TRANSLATION-INVARIANT OPTICAL-PATTERN RECOGNITION WITHOUT CORRELATION

Most optical pattern recognition techniques rely on correlation that i nherently achieves translation invariance. We introduce a significantl y different formulation for image recognition in which a set of inner product operators are used to achieve translation-invariant pattern re cognition. Our formulation extends the distortion-invariant linear pha se coefficient composite filter family, developed by Hassebrook et al. , into a set of translation-invariant inner product operators. Transla tion invariance is achieved by treating 2-D translation as distortion. The magnitudes of the inner product operations are insensitive to tra nslation, whereas the phase responses vary, but are discarded. For lar ge images containing many objects, this method can be applied by tilin g the 2-D operators to the test image size, elementwise multiplying by the test image, and then convolving with a binary rectangular window. Impressive numerical efficiency, exceeding that of fast Fourier trans form-based techniques, is attained by the inner product operator appro ach. Examples of our approach, distortion-invariant detection and disc rimination capabilities, idealized optical implementation, and compari son with conventional matched filters are presented, (C) 1996 Society of Photo-Optical Instrumentation Engineers.