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4 May 2009 Cellular automata enabling novel fast shape recognition for muon tomography
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Abstract
We present a novel and detailed algorithm for enabling passive muon tomography systems to be used for 3-D threat object recognition in real-time. Our method makes use of characteristic changes of the Hamming distance curve derived from Cellular Automata rules converted into a novel Data Model form. We show that fragmented and noisy shape images can be adequately processed and recognized without resorting to morphological or traditional template matching approaches. The approach is general and has utility in other target/shape recognition and imaging applications.
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Holger M. Jaenisch, James W. Handley, Kristina L. Jaenisch, and Nathaniel G. Albritton "Cellular automata enabling novel fast shape recognition for muon tomography", Proc. SPIE 7335, Automatic Target Recognition XIX, 733504 (4 May 2009); https://doi.org/10.1117/12.817833
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