Optical image classification using optical/digital hybrid image processing systems

Offering parallel and real-time operations, optical image classification is becoming a general technique in the solution of real-life image classification problems. This thesis investigates several algorithms for optical realization.;Compared to other statistical pattern recognition algorithms, the Kittler-Young transform can provide more discriminative feature spaces for image classification. We shall apply the Kittler-Young transform to image classification and implement it on optical systems. A feature selection criterion is designed for the application of the Kittler-Young transform to image classification. The realizations of the Kittler-Young transform on both a joint transform correlator and a matrix multiplier are successively conducted. Experiments of applying this technique to two-category and three-category problems are demonstrated.;To combine the advantages of the statistical pattern recognition algorithms and the neural network models, processes using the two methods are studied. The Karhunen-Loeve Hopfield model is developed for image classification. This model has significant improvement in the system capacity and the capability of using image structures for more discriminative classification processes.;As another such hybrid process, we propose the feature extraction perceptron. The application of feature extraction techniques to the perceptron shortens its learning time. An improved activation function of neurons (dynamic activation function), its design and updating rule for fast learning process and high space-bandwidth product image classification are also proposed. We have shortened by two-thirds the learning time on the feature extraction perceptron as compared with the original perceptron. By using this architecture, we have shown that the classification performs better than both the Kittler-Young transform and the original perceptron.