Comparison of deterministic and probabilistic model matching techniques for laser radar target recognition
Walter Armbruster · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
The paper compares the target identification performance of conventional model matching criteria and of new probabilistic techniques based on Bayesian hypothesis generation and verification. Match techniques are categorized into two types: those requiring target segmentation results and those which do not. Applied to low-resolution laser radar images of military vehicles, deterministic techniques using no segmentation results had the lowest target identification rates. New probabilistic techniques using no segmentation results are introduced, having significantly higher target identification rates than the best known deterministic procedures. The best results were attained by a probabilistic matching approach requiring target segmentation. Using certain simplifying assumptions, the latter technique can be reformulated as a deterministic procedure, involving no probabilities on scene parameters, and having almost the same target identification performance.