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Proceedings Paper

Effect of data truncation in an implementation of pixel clustering on a custom computing machine
Author(s): Miriam E. Leeser; James P. Theiler; Michael Estlick; Natalya V. Kitaryeva; John J. Szymanski
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Paper Abstract

We investigate the effect of truncating the precision of hyperspectral image data for the purpose of more efficiently segmenting the image using a variant of k-means clustering. We describe the implementation of the algorithm on field-programmable gate array (FPGA) hardware. Truncating the data to only a few bits per pixel in each spectral channel permits a more compact hardware design, enabling greater parallelism, and ultimately a more rapid execution. It also enables the storage of larger images in the onboard memory. In exchange for faster clustering, however, one trades off the quality of the produced segmentation. We find, however, that the clustering algorithm can tolerate considerable data truncation with little degradation in cluster quality. This robustness to truncated data can be extended by computing the cluster centers to a few more bits of precision than the data. Since there are so many more pixels than centers, the more aggressive data truncation leads to significant gains in the number of pixels that can be stored in memory and processed in hardware concurrently.

Paper Details

Date Published: 6 October 2000
PDF: 10 pages
Proc. SPIE 4212, Reconfigurable Technology: FPGAs for Computing and Applications II, (6 October 2000); doi: 10.1117/12.402511
Show Author Affiliations
Miriam E. Leeser, Northeastern Univ. (United States)
James P. Theiler, Los Alamos National Lab. (United States)
Michael Estlick, Northeastern Univ. (United States)
Natalya V. Kitaryeva, Northeastern Univ. (United States)
John J. Szymanski, Los Alamos National Lab. (United States)

Published in SPIE Proceedings Vol. 4212:
Reconfigurable Technology: FPGAs for Computing and Applications II
John Schewel; Peter M. Athanas; Chris H. Dick; John T. McHenry, Editor(s)

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