• Publications
  • Influence
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
  • S. Geman, D. Geman
  • Physics
    IEEE Transactions on Pattern Analysis and Machine…
  • 1 November 1984
TLDR
The analogy between images and statistical mechanics systems is made and the analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations, creating a highly parallel ``relaxation'' algorithm for MAP estimation.
Constrained Restoration and the Recovery of Discontinuities
TLDR
The authors examine prior smoothness constraints of a different form, which permit the recovery of discontinuities without introducing auxiliary variables for marking the location of jumps and suspending the constraints in their vicinity.
Nonlinear image recovery with half-quadratic regularization
TLDR
This approach is based on an auxiliary array and an extended objective function in which the original variables appear quadratically and the auxiliary variables are decoupled, and yields the original function so that the original image estimate can be obtained by joint minimization.
Shape Quantization and Recognition with Randomized Trees
TLDR
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, and a comparison with artificial neural networks methods is presented.
Tackling the widespread and critical impact of batch effects in high-throughput data
TLDR
It is argued that batch effects (as well as other technical and biological artefacts) are widespread and critical to address and experimental and computational approaches for doing so are reviewed.
Boundary Detection by Constrained Optimization
TLDR
A statistical framework is used for finding boundaries and for partitioning scenes into homogeneous regions and incorporates a measure of disparity between certain spatial features of block pairs of pixel gray levels, using the Kolmogorov-Smirnov nonparametric measures of difference between the distributions of these features.
Classifying Gene Expression Profiles from Pairwise mRNA Comparisons
TLDR
The TSP classifier achieves prediction rates with standard cancer data that are as high as those of previous studies which use considerably more genes and complex procedures and is parameter-free, thus avoiding the type of over-fitting and inflated estimates of performance that result when all aspects of learning a predictor are not properly cross-validated.
Simple decision rules for classifying human cancers from gene expression profiles
TLDR
The k-TSP classifier performs as efficiently as Prediction Analysis of Microarray and support vector machine, and outperforms other learning methods (decision trees, k-nearest neighbour and naïve Bayes) and is easy to interpret as the classifier involves only a small number of informative genes.
An Active Testing Model for Tracking Roads in Satellite Images
TLDR
A new approach for tracking roads from satellite images, and thereby illustrate a general computational strategy for tracking 1D structures and other recognition tasks in computer vision, related to recent work in active vision and motivated by the "divide-and-conquer" strategy of parlour games.
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