Le projet DYLAN ou les enjeux politiques, cognitifs et stratégiques du plurilinguisme

Composed of 19 universities from 12 countries, the Dylan Project is an integrated project (IP) of the 6th Framework Programme of the European Union, under Priority 7 « Citizenship and governance in a knowledge-based society ».The project addresses the core issue underlying topic 3.3.1: establish whether and how a European knowledge based society designed to ensure economic competitiveness and social cohesion can be created within a European Union that is linguistically more diverse than ever. The overarching objectives are to show that, in this respect, the linguistic diversity prevalent in Europe is potentially an asset rather than an obstacle and to identify the conditions under which individual and societal multilingualism can be turned to advantage.It will show in what ways the usage of multilingual repertoires can promote the creation and the transfer of knowledge (cognitive asset) and have an impact on communication control, problem solving and decision making (strategic asset), in the diversity of economic, political and educational contexts

A Fisher-Rao Metric for curves using the information in edges

Two curves which are close together in an image are indistinguishable given a measurement, in that there is no compelling reason to associate the measurement with one curve rather than the other. This observation is made quantitative using the parametric version of the Fisher-Rao metric. A probability density function for a measurement conditional on a curve is constructed. The distance between two curves is then defined to be the Fisher-Rao distance between the two conditional pdfs. A tractable approximation to the Fisher-Rao metric is obtained for the case in which the measurements are compound in that they consist of a point x and an angle α which specifies the direction of an edge at x. If the curves are circles or straight lines, then the approximating metric is generalized to take account of inlying and outlying measurements. An estimate is made of the number of measurements required for the accurate location of a circle in the presence of outliers. A Bayesian algorithm for circle detection is defined. The prior density for the algorithm is obtained from the Fisher-Rao metric. The algorithm is tested on images from the CASIA Iris Interval database

The MIN PFS problem and piecewise linear model estimation

AbstractWe consider a new combinatorial optimization problem related to linear systems (MIN PFS) that consists, given an infeasible system, in finding a partition into a minimum number of feasible subsystems. MIN PFS allows formalization of the fundamental problem of piecewise linear model estimation, which is an attractive alternative when modeling a wide range of nonlinear phenomena. Since MIN PFS turns out to be NP-hard to approximate within every factor strictly smaller than 3/2 and we are mainly interested in real-time applications, we propose a greedy strategy based on randomized and thermal variants of the classical Agmon–Motzkin–Schoenberg relaxation method for solving systems of linear inequalities. Our method provides good approximate solutions in a short amount of time. The potential of our approach and the performance of our algorithm are demonstrated on two challenging problems from image and signal processing. The first one is that of detecting line segments in digital images and the second one that of modeling time-series using piecewise linear autoregressive models. In both cases the MIN PFS-based approach presents various advantages with respect to conventional alternatives, including wider range of applicability, lower computational requirements and no need for a priori assumptions regarding the underlying structure of the data

Active intelligent vision using the dynamic generalized Hough transform

Parametric transformation is a powerful tool in shape analysis which gives good results even in the presence of noise and occlusion. Major shortcomings of the technique are excessive storage requirements, computational complexity and the need to initiate a separate transformation process with respect to each feature under detection. In addition, standard parametric transformation processes the entire image treating each image point independently. The proposed method selectively segments the image and provides a feedback mechanism linking image and transform space. Decisions are made concerning the probable instance of shape under detection, the viability of processing and the need to gather further evidence from the image