Donald Geman
Donald Geman
Johns Hopkins University, Center for Imaging Science
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Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images
S Geman, D Geman
IEEE Transactions on pattern analysis and machine intelligence, 721-741, 1984
Tackling the widespread and critical impact of batch effects in high-throughput data
JT Leek, RB Scharpf, HC Bravo, D Simcha, B Langmead, WE Johnson, ...
Nature Reviews Genetics 11 (10), 733-739, 2010
Constrained restoration and the recovery of discontinuities
D Geman, G Reynolds
IEEE Transactions on pattern analysis and machine intelligence 14 (3), 367-383, 1992
Shape quantization and recognition with randomized trees
Y Amit, D Geman
Neural computation 9 (7), 1545-1588, 1997
Nonlinear image recovery with half-quadratic regularization
D Geman, C Yang
IEEE transactions on Image Processing 4 (7), 932-946, 1995
Fundamentals of stochastic filtering
A Bain, D Crisan
Springer Science & Business Media, 2008
Boundary detection by constrained optimization
D Geman, S Geman, C Graffigne, P Dong
IEEE Transactions on pattern analysis and machine intelligence 12 (7), 609-628, 1990
An active testing model for tracking roads in satellite images
D Geman, B Jedynak
IEEE Transactions on Pattern Analysis and Machine Intelligence 18 (1), 1-14, 1996
Occupation densities
D Geman, J Horowitz
The Annals of Probability, 1-67, 1980
Simple decision rules for classifying human cancers from gene expression profiles
AC Tan, DQ Naiman, L Xu, RL Winslow, D Geman
Bioinformatics 21 (20), 3896-3904, 2005
Random fields and inverse problems in imaging
D Geman
École d'été de probabilités de Saint-Flour XVIII-1988, 115-193, 1990
Coarse-to-fine face detection
F Fleuret, D Geman
International Journal of computer vision 41 (1), 85-107, 2001
Classifying gene expression profiles from pairwise mRNA comparisons
D Geman, C d'Avignon, DQ Naiman, RL Winslow
Statistical applications in genetics and molecular biology 3 (1), 2004
Bayes smoothing algorithms for segmentation of binary images modeled by Markov random fields
H Derin, H Elliott, R Cristi, D Geman
IEEE Transactions on Pattern Analysis and Machine Intelligence, 707-720, 1984
Feature Selection for Unsupervised and Supervised Inference: The Emergence of Sparsity in a Weight-Based Approach.
L Wolf, A Shashua, D Geman
Journal of Machine Learning Research 6 (11), 2005
Joint induction of shape features and tree classifiers
Y Amit, D Geman, K Wilder
IEEE transactions on pattern analysis and machine intelligence 19 (11), 1300 …, 1997
Visual turing test for computer vision systems
D Geman, S Geman, N Hallonquist, L Younes
Proceedings of the National Academy of Sciences 112 (12), 3618-3623, 2015
A computational model for visual selection
Y Amit, D Geman
Neural computation 11 (7), 1691-1715, 1999
A coarse-to-fine strategy for multiclass shape detection
Y Amit, D Geman, X Fan
IEEE Transactions on Pattern Analysis and Machine Intelligence 26 (12), 1606 …, 2004
Robust prostate cancer marker genes emerge from direct integration of inter-study microarray data
L Xu, AC Tan, DQ Naiman, D Geman, RL Winslow
Bioinformatics 21 (20), 3905-3911, 2005
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