Institut de Recherche en Informatique et Systèmes Aléatoires

About: Institut de Recherche en Informatique et Systèmes Aléatoires is a education organization based out in Rennes, France. It is known for research contribution in the topics: Computer science & Visual servoing. The organization has 700 authors who have published 793 publications receiving 11112 citations.

An Optimized Blockwise Nonlocal Means Denoising Filter for 3-D Magnetic Resonance Images

Abstract: A critical issue in image restoration is the problem of noise removal while keeping the integrity of relevant image information. Denoising is a crucial step to increase image quality and to improve the performance of all the tasks needed for quantitative imaging analysis. The method proposed in this paper is based on a 3-D optimized blockwise version of the nonlocal (NL)-means filter (Buades, , 2005). The NL-means filter uses the redundancy of information in the image under study to remove the noise. The performance of the NL-means filter has been already demonstrated for 2-D images, but reducing the computational burden is a critical aspect to extend the method to 3-D images. To overcome this problem, we propose improvements to reduce the computational complexity. These different improvements allow to drastically divide the computational time while preserving the performances of the NL-means filter. A fully automated and optimized version of the NL-means filter is then presented. Our contributions to the NL-means filter are: 1) an automatic tuning of the smoothing parameter; 2) a selection of the most relevant voxels; 3) a blockwise implementation; and 4) a parallelized computation. Quantitative validation was carried out on synthetic datasets generated with BrainWeb (Collins, , 1998). The results show that our optimized NL-means filter outperforms the classical implementation of the NL-means filter, as well as two other classical denoising methods [anisotropic diffusion (Perona and Malik, 1990)] and total variation minimization process (Rudin, , 1992) in terms of accuracy (measured by the peak signal-to-noise ratio) with low computation time. Finally, qualitative results on real data are presented.

Making components contract aware

Abstract: Components have long promised to encapsulate data and programs into a box that operates predictably without requiring that users know the specifics of how it does so. Many advocates have predicted that components will bring about widespread software reuse, spawning a market for components usable with such mainstream software buses as the Common Object Request Broker Architecture (CORBA) and the Distributed Component Object Model (DCOM). In the Windows world, at least, this prediction is becoming a reality. Yet recent reports indicate mixed results when using and reusing components in mission-critical settings. Such results raise disturbing questions. How can you trust a component? What if the component behaves unexpectedly, either because it is faulty or simply because you misused it? Before we can trust a component in mission-critical applications, we must be able to determine, reliably and in advance, how it will behave. In this article the authors define a general model of sofware contracts and show how existing mechanisms could be used to turn traditional components into contract-aware ones.

Sequential Analysis: Hypothesis Testing and Changepoint Detection

Abstract: Sequential Analysis: Hypothesis Testing and Changepoint Detection systematically develops the theory of sequential hypothesis testing and quickest changepoint detection. It also describes important applications in which theoretical results can be used efficiently. The book reviews recent accomplishments in hypothesis testing and changepoint detection both in decision-theoretic (Bayesian) and non-decision-theoretic (non-Bayesian) contexts. The authors not only emphasize traditional binary hypotheses but also substantially more difficult multiple decision problems. They address scenarios with simple hypotheses and more realistic cases of two and finitely many composite hypotheses. The book primarily focuses on practical discrete-time models, with certain continuous-time models also examined when general results can be obtained very similarly in both cases. It treats both conventional i.i.d. and general non-i.i.d. stochastic models in detail, including Markov, hidden Markov, state-space, regression, and autoregression models. Rigorous proofs are given for the most important results. Written by leading authorities in the field, this book covers the theoretical developments and applications of sequential hypothesis testing and sequential quickest changepoint detection in a wide range of engineering and environmental domains. It explains how the theoretical aspects influence the hypothesis testing and changepoint detection problems as well as the design of algorithms.

A Library for doing polyhedral operations

Abstract: The design and implementation of a library of C-code procedures to perform operations on rational polyhedra is described. The library supports intersection, union, difference, simplification in context, convex hull, affine image, affine preimage, and computation of dual forms. Since not all of these functions are closed over polyhedra, the library is extended to operate on finite unions of polyhedra. The major design decisions made during the implementation of the library are discussed. The data structure used for representing finite unions of polyhedra is developed and validity rules for the representation of polyhedra are derived. And finally, the algorithms used to implement the various functions in the library are presented.

Prediction of functional residues in water channels and related proteins.

Abstract: In this paper, we present an updated classification of the ubiquitous MIP (Major Intrinsic Protein) family proteins, including 153 fully or partially sequenced members available in public databases. Presently, about 30 of these proteins have been functionally characterized, exhibiting essentially two distinct types of channel properties: (1) specific water transport by the aquaporins, and (2) small neutral solutes transport, such as glycerol by the glycerol facilitators. Sequence alignments were used to predict amino acids and motifs discriminant in channel specificity. The protein sequences were also analyzed using statistical tools (comparisons of means and correspondence analysis). Five key positions were clearly identified where the residues are specific for each functional subgroup and exhibit high dissimilar physico-chemical properties. Moreover, we have found that the putative channels for small neutral solutes clearly differ from the aquaporins by the amino acid content and the length of predicted loop regions, suggesting a substrate filter function for these loops. From these results, we propose a signature pattern for water transport.