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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| Fformat: | Journal article |
| Iaith: | English |
| Cyhoeddwyd: |
1987
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| Crynodeb: | 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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