skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Optical image classification using optical/digital hybrid image-processing systems

Miscellaneous ·
OSTI ID:7205547

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. The author applies 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, he proposes the feature extraction perceptron. The application of feature extraction techniques to the perceptron shortens its learning time.

Research Organization:
Pennsylvania State Univ., University Park, PA (United States)
OSTI ID:
7205547
Resource Relation:
Other Information: Thesis (Ph.D.)
Country of Publication:
United States
Language:
English