A high speed optical correlator is presented in this paper. It is a joint transform correlator using a BSO photorefractive crystal in the Fourier plane. The performance of the system such a rotation and scale robustness are presented for fingerprint recognition. To demonstrate the interest of such an optical processor, a comparison with numerical systems is presented. Besides, we will also show that the evolution of correlators is quite compatible with the evolution of numerical processors.
An optical photorefractive joint transform correlator (PRJTC) was built using a twisted liquid crystal spatial light modulator in the input plane to display the images and a photorefractive crystal in the Fourier plane to perform the nonlinear correlation. We present here new correlation filters to optimize the correlation. There filters are correlated with the scene instead of the simple reference. To calculated these filters, we introduce two characteristics of the setup to optimize the filters: the nonlinearity of the photorefractive crystal, the coding domain of the displaying device.
A high-speed optical photorefractive correlator using a ferroelectric spatial light modulator and a new type of binary filters optimized for the crystal nonlinearity will be presented in terms of characteristics and performances.
We present a method to achieve phase conjugation based on saturable-gain degenerate four- wave mixing in the laser medium itself. Experimental and theoretical investigations show that phase conjugate reflectivities and efficiencies higher than 100% can be achieved with such an interaction. High conjugate-reflectivity at (lambda) equals 1.06 micrometers is demonstrated in conventional flash-pumped Nd:YAG amplifiers and in compact diode-pumped Nd:YVO4 amplifiers.
We report the implementation and performance of a photorefractive joint transform correlator operating as an optical post-processor of an electronic fingerprint database search/identification system. The approach is as follows: in an initial electronic identification step, a list of possible 'candidates' is extracted from a large database, using standard electronic group-classification techniques. This candidate list is processed by optical correlation for final identification. We present the experimental demonstration of database search using a 3500- fingerprint candidate list. More specifically, we have operated on group-1 fingerprints, for which the lack of bifurcation, center and delta prevents efficient electronic classification. Undistorted fingerprints are unambiguously recognized. In this approach, significant fingerprint distortions can be handled. We discuss the system performance in terms of robustness, speed and efficiency.
A photorefractive joint transform correlator is connected to a PC-based image processing board and tested for fingerprint recognition. Distortions due to rotation, scale, and partial hiding are considered: ± 4 deg in rotation, ± 7% in scale, and up to 80% hiding can be handled by the correlator. The performance of the correlator operating with a realtime fingerprint acquisition camera is then measured. The captured fingerprint is generally distorted with respect to its original counterpart stored in the PC memory. These distortions are mostly due to the positioning and pressure of the finger on the glass prism of the camera. We evaluate the recognition performance qualitatively and statistically with these distortions on a limited data bank and demonstrate 80% successful recognition rate.
This paper reviews the basic principles, physical processes, and most recent demonstrations of optical correlators using dynamic holographic techniques in nonlinear media such as bulk photorefractives (PR), thin multiple quantum wells (MQW), and bacteriorhodopsin (BR) films.
We study a hybrid optoelectronic architecture for pattern recognition. In this architecture, a multichannel correlator realizes feature extractions on the analyzed image while an electronic neural network (NN) performs the high-level pattern recognition task. Due to its in situ learning and adaptive capabilities, the NN provides an efficient way for full exploitation of the computational power of optical processors. Indeed, not only the theoretical transfer function of the pattern recognition system is realized but also the imperfections of the analog optical computation are learned in the processor. The potential of this approach is illustrated on a simple multiclass problem of robotic classification. precise comparisons with different techniques of filter synthesis for the feature extraction performed by the multichannel correlator are carefully analyzed. An optical implementation based on a joint transform correlator using a photorefractive crystal is presented.
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