MODEL-BASED RECOGNITION USING 3D SHAPE ALONE

The author shows that shape data alone, without absolute size, are highly effective in constraining the size of the search space of matches to stored 3D object models. The shape constraints developed are applied to sparse and error-prone measurements of surface orientations and scaled depths (that i...

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Main Author: Murray, D
Format: Journal article
Language:English
Published: 1987
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author Murray, D
author_facet Murray, D
author_sort Murray, D
collection OXFORD
description The author shows that shape data alone, without absolute size, are highly effective in constraining the size of the search space of matches to stored 3D object models. The shape constraints developed are applied to sparse and error-prone measurements of surface orientations and scaled depths (that is, depths scaled by a constant but unknown factor) synthesized from polyhedral models which themselves have six degrees of positional freedom with respect to the sensor. The matching paradigm used is that of Grimson and Lozano-Perez in which feasible interpretations of the data are obtained by requiring geometric consistency between metrics made on pairs of data and their associated matched pair of model faces and then tested by geometrical transformation.
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spelling oxford-uuid:d0449344-b53e-4ba1-a3f7-8b847f2d19fd2022-03-27T07:48:46ZMODEL-BASED RECOGNITION USING 3D SHAPE ALONEJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:d0449344-b53e-4ba1-a3f7-8b847f2d19fdEnglishSymplectic Elements at Oxford1987Murray, DThe author shows that shape data alone, without absolute size, are highly effective in constraining the size of the search space of matches to stored 3D object models. The shape constraints developed are applied to sparse and error-prone measurements of surface orientations and scaled depths (that is, depths scaled by a constant but unknown factor) synthesized from polyhedral models which themselves have six degrees of positional freedom with respect to the sensor. The matching paradigm used is that of Grimson and Lozano-Perez in which feasible interpretations of the data are obtained by requiring geometric consistency between metrics made on pairs of data and their associated matched pair of model faces and then tested by geometrical transformation.
spellingShingle Murray, D
MODEL-BASED RECOGNITION USING 3D SHAPE ALONE
title MODEL-BASED RECOGNITION USING 3D SHAPE ALONE
title_full MODEL-BASED RECOGNITION USING 3D SHAPE ALONE
title_fullStr MODEL-BASED RECOGNITION USING 3D SHAPE ALONE
title_full_unstemmed MODEL-BASED RECOGNITION USING 3D SHAPE ALONE
title_short MODEL-BASED RECOGNITION USING 3D SHAPE ALONE
title_sort model based recognition using 3d shape alone
work_keys_str_mv AT murrayd modelbasedrecognitionusing3dshapealone