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Proceedings Paper

Image processing and low-discrepancy sequences
Author(s): Dinesh Nair; Lothar Wenzel
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Paper Abstract

It is well known that high-dimensional integral can be solved with Monte Carlo algorithms. Recently, it was discovered that there is a relationship between low discrepancy sets and the efficient evaluation of higher-dimensional integral. Theory suggests that for midsize dimensional problems, algorithms based on low discrepancy sets should outperform all other existing methods by an order of magnitude in terms of the number of sample points used to evaluate the integral. We show that the field of image processing can potentially take advantage of specific properties of low discrepancy sets. To illustrate this, we applied the theory of low discrepancy sequences to some relatively simple image processing and computer vision related operations such as the estimation of gray level image statistics, fast location of objects in a binary image and the reconstruction of images from a sparse set of points. Our experiments show that compared to standard methods, the proposed new algorithms are faster and statistically more robust. Classical low discrepancy sets based on the Halton and Sobol' sequences were investigated thoroughly and showed promising results. The use of low discrepancy sequences in image processing for image characterization, understanding and object recognition is a novel and promising area for further investigation.

Paper Details

Date Published: 2 November 1999
PDF: 10 pages
Proc. SPIE 3807, Advanced Signal Processing Algorithms, Architectures, and Implementations IX, (2 November 1999); doi: 10.1117/12.367625
Show Author Affiliations
Dinesh Nair, National Instruments (United States)
Lothar Wenzel, National Instruments (United States)

Published in SPIE Proceedings Vol. 3807:
Advanced Signal Processing Algorithms, Architectures, and Implementations IX
Franklin T. Luk, Editor(s)

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