Abstract
AbstractThe Connection Machine (CM) has been demonstrated to be an efficient and fast computational engine for the solution of many problems related to image processing. The high‐level parallelism of the CM naturally fits to many large‐scale data intensive applications.In this paper the implementation of parallel algorithms for the analysis of multidimensional images on the CM is presented. Different aspects in the analysis of multidimensional images are considered. In the field of artificial vision, the implementation of algorithms for the filtering of image sequences (both in space and time) and the estimation of the optical flow is described and some results in terms of accuracy and computation time are presented.The processing of three‐dimensional images is investigated in the field of biomedical engineering. In this case the goal is the development of algorithms for the 3‐D reconstruction of human body segments and their visualization.The parallel implementations exploit the fine grain parallelism allowed by the CM, processing each point of the data on a different processor. This mechanism is allowed by the possibility of dynamically reconfiguring the connectivity of the CM nodes and of defining a huge number of virtual processors. Moreover, as the CM processors operate on one‐bit data, it is possible to tune the number of bits for each data point to match the accuracy required by the application.
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