Support de cours pour le congres PIXIM 89. Version revisee. Segmentation d'images : ou en sommes nous ?

L'objectif de cet article est d'effectuer un etat de l'art des differentes methodes de segmentation d'images en se focalisant sur les derniers developpements. Une etape fondamentale dans la plupart des systemes de vision par ordinateur est d'engendrer une description compacte d'une image, plus exploitable que l'ensemble des pixels. De nombreuses techniques dites de "segmentation d'images" permettent d'atteindre cet objectif. Elles sont generalement fondees sur la recherche des discontinuites locales (detection de contours) ou sur la detection de zones de l'image presentant des caracteristiques d'homogeneite (extraction de regions). Ces deux approches sont duales en ce sens qu'une region definit une ligne par son contour et qu'une ligne fermee definit une region. Elles conduisent cependant a des algorithmes differents et ne fournissent pas les memes resultats. On caracterisera les differentes classes de methodes de segmentation d'images en contours ou en regions. Pour chaque type de methode un algorithme sera decrit en detail. On s'attachera plus particulierement aux techniques les plus recentes

From volume medical images to quadratic surface patches

Projet SYNTIMIn this paper, we show how to extract reliable informations about the shape of 3D objects, obtained from volume medical images . We present an optimal region-growing algorithm, that makes use of the differential characteristics of the object sur face, and achieves a stable segmentation into a set of patches of quadratic surfaces. We show how this segmentation can be used to recognize and locate a target sub-structure on a global anatomic structure

3D edge detection using recursive filtering : application to scanner images

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Vision par ordinateur: outils fondamentaux

National audienceLa deuxième édition revue et augmentée de cet ouvrage présente les outils fondamentaux de la vision par ordinateur dans un langage mathématique accessible aux étudiants de niveau licence en mathématiques ou informatique. Il donne également de nombreux exemples d'utilisation de la vision par ordinateur dans deux domaines de technologie de pointe : la robotique et l'imagerie médicale. L'ouvrage est complété par 185 références bibliographiques commentées tout le long du texte

From Voxel to Intrinsic Surface Features

International audienceWe establish a theoretical link between 3D edge detection and local surface approximation using uncertainty. As a practical application of the theory, we present a method for computing typical curvature features from 3D medical images. We determine the uncertainties inherent in edge (and surface) detection in 2- and 3-dimensional images by quantitatively analysing the uncertainty in edge position, orientation and magnitude produced by the multidimensional (2D and 3D) versions of the Deriche-Canny recursive separable edgedetector. The uncertainty is shown to depend on edge orientation, e.g. the position uncertainty may vary with a ratio larger than 2.8 in the 2D case, and 3.5 in the 3D case. These uncertainties are then used to compute local geometric models (quadric surface patches) of the surface, which are suitable for reliably estimating local surface characteristics; for example, Gaussian and Mean curvature. We demonstrate the effectiveness of our methods compared to previous techniques. These curvatures are then used to obtain more structured features such as curvature extrema and lines of curvature extrema. The final goal is to extract robust geometric features on which registration and/or tracking procedures can rely

Using uncertainty to link edge detection and local surface modelling

We establish a theoretical link between the 3D edge detection and the local surface approximation using uncertainty. As a practical application of the theory, we present a method for computing typical curvature features from 3D medical images. We use the uncertainties inherent in edge (and surface) detection in 2- and 3-dimensional images determined by quantitatively analyzing the uncertainty in edge position, orientation and magnitude produced by the multidimensional (2-D and 3-D) versions of the Monga-Deriche-Canny recursive separable edge-detector. These uncertainties allow to compute local geometric models (quadric surface patches) of the surface, which are suitable for reliably estimating local surface characteristics, for example, Gaussian and Mean curvature. We demonstrate the effectiveness of our methods compared to previous techniques. These curvatures are then used to obtain more structured features such as curvature extrema and lines of curvature extrema. The final goal is to extract robust geometric features on which registration and/or tracking procedures can rely

Thin nets and Crest lines : Application to Satellite Data and Medical Images

Projet SYNTIMIn this paper, we describe a new approach for extracting {\em thin nets} in grey level images. The key point of our approach is to model thin nets as the crest lines of the image surface. Crest lines are the lines where the magnitude of the maximum curvature is locally maximum in the corresponding principal direction. We define these lines using first, second and third derivatives of the image. We compute the image derivatives using recursive filters approximating the Gaussian filter and its derivatives. Using an adapted scale factor, we apply this approach to the extraction of roads in satellite data and blood vessels in medical images. We also apply this method to the extraction of the crest lines in depth maps of human faces

Segmentation of 3D pore space from CT images using curvilinear skeleton: application to numerical simulation of microbial decomposition

Recent advances in 3D X-ray Computed Tomographic (CT) sensors have stimulated research efforts to unveil the extremely complex micro-scale processes that control the activity of soil microorganisms. Voxel-based description (up to hundreds millions voxels) of the pore space can be extracted, from grey level 3D CT scanner images, by means of simple image processing tools. Classical methods for numerical simulation of biological dynamics using mesh of voxels, such as Lattice Boltzmann Model (LBM), are too much time consuming. Thus, the use of more compact and reliable geometrical representations of pore space can drastically decrease the computational cost of the simulations. Several recent works propose basic analytic volume primitives (e.g. spheres, generalized cylinders, ellipsoids) to define a piece-wise approximation of pore space for numerical simulation of draining, diffusion and microbial decomposition. Such approaches work well but the drawback is that it generates approximation errors. In the present work, we study another alternative where pore space is described by means of geometrically relevant connected subsets of voxels (regions) computed from the curvilinear skeleton. Indeed, many works use the curvilinear skeleton (3D medial axis) for analyzing and partitioning 3D shapes within various domains (medicine, material sciences, petroleum engineering, etc.) but only a few ones in soil sciences. Within the context of soil sciences, most studies dealing with 3D medial axis focus on the determination of pore throats. Here, we segment pore space using curvilinear skeleton in order to achieve numerical simulation of microbial decomposition (including diffusion processes). We validate simulation outputs by comparison with other methods using different pore space geometrical representations (balls, voxels).Comment: preprint, submitted to Computers & Geosciences 202

From voxel to curvature

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