An optical Fourier/electronic neurocomputer automated inspection system

An optical Fourier/electronic neurocomputer automated inspection system prototype is described. The system is composed to two modules: (1) a video-input optical/electronic Fourier feature extraction module, and (2) a PC/AT-based neurocomputer for feature signature (i.e., image) classification. Global shape and texture analysis, capable of discriminating relatively small and unpredictable image differences, is performed at speeds up to 15 images/s. The system performs 2-D image data compression by utilizing the attractive properties of coherent optical Fourier transform generation and optical feature sampling. Neural network multiclass pattern classifier algorithms (i.e., backpropagation and counterpropagation) are used to ensure system robustness in the presence of noisy, degraded, partial, or distorted images. It is expected that discrimination results can be used to track image-change trends for adaptive process control. Preliminary experimental results are presented.<>