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Proceedings Paper

Three-Dimensional Generalized Hough Transform For Object Identification
Author(s): Wang-He Lou; Anthony P. Reeves
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Paper Abstract

A generalized version of the Hough transform, called the 3-D Generalized Hough Transform (3-D GHT), provides an effective technique for the identification and location determination of objects in three dimensional Euclidean space. This technique is suitable for multiple object identification with occlusion; the surfaces of the objects of interest may have arbitrary complexity. The 3-D GHT is defined with respect to a 21/2-D image in which the surface normal has been determined for each pixel. As with the 2-D generalized Hough transform (2-D GHT), each active pixel is mapped into a set of locations in an accumulator array by means of a table defined for a given object. Peaks in this array correspond to possible locations for the transform defining object. A figure of merit is computed for each significant peak based on its local neighborhood in accumulator space in order to normalize with respect to noise and interference from other objects. Peak merit values from different object arrays for a given location are compared to determine the object type. The 3-D GHT has been tested with a number of synthetic range images to which various amounts of Gaussian noise had been added. The results indicate that the 3-D GHT generates less false peaks than the 2-D GHT. Furthermore, the 3-D GHT can distinguish between some objects that appear identical to the 2-D GHT due to the use of range information. The 3-D GHT has also been shown to be effective in multiple object images in which objects occluded each other.

Paper Details

Date Published: 1 March 1990
PDF: 12 pages
Proc. SPIE 1192, Intelligent Robots and Computer Vision VIII: Algorithms and Techniques, (1 March 1990); doi: 10.1117/12.969749
Show Author Affiliations
Wang-He Lou, Cornell University (United States)
Anthony P. Reeves, Cornell University (United States)

Published in SPIE Proceedings Vol. 1192:
Intelligent Robots and Computer Vision VIII: Algorithms and Techniques
David P. Casasent, Editor(s)

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