A pipelined architecture for on-line low-level vision

The concept of online processing is presented as an effective approach to overcome the data distribution overhead in parallel real-time implementation of low-level vision. Online processing refers to pipelining each input item with the pipeline rate equal to the input arrival rate. Online algorithms are presented for four benchmark low level tasks, namely two-dimensional convolution, two-dimensional rank value filtering, connected component labeling, and Hough transform. A two-stage pipelined module that allows online implementations is described. This module is to function as a coprocessor to nodes in B-HIVE, a loosely coupled multiprocessor for integrated vision.<
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