Neural optoelectronic correlator for pattern recognition

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.