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Centroiding and classification of objects using a processor array with a scalable region of interest

In this paper we describe how the location and size of an object in a multi-object scene can be identified and classified using a processor array with a scalable region of interest. Objects of interest can be classified by matching them with 25-pixel object prototypes in a window that is adjustable from 17 x 17 to 5 x 5. Matlab simulations of the algorithms are shown. In order to carry out the operations effectively, the processor is equipped with a global OR and global sum. Also, the outputs of the row and column decoders can be determined by boundary cell outputs, in addition to the address bits. A 64 x 64-cell array has been sent to fabrication.