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Three-dimensional object recognition using similar triangles and decision treesA system, TRIDEC, that is capable of distinguishing between a set of objects despite changes in the objects' positions in the input field, their size, or their rotational orientation in 3D space is described. TRIDEC combines very simple yet effective features with the classification capabilities of inductive decision tree methods. The feature vector is a list of all similar triangles defined by connecting all combinations of three pixels in a coarse coded 127 x 127 pixel input field. The classification is accomplished by building a decision tree using the information provided from a limited number of translated, scaled, and rotated samples. Simulation results are presented which show that TRIDEC achieves 94 percent recognition accuracy in the 2D invariant object recognition domain and 98 percent recognition accuracy in the 3D invariant object recognition domain after training on only a small sample of transformed views of the objects.
Document ID
Document Type
Reprint (Version printed in journal)
Spirkovska, Lilly (NASA Ames Research Center Moffett Field, CA, United States)
Date Acquired
August 16, 2013
Publication Date
January 1, 1993
Publication Information
Publication: Pattern Recognition
Volume: 26
Issue: 5
ISSN: 0031-3203
Subject Category
Funding Number(s)
PROJECT: RTOP 506-59-31
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