Proceedings PaperClassification Of Optical Data By Orthogonalized Spatial Filtering
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Orthogonalized components of images are provided by the association of digital procedures and pure optical processing, in order to achieve classifying mappings. In a first step preprocessed data are optically delivered - typically by sampling of Fourier spectra. They satisfy both orthonormal and dimensionally reduced description of the considered images, which have not to be entirely digitized. The feature extraction consists of computing the dominant eigenvectors of the data covariance matrix or the Fourier descriptors of differences between spectra. The data are classifyied in the reduced space defined by the dominant eigen-vectors or Fourier descriptors. Applications to handwriting recognition and clustering are presented, starting from series of complete pages.