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Tracking image features using a parallel computational model

T.J. Ellis, M. Mirmehdi, G.R. Dowling, Tracking image features using a parallel computational model. Proc. SPIE Conf. no. 1708 "Applications of Artificial Intelligence X: Machine vision and Robots", pp. 172–183. April 1992. No electronic version available.

Abstract

This paper describes a parallel implementation of an image feature tracking system. The system is designed to operate as the front-end of a vision system for controlling autonomous guided vehicles (AGV). Image features or tokens (edge-based line segments in the example given here) are extracted from the image and allocated to individual tracking processes. Both the extraction and the tracking stages are performed by concurrent processes. Arbitrary tracking algorithms may be associated with each process; in the current implementation, a Kalman filter is used to track and predict tokens in subsequent image frames.

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