An Investigation Into Intermediate-Level Image Processing Using A Tree-Connected Transputer Network

Abstract

The regularity and local neighbourhood interdependence of picture data and the repetitive nature of many feature extraction algorithms may be usefully exploited in the design of specialised computer architectures for image processing at the pixel level. However, the features detected in the image will vary in type, number, position and size. The irregularity of this feature data prevents it from being easily partitioned. Also, at subsequent processing levels, various feature extraction, grouping and measurement algorithms will be employed. These are often more complex than low-level operations, and may be broken down into concurrently operating sub-processes. A more flexible multiprocessor architecture is therefore required, on to which a variety of algorithms can be mapped. This paper describes an augmented tree-structured MIMD processor network for intermediate level image processing. The Inmos Transputer has been chosen as the basic architectural building block. The programming and operation of the proposed architecture is illustrated using a Hough transform algorithm and a connected region finding routine.