Generalized synthetic discriminant functions

Performance Analysis of the Modified-Hybrid Optical Neural Network Object Recognition System Within Cluttered Scenes

“…As for all the HONN-type systems, the multiplying coefficient now becomes a non-linear function of the input weights and the layer weights, rather than a simple linear multiplying constant as used in a constrained linear combinatorial-type filter synthesis procedure. The non-linear M-HONN system is inherently shift invariant and it may be employed in an optical correlator as would a linear superposition constrained-type filter, such as the synthetic discriminant function (SDF) -type (Bahri & Kumar, 1988) …”

Advances in Adaptive Composite Filters for Object Recognition

“…Analytical filters are typically given by a closed form mathematical expression that is directly derived from the respective signal and noise models while optimizing specific quality metrics (Javidi & Wang, 1997;Kerekes & Vijaya-Kumar, 2006;Vijaya-Kumar et al, 2000;Yaroslavsky, 1993). On the other hand, composite filters are constructed by combining a set of training images, which are explicit representations of the target object and their expected distortions (Bahri & Kumar, 1988;Kerekes & Vijaya-Kumar, 2008;Vijaya-Kumar, 1992). It is assumed that when the training images are properly chosen, we can synthesize composite filters that achieve very good and robust performance in recognizing the target object.…”

Comparative study of maximum average correlation height filter variants using ladar imagery

“…Correlation filters, and in particular maximum average correlation height ͑MACH͒ filters or optimal trade-off synthetic discriminant filters ͑OTSDF͒, [1][2][3][4][5][6][7][8] have been used to detect and classify objects in imagery. Studies using MACH filters to detect and classify military vehicles in cluttered environments 1,9 have demonstrated high detection and classification rates.…”