Jacques-Olivier Lachaud

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This work presents a generic deformable model for extracting objects from volumetric data with a coarse-to-fine approach. This model is based on a dynamic triangulated surface which alters its geometry according to internal and external constraints to perform shape recovery. A new framework for topology changes is proposed to extract complex objects: within(More)
This paper presents a new tangent estimator to digitized curves based on digital line recognition. It outperforms existing ones on important criteria while keeping the same computation time: accuracy on smooth or polygonal shapes, isotropy, preservation of inflexion points and convexity, asymptotic behaviour. Its asymptotic convergence (sometimes called(More)
This paper presents a comparative evaluation of tangent es-timators based on digital line recognition on digital curves. The comparison is carried out with a comprehensive set of criteria: accuracy on smooth or polygonal shapes, behaviour on convex/concave parts, computation time, isotropy, aymptotic convergence. We further propose a new estimator mixing(More)
Iso-surfaces are routinely used for the visualization of volumetric structures. Further processing (such as quantitative analysis, morphometric measurements, shape description) requires volume representations. The skeleton representation matches these requirements by providing a concise description of the object. This paper has two parts. First, we exhibit(More)
Due to their general and robust formulation deformable models offer a very appealing approach to 3D image seg-mentation. However there is a trade-off between model genericity, model accuracy and computational efficiency. In general, fully generic models require a uniform sampling of either the space or their mesh. The segmentation accuracy is thus a global(More)
Discrete geometry redefines notions borrowed from Euclidean geometry creating a need for new algorithmical tools. The notion of convexity does not translate trivially, and detecting if a discrete region of the plane is convex requires a deeper analysis. To the many different approaches of digital convexity, we propose the combinatorics on words point of(More)
We present a parametric deformable model which recovers image components with a complexity independent from the resolution of input images. The proposed model also automatically changes its topology and remains fully compatible with the general framework of deformable models. More precisely, the image space is equipped with a metric that expands salient(More)
Discrete geometric estimators approach geometric quantities on digitized shapes without any knowledge of the continuous shape. A classical yet difficult problem is to show that an estimator asymptot-ically converges toward the true geometric quantity as the resolution increases. We study here the convergence of local estimators based on Digital Straight(More)
In this paper we introduce a new curvature estimator along digital contours, that we called Global Min-Curvature estimator (GMC). As opposed to previous curvature estimators, it considers all the possible shapes that are digitized as this contour, and selects the most probable one with a global optimization approach. The GMC estimator exploits the geometric(More)