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Proceedings Paper

Volume correlation filters for recognizing patterns in 3D data
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Paper Abstract

Correlation filters are ideally suited for recognizing patterns in three-dimensional (3D) data. Whereas most model-based techniques tend to measure the overall dimensions of objects and their larger features, correlation filters can readily (and efficiently) exploit intricate surface details, the gray values of surfaces as well as internal structure, if any. Thus correlation filters may be the preferred approach in scenarios when intensity and range data are both available, or when the internal structure of an object has been mapped (e.g. tomography). In this paper, we outline the development of filters for 3D data that we refer to as Volume Correlation Filters (VCFs), illustrate their use with range images of an object, and outline future work for the development of 3D correlation techniques.

Paper Details

Date Published: 13 November 2001
PDF: 8 pages
Proc. SPIE 4471, Algorithms and Systems for Optical Information Processing V, (13 November 2001); doi: 10.1117/12.449356
Show Author Affiliations
Abhijit Mahalanobis, Lockheed Martin Corp. (United States)
Bhagavatula Vijaya Kumar, Carnegie Mellon Univ. (United States)
Alan J. Van Nevel, Naval Air Warfare Ctr. (United States)


Published in SPIE Proceedings Vol. 4471:
Algorithms and Systems for Optical Information Processing V
Bahram Javidi; Demetri Psaltis, Editor(s)

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