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Intervention planning is crucial for maintenance operations in particle accelerator environments with ionizing radiation, during which the radiation dose received by maintenance workers should be reduced to a minimum. In this context, we discuss the development of a new software tool and the entailed methodology, including the visualization aspects. The software tool integrates interactive exploration of a scene depicting an accelerator facility augmented with residual radiation level simulations, with the visualization of intervention data such as the followed trajectory and maintenance tasks. Its conception allows for future inclusion of measurements performed by mobile robotic devices. In this work, we explore the systems engineering life cycle of the development process of an interactive intervention planner, which includes the needs analysis, specification explicitation, conceptual mathematical modelling, iterative implementation, design and prototype testing and usability testing.

Intra-shape deformations complicate 3D object recognition and retrieval and need therefore proper modeling. A method for inelastic deformation invariant object recognition is proposed, representing 3D objects by diffusion distance tensors (DDT), i.e. third order tensors containing the average diffusion distance for different diffusion times between each pair of points on the surface. In addition to the DDT, also geodesic distance matrices (GDM) are used to represent the objects independent of the reference frame. Transforming these distance tensors into modal representations provides a sampling order invariant shape descriptor. Different dissimilarity measures can be used for comparing these shape descriptors. The final object pair dissimilarity is the sum or product of the dissimilarities obtained by modal representations of the GDM and DDT. The method is validated on the TOSCA non-rigid world database and the SHREC 2010 dataset of non-rigid 3D models indicating that our method combining these two representations provides a more noise robust but still inter-subject shape variation sensitive method for the identification and the verification scenario in object retrieval.

This paper presents a SIFT algorithm adapted for 3D surfaces (called meshSIFT) and its applications to 3D face pose normalisation and recognition. The algorithm allows reliable detection of scale space extrema as local feature locations. The scale space contains the mean curvature in each vertex on different smoothed versions of the input mesh. The meshSIFT algorithm then describes the neighbourhood of every scale space extremum in a feature vector consisting of concatenated histograms of shape indices and slant angles. The feature vectors are reliably matched by comparing the angle in feature space. Using RANSAC, the best rigid transformation can be estimated based on the matched features leading to 84% correct pose normalisation of 3D faces from the Bosphorus database. Matches are mostly found between two face surfaces of the same person, allowing the algorithm to be used for 3D face recognition. Simply counting the number of matches allows 93:7% correct identification for face surfaces in the Bosphorus database and 97:7% when only frontal images are considered. In the verification scenario, we obtain an equal error rate of 15:0% to 5:1% (depending on the investigated face surfaces). These results outperform most other algorithms found in literature. © 2010 IEEE. Maes C., Fabry T., Keustermans J., Smeets D., Suetens P., Vandermeulen D., ''Feature detection on 3D face surfaces for pose normalisation and recognition'', IEEE fourth international conference on biometrics : theory, applications and systems - BTAS 2010, 6 pp., September 27-29, 2010, Washington DC, USA. ispartof: pages:1-6 ispartof: Proceedings BTAS 2010 pages:1-6 ispartof: International conference on biometrics : theory, applications and systems - BTAS 2010 location:Washington DC, USA date:27 Sep - 29 Sep 2010 status: published

Currently, the recognition of faces under varying expressions is one of the main challenges in the face recognition community. In this paper a method is presented dealing with those expression variations by using an isometric deformation model. The method is built upon the geodesic distance matrix as a representation of the 3D face. We will show that the set of largest singular values is an excellent expression-invariant shape descriptor. Face comparison is performed by comparison of their shape descriptors using the mean normalized Manhattan distance as dissimilarity measure. The presented method is validated on a subset of 900 faces of the BU-3DFE face database resulting in an equal error rate of 13.37% for the verification scenario. This result is comparable with the equal error rates of other 3D expression-invariant face recognition methods using an isometric deformation model on the same database.

3D face recognition is a widely researched problem for wich many methods have been proposed. Unfortunately, most of these methods need vast prior knowledge in the form of large 3D face training sets. Also, many methods need point correspondences, which are hard to obtain. In this paper, we present a general 3D face recognition method based on point cloud kernel correlation and kernel density estimation for registration, which doesn't make use of a training set of faces or point correspondences and can handle noisy, unpreprocessed face scans. Initial experiments on the publicly available SHREC 2008 3D face database show good face recognition accuracy and robust behavior across face expressions.