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Bobby R. Hunt
Researcher at University of Arizona
Publications - 88
Citations - 3085
Bobby R. Hunt is an academic researcher from University of Arizona. The author has contributed to research in topics: Image restoration & Image processing. The author has an hindex of 24, co-authored 86 publications receiving 2989 citations. Previous affiliations of Bobby R. Hunt include Raytheon.
Papers
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Proceedings ArticleDOI
Pan-STARRS: a large synoptic survey telescope array
Nick Kaiser,Herve Aussel,Barry E. Burke,Hans Boesgaard,K. C. Chambers,Mark Chun,J. N. Heasley,Klaus-Werner Hodapp,Bobby R. Hunt,Robert Jedicke,David Jewitt,R.-P. Kudritzki,Gerard Anthony Luppino,Michael Maberry,Eugene A. Magnier,David G. Monet,Peter M. Onaka,Andrew J. Pickles,P. H. Rhoads,Theodore Simon,Alexander S. Szalay,István Szapudi,David J. Tholen,John L. Tonry,Mark Waterson,John Wick +25 more
TL;DR: In this article, the authors describe the motivation for a distributed aperture approach for the LSST, the current concept for Pan-STARRS, and its performance goals and science reach.
Patent
Pattern recognition system
Timothy L. Hutcheson,Wilson Or,Venkatesh Narayanan,Subramaniam Mohan,Peter G. Wohlmut,Ramanujam Srinivasan,Bobby R. Hunt,Thomas W. Ryan +7 more
TL;DR: In this article, a feature vector consisting of the highest order (most discriminatory) magnitude information from the power spectrum of the Fourier transform of the image is formed, and the output vector is subjected to statistical analysis to determine if a sufficiently high confidence level exists to indicate that a successful identification has been made.
Journal ArticleDOI
Karhunen-Loeve multispectral image restoration, part I: Theory
Bobby R. Hunt,O. Kubler +1 more
TL;DR: It is shown that optimal restoration must take place in the Karhunen-Loeve domain and that high quality approximate multispectral restorations must be achieved at less computational cost than exact restoration.
Journal ArticleDOI
Compression of hyperspectral imagery using the 3-D DCT and hybrid DPCM/DCT
TL;DR: Two systems are presented for compression of hyperspectral imagery which utilize trellis coded quantization (TCQ) and DPCM to spectrally decorrelate the data, while a 2D DCT coding scheme is used for spatial decorrelation.
Patent
Apparatus for generating a feature matrix based on normalized out-class and in-class variation matrices
Timothy L. Hutcheson,Wilson Or,Venkatesh Narayanan,Subramaniam Mohan,Peter G. Wohlmut,Ramanujam Srinivasan,Bobby R. Hunt,Thomas W. Ryan +7 more
TL;DR: In this paper, a feature vector consisting of the highest order (most discriminatory) magnitude information from the power spectrum of the Fourier transform of the image is formed, and the output vector is subjected to statistical analysis to determine if a sufficiently high confidence level exists to indicate that a successful identification has been made.