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General-purpose high-speed heterogeneous machine vision architecture

Machine vision systems are characterized by a requirement for (at least) two disparate kinds of processing: low level, highly repetitive data independent processing and high(er) level data dependent processing, often involving decision making. To date, the most efficient implementations of low level processing, typically at the pixel level, are to be found in special purpose board and chip level devices. At the higher level, however, increasingly more abstract or symbolic representations are required, and at present the capability of appropriate single processors is insufficient to match the low level component. Here parallel processing technology is being used to provide the required processing speed. This paper presents the design and implementation of one such system, in which the low level component consists of a number of datacube image processing boards, and the high-level component is provided by an array of transputers. We show how the design criteria motivate the choice of hardware and how flexible the resulting system actually is. The utility of the system and some achievable performance figures are presented in the context of Canny edge detection and a decentralized target tracker. The future development of the system is considered in the light of the forthcoming T9000 Transputer.