N
Neal R. Harvey
Researcher at Los Alamos National Laboratory
Publications - 56
Citations - 965
Neal R. Harvey is an academic researcher from Los Alamos National Laboratory. The author has contributed to research in topics: Image processing & Feature extraction. The author has an hindex of 18, co-authored 56 publications receiving 947 citations. Previous affiliations of Neal R. Harvey include University of Strathclyde.
Papers
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Journal ArticleDOI
Comparison of GENIE and conventional supervised classifiers for multispectral image feature extraction
Neal R. Harvey,James Theiler,Steven P. Brumby,Simon Perkins,John J. Szymanski,Jeffrey J. Bloch,Reid B. Porter,M. Galassi,Aaron Cody Young +8 more
TL;DR: The authors describe their system in detail together with experiments involving comparisons of GENIE with several conventional supervised classification techniques, for a number of classification tasks using multispectral remotely sensed imagery.
Proceedings ArticleDOI
Investigation of image feature extraction by a genetic algorithm
Steven P. Brumby,James Theiler,Simon Perkins,Neal R. Harvey,John J. Szymanski,Jeffrey J. Bloch,Melanie Mitchell +6 more
TL;DR: The implementation and performance of a genetic algorithm which generates image feature extraction algorithms for remote sensing applications and the basis set of primitive image operators and chromosomal representation of a complete algorithm are described.
Journal ArticleDOI
Utility of multispectral imaging for nuclear classification of routine clinical histopathology imagery.
Laura E. Boucheron,Laura E. Boucheron,Zhiqiang Bi,Neal R. Harvey,B.S. Manjunath,David L. Rimm +5 more
TL;DR: The results indicate that multispectral imagery for routine H&E stained histopathology provides minimal additional spectral information for a pixel-level nuclear classification task than would standard RGB imagery.
Proceedings ArticleDOI
GENIE: a hybrid genetic algorithm for feature classification in multispectral images
Simon Perkins,James Theiler,Steven P. Brumby,Neal R. Harvey,Reid B. Porter,John J. Szymanski,Jeffrey J. Bloch +6 more
TL;DR: Genie as mentioned in this paper is a hybrid learning system that combines a GA and a more conventional classifier to output a final classification. But the GA alone is not sufficient to correctly classify a pixel.
Proceedings ArticleDOI
Using blocks of skewers for faster computation of pixel purity index
TL;DR: A variant of the PPI algorithm in which blocks of B skewers are considered at a time, and a hardware implementation on a field programmable gate array (FPGA) processor both of the original PPI algorithms and of the block-skewer approach are discussed.