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<p>A representation paradigm for instantiating and refining multiple, concurrent descriptions of an object from a sequence of imagery is presented. It is designed for the perception system of an autonomous robot that needs to describe many types of objects, initially detects objects at a distance and gradually acquires higher resolution data, and continuously collects sensory input. Since the data change significantly over time, the paradigm supports the evolution of descriptions, progressing from crude 2-D 'blob' descriptions to complete semantic models. To control this accumulation of new descriptions, the authors introduce the idea of representation space, a lattice of representations that specifies the order in which they should be considered for describing an object. A system, TraX, that constructs and refines models of outdoor objects detected in sequences of range data is described.</p>
robot vision; pattern recognition; range data sequence detection; artificial intelligence; representation space paradigm; concurrent descriptions; perception system; semantic models; TraX; artificial intelligence; computer vision; computerised pattern recognition

A. Bobick and R. Bolles, "The Representation Space Paradigm of Concurrent Evolving Object Descriptions," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 14, no. , pp. 146-156, 1992.
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