A generic methodology for partitioning unorganised 3D point clouds for robotic vision

International audienceRange image segmentation has many applications in computer vision areas such as computer graphics and robotic vision. A generic methodology for 3D point set analysis in which planar structures play an important role is defined. It consists mainly of a specific K-means algorithm which is able to process different shapes in cluster. At the same time, within geometric and topologic considerations, a set of application-driven heuristics is designed. This helps to find out the right number of structures in point sets in order to give a good visualization and representation of a large scale environment without a priori models. Our aim is to propose a simple and generic frame for 3D scene understanding. Tests were realised on different types of environment data: natural and man-made. This research project has been realized with EADS (French Air Space Society)

Extraction of cartographic objects in high resolution satellite images for object model generation

The aim of this study is to detect man-made cartographic objects in high-resolution satellite images. New generation satellites offer a sub-metric spatial resolution, in which it is possible (and necessary) to develop methods at object level rather than at pixel level, and to exploit structural features of objects. With this aim, a method to generate structural object models from manually segmented images has been developed. To generate the model from non-segmented images, extraction of the objects from the sample images is required. A hybrid method of extraction (both in terms of input sources and segmentation algorithms) is proposed: A region based segmentation is applied on a 10 meter resolution multi-spectral image. The result is used as marker in a "marker-controlled watershed method using edges" on a 2.5 meter resolution panchromatic image. Very promising results have been obtained even on images where the limits of the target objects are not apparent

AN AUTOMATIC SYSTEM FOR THE ANALYSIS OF INTERCELLULAR COMMUNICATION AND EARLY CARCINOGENESIS

International audienceSome recent works on intercellular communication pointed out an impaired trafficking of Cx43 proteins in early carcinogenesis. In collaboration with biologists, we propose an automatic system for the analysis of spatial protein configurations within cells at early tumor stages. This system is an essential step towards the future development of a computer-aided diagnosis tool and the statistical validation of biological hypotheses about Cx43 expressions and configurations during tumorogenesis. The proposed system contains two dependent part: a segmentation part in which the cell structures of interest are automatically located on images and a characterization part in which some spatial features are computed for the classification of cells. Using immunofluorescent images of cells, the nucleus, cytoplasm and proteins structures within the cell are extracted. Then, some spatial features are computed to characterize spatial configurations of the proteins with regard to the nucleus and cytoplasm areas in the image. Last, the 3D cell images are classified into pathogenic or viable classes. The system has been quantitatively evaluated over 60 cell images acquired by a deconvolution high-resolution microscope and whose ground truth has been manually given by a biologist expert. As a perspective, a 3D spatial reasoning and visualization module is currently under development

Interprétation de nuages de points : application à la modélisaion d'environnements 3D en robotique mobile

This thesis work deals with the analysis of 3D unorganized point sets and relies on two main tools : an efficient partitioning algorithm inspired by the fuzzy K-means formalism on one hand, and morphological mesh filterings algorithms on the other hand. This framework works without models in an unknown environment.Cette thèse traite de l'analyse de nuages de points 3D désorganisé et s'appuie sur deux outils : un algorithme de partitionnement efficace inspiré des C-moyennes floues d'une part, et des outils de filtrage morphologique de représentation à base de triangulation de Delaunay d'autre part. Le cadre applicatif essentiel est la navigation autonome en robotique mobile en environnement inconnu, c'est-à-dire dans modèle. Mais la méthodologie générique développée a été appliquée à d'autres types d'environnements, notamment plus structurés