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ECVision indexed and annotated bibliography of cognitive computer vision publications This bibliography was created by Hilary Buxton and Benoit Gaillard, University of Sussex, as part of ECVision Specific Action 8-1 The complete text version of this BibTeX file is available here: ECVision_bibliography.bib A. Imiya and K. Kawamoto Learning dimensionality and orientations of 3{D} objects ABSTRACT 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.

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