• Publications
  • Influence
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
  • S. Geman, D. Geman
  • Mathematics, Computer Science
  • IEEE Transactions on Pattern Analysis and Machine…
  • 1 November 1984
TLDR
We make an analogy between images and statistical mechanics systems. Expand
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Constrained Restoration and the Recovery of Discontinuities
TLDR
The linear image restoration problem is to recover an original brightness distribution X/sup0/ given the blurred and noisy observations Y=KX/sup 0/+B, where K and B represent the point spread function and measurement error, respectively. Expand
  • 1,154
  • 82
Shape Quantization and Recognition with Randomized Trees
  • Y. Amit, D. Geman
  • Computer Science, Mathematics
  • Neural Computation
  • 1 October 1997
TLDR
We explore a new approach to shape recognition based on a virtually infinite family of binary features (queries) of the image data, designed to accommodate prior information about shape invariance and regularity. Expand
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Nonlinear image recovery with half-quadratic regularization
TLDR
We use an auxiliary array and an extended objective function in which the original variables appear quadratically and the auxiliary variables are decoupled. Expand
  • 722
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Boundary Detection by Constrained Optimization
TLDR
A statistical framework is used for finding boundaries and for partitioning scenes into homogeneous regions. Expand
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Tackling the widespread and critical impact of batch effects in high-throughput data
High-throughput technologies are widely used, for example to assay genetic variants, gene and protein expression, and epigenetic modifications. One often overlooked complication with such studies isExpand
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Simple decision rules for classifying human cancers from gene expression profiles
TLDR
MOTIVATION Various studies have shown that cancer tissue samples can be successfully detected and classified by their gene expression patterns using machine learning approaches. Expand
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Classifying Gene Expression Profiles from Pairwise mRNA Comparisons
TLDR
We present a new approach to molecular classification based on mRNA comparisons. Expand
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