Search results for "Gaussian"
showing 10 items of 652 documents
Quality-preserving low-cost probabilistic 3D denoising with applications to Computed Tomography
2021
AbstractWe propose a pipeline for a synthetic generation of personalized Computer Tomography (CT) images, with a radiation exposure evaluation and a lifetime attributable risk (LAR) assessment. We perform a patient-specific performance evaluation for a broad range of denoising algorithms (including the most popular Deep Learning denoising approaches, wavelets-based methods, methods based on Mumford-Shah denoising etc.), focusing both on accessing the capability to reduce the patient-specific CT-induced LAR and on computational cost scalability. We introduce a parallel probabilistic Mumford-Shah denoising model (PMS), showing that it markedly-outperforms the compared common denoising methods…
Classification Boundary Approximation by Using Combination of Training Steps for Real-Time Image Segmentation
2007
We propose a method of real-time implementation of an approximation of the support vector machine decision rule. The method uses an improvement of a supervised classification method based on hyperrectangles, which is useful for real-time image segmentation. We increase the classification and speed performances using a combination of classification methods: a support vector machine is used during a pre-processing step. We recall the principles of the classification methods and we evaluate the hardware implementation cost of each method. We present our learning step combination algorithm and results obtained using Gaussian distributions and an example of image segmentation coming from a part …
Gaussian Process Regression (GPR) Representation in Predictive Model Markup Language (PMML)
2017
International audience; This paper describes Gaussian process regression (GPR) models presented in predictive model markup language (PMML). PMML is an extensible-markup-language (XML) -based standard language used to represent data-mining and predictive analytic models, as well as pre- and post-processed data. The previous PMML version, PMML 4.2, did not provide capabilities for representing probabilistic (stochastic) machine-learning algorithms that are widely used for constructing predictive models taking the associated uncertainties into consideration. The newly released PMML version 4.3, which includes the GPR model, provides new features: confidence bounds and distribution for the pred…
Conjugacy problem for braid groups and Garside groups
2003
We present a new algorithm to solve the conjugacy problem in Artin braid groups, which is faster than the one presented by Birman, Ko and Lee. This algorithm can be applied not only to braid groups, but to all Garside groups (which include finite type Artin groups and torus knot groups among others).
Dynamic factorial graphical models for dynamic networks
2014
Dynamic networks models describe a growing number of important scientific processes, from cell biology and epidemiology to sociology and finance. Estimating dynamic networks from noisy time series data is a difficult task since the number of components involved in the system is very large. As a result, the number of parameters to be estimated is typically larger than the number of observations. However, a characteristic of many real life networks is that they are sparse. For example, the molec- ular structure of genes make interactions with other components a highly-structured and, therefore, a sparse process. Penalized Gaussian graphical models have been used to estimate sparse networks. H…
PCA Gaussianization for image processing
2009
The estimation of high-dimensional probability density functions (PDFs) is not an easy task for many image processing applications. The linear models assumed by widely used transforms are often quite restrictive to describe the PDF of natural images. In fact, additional non-linear processing is needed to overcome the limitations of the model. On the contrary, the class of techniques collectively known as projection pursuit, which solve the high-dimensional problem by sequential univariate solutions, may be applied to very general PDFs (e.g. iterative Gaussianization procedures). However, the associated computational cost has prevented their extensive use in image processing. In this work, w…
Experiments with an adaptive Bayesian restoration method
1989
Abstract This paper describes a Bayesian restoration method applied to two-dimensional measured images, whose detector response function is not completely known. The response function is assumed Gaussian with standard deviation depending on the estimate of the local density of the image. The convex hull of the K -nearest neighbours ( K NN) of each ‘on’ pixel is used to compute the local density. The method has been tested on ‘sparse’ images, with and without noise background.
Syntheses, Structures, Magnetic Properties, and Density Functional Theory Magneto-Structural Correlations of Bis(μ-phenoxo) and Bis(μ-phenoxo)-μ-acet…
2013
The bis(mu-phenoxo) (FeNiIII)-Ni-II compound [Fe-III(N-3)(2)LNiII(H2O)(CH3CN)](ClO4) (1) and the bis(mu-phenoxo)-mu-acetate/bis(mu-phenoxo)-bis(mu-acetate) (FeNiII)-Ni-III compound {[Fe-III(OAc)LNiII(H2O)(mu-OAc)](0.6)center dot[(FeLNiII)-L-III(mu-OAc)(2)](0.4)}(ClO4)center dot 1.1H(2)O (2) have been synthesized from the Robson type tetraiminodiphenol macrocyclic ligand H2L, which is the [2 + 2] condensation product of 4-methyl-2,6-diformylphenol and 2,2'-dimethy1-1,3-diaminopropane. Single-crystal X-ray structures of both compounds have been determined. The cationic part of the dinuclear compound 2 is a cocrystal of the two species [Fe-III(OAc)LNiII(H2O)(mu-OAc)](+) (2A) and [(FeLNiII)-L-I…
Poor-Contrast Particle Image Processing in Microscale Mixing
2010
Particle image velocimetry (PIV) often employs the cross-correlation function to identify average particle displacement in an interrogation window. The quality of correlation peak has a strong dependence on the signal-to-noise ratio (SNR), or contrast of the particle images. In fact, variable-contrast particle images are not uncommon in the PIV community: Strong light sheet intensity variations, wall reflections, multiple scattering in densely-seeded regions and two-phase flow applications are likely sources of local contrast variations. In this paper, we choose an image pair obtained in a micro-scale mixing experiment with severe local contrast gradients. In regions where image contrast is…
Sparse model-based network inference using Gaussian graphical models
2010
We consider the problem of estimating a sparse dynamic Gaussian graphical model with L1 penalized maximum likelihood of structured precision matrix. The structure can consist of specific time dynamics, known presence or absence of links in the graphical model or equality constraints on the parameters. The model is defined on the basis of partial correlations, which results in a specific class precision matrices. A priori L1 penalized maximum likelihood estimation in this class is extremely difficult, because of the above mentioned constraints, the computational complexity of the L1 constraint on the side of the usual positive-definite constraint. The implementation is non-trivial, but we sh…