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Proceedings Paper

Correlation Synthetic Discriminant Functions for Object Recognition and Classification in High Clutter
Author(s): David Casasent; William Rozzi; Donald Fetterly
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Paper Abstract

Correlation synthetic discriminant functions (SDFs) represent a practical and novel extension of matched spatial filter (MSF) correlators for distortion-invariant multi-object and multi-class pattern recognition. This paper reviews the off-line synthesis of such filters and the advantageous features of correlation shape control that they provide. We then concentrate on extensive tests performed with these filters to assess their performance in the identification of ship images, subjected to 3-D distortions. The pattern recognition problem addressed involves multi-object, multi-class recognition with aspect distortion-invariance in the presence of clutter. An adaptive threshold is shown to allow recognition of objects in the presence of spatially-varying modulation. The noise performance of these filters is also found to be most excellent. Correct classification rates approaching 98% can be obtained with these correlation SDFs.

Paper Details

Date Published: 19 December 1985
PDF: 11 pages
Proc. SPIE 0575, Applications of Digital Image Processing VIII, (19 December 1985); doi: 10.1117/12.966496
Show Author Affiliations
David Casasent, Carnegie-Mellon University (United States)
William Rozzi, Carnegie-Mellon University (United States)
Donald Fetterly, General Dynamics (United States)


Published in SPIE Proceedings Vol. 0575:
Applications of Digital Image Processing VIII
Andrew G. Tescher, Editor(s)

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