Design and performance evaluation of parallel architectures for image segmentation processing

Abstract

The design of parallel architectures to perform image segmentation processing is given. In addition, the various designs are evaluated as to their performance, and a discussion of an optimal design is given. In this thesis, a set of eight segmentation algorithms has been provided as a starting point. Four of these algorithms will be evaluated and partitioned using two techniques. From this study of partitioning and considering the data flow through the total system, architectures utilizing parallel techniques will be derived. Timing analysis using pen and paper techniques will be given on the architectures using three of today's current technologies. Next, NETWORK II.5 simulations will be run to provide performance measures. Finally, evaluations of the various architectures will be made as well as the applicability of using NETWORK II.5 as a simulation language.