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19 December 1985 Correlation Synthetic Discriminant Functions for Object Recognition and Classification in High Clutter
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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.
© (1985) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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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