Pattern recognition with an adaptive joint transform correlator

Abstract

An adaptive joint transform correlator for real-time pattern recognition is presented. A reference image for the correlator is generated with a new iterative algorithm. The training algorithm is based on synthetic discriminant functions. The obtained reference image contains the information needed to reliably discriminate a target against known false objects and a cluttered background. Calibration lookup tables of all optoelectronics elements used are included in the design of the adaptive joint transform correlator. Two methods for the implementation of the proposed joint transform correlator in an optodigital setup are considered. Experimental results are provided and compared with those of computer simulations.

Distortion-Invariant Pattern Recognition with Adaptive Correlation Filters

“…Therefore, other sidelobes may appear everywhere on the correlation plane. To achieve a good recognition, a modified iterative algorithm (Diaz-Ramirez et al, 2006;González-Fraga et al, 2006) is proposed. At each iteration, the algorithm suppresses the highest sidelobe peak, and therefore the value of discrimination capability monotonically increases until a prespecified value is reached.…”

Advances in Adaptive Composite Filters for Object Recognition

“…To overcome these shortcomings, recent works propose an adaptive approach towards filter synthesis (Aguilar-Gonzalez et al, 2008;Diaz-Ramirez & Kober, 2007;Diaz-Ramirez et al, 2006;Gonzalez-Fraga et al, 2006;Martinez-Diaz et al, 2008;Ramos-Michel & Kober, 2008). In such an approach, the goal is to construct a composite filter with optimal performance characteristics for a fixed set of patterns, rather than a filter that achieves average performance over an ensemble of images.…”

Correlation Pattern Recognition in Nonoverlapping Scene Using a Noisy Reference

“…Since the pioneering work by VanderLugt [1], correlation filters have been extensively studied for the purpose of pattern recognition [2][3][4][5][6][7][8][9][10][11][12][13][14][15]. Within the context of pattern recognition, detection and location estimation are two very important tasks.…”