Cellular automata enabling novel fast shape recognition for muon tomography

Abstract

We present a simple and efficient muon tomography simulation based on Data Modeling and a fast method for real-time threat target identification in obscured environments. Our approach introduces a fast form of statistical characterization in conjunction with equation based Data Models that makes the use of median calculation and Point of Closest Approach (POCA) reconstruction unnecessary. Our method enables accurate medium to high Z multi-target identification without background subtraction and in less than 10 seconds total processing time. Our method is general and applies to other volumetric/voxel processing as well. © 2009 SPIE.