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17 July 1998 Parallel algorithm for target recognition using a multiclass hash database
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A method for recognition of unknown targets using large databases of model targets is discussed. Our approach is based on parallel processing of multi-class hash databases that are generated off-line. A geometric hashing technique is used on feature points of model targets to create each class database. Bit level coding is then performed to represent the models in an image format. Parallelism is achieved during the recognition phase. Feature points of an unknown target are passed to parallel processors each accessing an individual class database. Each processor reads a particular class of hash data base and indexes feature points of the unknown target. A simple voting technique is applied to determine the best match model with the unknown. The paper discusses our technique and the results from testing with unknown FLIR targets.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mosleh Uddin and Harley R. Myler "Parallel algorithm for target recognition using a multiclass hash database", Proc. SPIE 3374, Signal Processing, Sensor Fusion, and Target Recognition VII, (17 July 1998);

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