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. 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
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Related Subjects
PATTERN RECOGNITION
ALGORITHMS
CLASSIFICATION
COMPARATIVE EVALUATIONS
IMAGE PROCESSING
LEARNING
NEURAL NETWORKS
PARALLEL PROCESSING
REAL TIME SYSTEMS
TRANSFORMATIONS
EVALUATION
MATHEMATICAL LOGIC
PROCESSING
PROGRAMMING
990200* - Mathematics & Computers