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Learning dimensionality and orientations of 3D objects
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DOI：10.1016/S0167-8655(00)00101-X.png)
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We naturally classify plates as flat and boxes as of bulky structure. This understanding is based on the dimensionality of the objects. The dimensionality and orientation are important features for recognition of a 3D object, since these geometric properties are fundamental features for the classification of objects and for grasp-control for robots. In this paper, we derive a computational model for the classification of dimensionality of objects using properties of the mechanical moments of solid objects. Our model is based on the principal component analyzer (PCA) since the analyzer in the 3D Euclidean space derives directions of the mechanical moments of the objects from random samples. The directions of the principal components also determine the direction of objects. Therefore, our algorithm computes the orientations of objects in 3D space. (C) 2001 Elsevier Science B.V. All rights reserved.
Keyword:
dimensionality
orientation
3D object
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