Model-based 3-D object recognition using scalar transform descriptors

Kenneth Dawson; David St. G. Vernon

doi:10.1117/12.57132

1 February 1992 Model-based 3-D object recognition using scalar transform descriptors

Kenneth Dawson, David St. G. Vernon

Proceedings Volume 1609, Model-Based Vision Development and Tools; (1992) https://doi.org/10.1117/12.57132
Event: Robotics '91, 1991, Boston, MA, United States

Abstract

Three dimensional object recognition is an essential capability for any advanced machine vision system. We present a new technique for the recognition of 3-D objects on the basis of comparisons between 3-D models. Secondary representations of the models, which may be considered as complex scalar transform descriptors, are employed. The use of these representations overcomes the common dependency of matching individual model primitives (such as edges or surfaces). The secondary representations used are one-dimensional histograms of components of the visible orientations, depth maps and needle diagrams. Matching is achieved using template matching and normalized correlation techniques between the secondary representations. We demonstrate the power of this new technique with several examples of object recognition of models derived from actively sensed range data.

Citation Download Citation

Kenneth Dawson and David St. G. Vernon "Model-based 3-D object recognition using scalar transform descriptors", Proc. SPIE 1609, Model-Based Vision Development and Tools, (1 February 1992); https://doi.org/10.1117/12.57132

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