Abstract
This paper describes the implementations of convolution and regular moments on a transputer network. Discrete convolution is the principal spatial domain method for digital image enhancement. Moments are by far the most popular descriptors for image regions and boundary segments. Both convolution and moments are computationally expensive and difficult to accomplish in real time. To reduce computational time, parallel implementations of convolution and moments were investigated and the details of the best two implementations employing different interprocessor communication topologies on a multi-transputer system are described. Two theoretical performance models based on the implementations are used to predict the number of processors needed to satisfy the requirements for a real-time image processing system. The methodology presented for parallel processing can be easily adapted for other distributed memory multiprocessor systems.
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