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Title: EFFECT OF DATA TRUNCATION IN AN IMPLEMENTATION OF PIXEL CLUSTERING ON A CUSTOM COMPUTING MACHINE

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.

Authors:
;
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
US Department of Energy (US)
OSTI Identifier:
768734
Report Number(s):
LA-UR-00-3965
TRN: AH200123%%341
DOE Contract Number:  
W-7405-ENG-36
Resource Type:
Conference
Resource Relation:
Conference: Conference title not supplied, Conference location not supplied, Conference dates not supplied; Other Information: PBD: 1 Aug 2000
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; ALGORITHMS; DESIGN; IMPLEMENTATION; MEMORY MANAGEMENT; SPECTRA; IMAGES

Citation Formats

LEESER, M, THEILER, J, and ET AL. EFFECT OF DATA TRUNCATION IN AN IMPLEMENTATION OF PIXEL CLUSTERING ON A CUSTOM COMPUTING MACHINE. United States: N. p., 2000. Web.
LEESER, M, THEILER, J, & ET AL. EFFECT OF DATA TRUNCATION IN AN IMPLEMENTATION OF PIXEL CLUSTERING ON A CUSTOM COMPUTING MACHINE. United States.
LEESER, M, THEILER, J, and ET AL. 2000. "EFFECT OF DATA TRUNCATION IN AN IMPLEMENTATION OF PIXEL CLUSTERING ON A CUSTOM COMPUTING MACHINE". United States. https://www.osti.gov/servlets/purl/768734.
@article{osti_768734,
title = {EFFECT OF DATA TRUNCATION IN AN IMPLEMENTATION OF PIXEL CLUSTERING ON A CUSTOM COMPUTING MACHINE},
author = {LEESER, M and THEILER, J and ET AL},
abstractNote = {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.},
doi = {},
url = {https://www.osti.gov/biblio/768734}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Aug 01 00:00:00 EDT 2000},
month = {Tue Aug 01 00:00:00 EDT 2000}
}

Conference:
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