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Proceedings Paper

Application Of An Algotecture For Invariant Feature Extraction In Machine Vision
Author(s): Richard A. Messner; Harold H. Szu
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Paper Abstract

The exponential non-uniform to uniform hardwired spatial coordinate transformation inherently imbeds an algorithm in the hardware architecture and has thus been called an algotecture. It has been suggested that the algotecture described may be more sensitive to centroid pointing errors than conventional cartesian grid structures. Simulation results for crosscorrelation template matching in the algotecture space, as opposed to standard rectilinear coordinate space, is presented for the case of annulii images with various centroid mismatch. These simulations support the claim that the algotecture mapping is less sensitive to centroid mismatch. The use of template matching on an algotecture mapped grey scale image shows the feasibility of using this technique on more complex images. Since crosscorrelation is a relatively time consuming operation, a sliding window differencing similarity measure is proposed to accomplish fast detection in the algotecture mapped space directly at the sensor level. Coupling this idea with the classification of objects via the formation of orthogonal feature vectors contained in separate spatial frequency channels which are constrained by human visual system physiological data provides a fast method of object classification which exploits the fact that different features occur in different spatial frequency bands. Finally, the use of a three spatial frequency bandpass feature extraction filter system useful for an intra-class, inter-class, and membership identification classification scheme is discussed.

Paper Details

Date Published: 11 December 1985
PDF: 8 pages
Proc. SPIE 0579, Intelligent Robots and Computer Vision IV, (11 December 1985); doi: 10.1117/12.950781
Show Author Affiliations
Richard A. Messner, University of New Hampshire (United States)
Harold H. Szu, Naval Research Laboratory (United States)


Published in SPIE Proceedings Vol. 0579:
Intelligent Robots and Computer Vision IV
David P. Casasent, Editor(s)

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