Correlation Synthetic Discriminant Functions for Object Recognition and Classification in High Clutter

David Casasent; William Rozzi; Donald Fetterly

doi:10.1117/12.966496

19 December 1985 Correlation Synthetic Discriminant Functions for Object Recognition and Classification in High Clutter

David Casasent, William Rozzi, Donald Fetterly

Proceedings Volume 0575, Applications of Digital Image Processing VIII; (1985) https://doi.org/10.1117/12.966496
Event: 29th Annual Technical Symposium, 1985, San Diego, United States

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.

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David Casasent, William Rozzi, and Donald Fetterly "Correlation Synthetic Discriminant Functions for Object Recognition and Classification in High Clutter", Proc. SPIE 0575, Applications of Digital Image Processing VIII, (19 December 1985); https://doi.org/10.1117/12.966496

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