Variational tetrahedral meshing

In this paper, a novel Delaunay-based variational approach to isotropic tetrahedral meshing is presented. To achieve both robustness and efficiency, we minimize a simple mesh-dependent energy through global updates of both vertex positions and connectivity. As this energy is known to be the ∠1 distance between an isotropic quadratic function and its linear interpolation on the mesh, our minimization procedure generates well-shaped tetrahedra. Mesh design is controlled through a gradation smoothness parameter and selection of the desired number of vertices. We provide the foundations of our approach by explaining both the underlying variational principle and its geometric interpretation. We demonstrate the quality of the resulting meshes through a series of examples

Usefulness of Semi-Automatic Tools for Airborne Minefield Detection

Potentialities of airborne minefield detection are nowadays investigated. Such approaches are based on the detection of minefield indicators on mono or multi-sensor airborne images. Most of the indicators will have to be located in vast area using high-resolution imagery. This leads to a tremendous amount of data to be interpreted. Hence semi-automatic detection techniques must be used to pre-filter the input information flux or the user will be flooded by the incoming data. In this paper, we illustrate and evaluate such a semi-automatic technique for the detection of un-obscured anti-tank mines. The potentiality of semi-automatic tools for the detection of other mine indicators is also discussed. Introduction Landmines are the cause of a huge and world-wide humanitarian disaster as about 110 million landmines are scattered in 64 countries around the globe. The currently used technology is not efficient. Therefore de-mining campaigns are slow and expensive. The major problems are lin..

Brussels

This paper describes a new method for a feature-based supervised classification of multi-channel SAR data. Classic feature selection and classification methods are inadequate due to the diverse statistical distributions of the input features. A method based on logistic regression (LR) and multinomial logistic regression (MNLR) for separating different classes is therefore proposed. Both methods, LR and MNLR, are less dependent on the statistical distribution of the input data. A new spatial regularization method is also introduced to increase consistency of the classification result. The classification method was applied to a project on humanitarian demining in which the relevant classes were defined by experts of a Mine Action Center. A ground survey mission collected learning and validation samples for each class. Results of the proposed classification methods are shown and compared to a maximum likelihood classifier

Space and Airborne Mined Area Reduction Tool (SMART)

Final Reportinfo:eu-repo/semantics/publishe

Final Report, Space and Airborne Mined Area Reduction Tools, project SMART, European Commission IST-2000-25044

Final Report, Space and Airborne Mined Area Reduction Tools, project SMART, European Commission IST-2000-25044, V3, Classification: Public, 20.04. 2005, 46 p. URL: http://www. smart. rma. ac. be.info:eu-repo/semantics/publishe