1 May 1992 An optical correlator feature extractor neural net system
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Optical Engineering, 31(5), (1992). doi:10.1117/12.57138
Abstract
The three optical information processing techniques of detection, recognition, and identification can and should be combined to achieve the best benefits of each. All methods are required for difficult pattern recognition problems. We consider the identification of multiple objects in the field of view in clutter. A morphological correlator is used to achieve detection. Hierarchical and symbolic pattern recognition correlators can also achieve detection as well as recognition. For very large class probems, feature extractors are required for identification, but first require detection. For difficult multiclass discrimination problems, neural net methods (rather than linear discriminant functions) are needed for identification.
David P. Casasent, "An optical correlator feature extractor neural net system," Optical Engineering 31(5), (1 May 1992). http://dx.doi.org/10.1117/12.57138
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KEYWORDS
Neural networks

Neurons

Optical correlators

Image filtering

Detection and tracking algorithms

Optical filters

Pattern recognition

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