general operation in optical pattern recognition involving linear and nonlinear filtering methods. DNC also allows to apply new non-symmetric operators to both the analyzed scene channel and to the reference target channel. A third nonlinearity introduced in the frequency domain allows the control of the region of the spectrum where the DNC is applied. The implementation of the DNC is carried out in a sole filterless optoelectronic processor based on a two-step joint transform correlator assisted by computer. Experimental conditions related to camera and spatial light modulator features have an influence on the method performance. We present some applications of the DNC to textured and color pattern recognition with variable discrimination capability.

Citation Download Citation

Elisabet Perez, Maria Sagrario Millan Garcia-Verela, and Katarzyna Chalasinska-Macukow "Dual nonlinear correlation applied to textured and color object recognition", Proc. SPIE 3409, Electronic Imaging: Processing, Printing, and Publishing in Color, (7 September 1998); https://doi.org/10.1117/12.324130

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