22 October 2001 Three-dimensional correlation filters for orientation invariant recognition
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Abstract
Correlation filters are ideally suited for recognizing patterns in 3D data. Whereas most model-based techniques tend to measure the overall dimensions of objects and their larger features, correlation filters can readily 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. In this paper, we outline the development of filters for 3D data that we refer to as Volume Correlation Filters, illustrate their use with range images of an object, and outline future work for the development of 3D correlation techniques.
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Abhijit Mahalanobis, Bhagavatula Vijaya Kumar, Alan J. Van Nevel, "Three-dimensional correlation filters for orientation invariant recognition", Proc. SPIE 4379, Automatic Target Recognition XI, (22 October 2001); doi: 10.1117/12.445396; https://doi.org/10.1117/12.445396
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