Frederic Fol Leymarie

Tracking deformable objects in the plane using an active contour model

The problems of segmenting a noisy intensity image and tracking a nonrigid object in the plane are discussed. In evaluating these problems, a technique based on an active contour model commonly called a snake is examined. The technique is applied to cell locomotion and tracking studies. The snake permits both the segmentation and tracking problems to be simultaneously solved in constrained cases. A detailed analysis of the snake model, emphasizing its limitations and shortcomings, is presented, and improvements to the original description of the model are proposed. Problems of convergence of the optimization scheme are considered. In particular, an improved terminating criterion for the optimization scheme that is based on topographic features of the graph of the intensity image is proposed. Hierarchical filtering methods, as well as a continuation method based on a discrete sale-space representation, are discussed. Results for both segmentation and tracking are presented. Possible failures of the method are discussed. >

Simulating the grassfire transform using an active contour model

A method for shape description of planar objects that integrates both region and boundary features is presented. The method is an implementation of a 2D dynamic grassfire that relies on a distance surface on which elastic contours minimize an energy function. The method is based on an active contour model. Numerous implementation aspects of the shape description method were optimized. A Euclidean metric was used for optimal accuracy, and the active contour model permits bypassing some of the discretization limitations inherent in using a digital grid. Noise filtering was performed on the basis of both contour feature measures and region measures, that is, curvature extremum significance and ridge support, respectively, to obtain robust shape descriptors. Other improvements and variations of the algorithmic implementation are proposed. >

Fast raster scan distance propagation on the discrete rectangular lattice

Abstract The main result of this paper is that simple (raster scan) sequential algorithms for computing Euclidean Distance Transforms can be implemented in an optimized manner from the point of view of numerical computations. We will show that these fast implementations have computational complexities comparable to the city block, chessboard, and other simple chamfer Distance Transforms.

The Shock Scaffold for Representing 3D Shape

The usefulness of the 3D Medical Axis (MA) is dependent on both the availability of accurate and stable methods for computing individual MA points and on schemes for deriving the local structure and connectivity among these points. We propose a framework which achieves both by combining the advantages of exact bisector computations used in computational geometry, on the one hand, and the local nature of propagation-based algorithms, on the one hand, and the local nature of propagation-based algorithms, on the other, but without the computational complexity, connectivity, added dimensionality, and post processing issues commonly found in these approaches. Specifically, the notion of flow of shocks along the MA manifold is used to identify flow along special points which are represented as 2D sheets. The scaffold not only organizes shape information in a hierarchical manner, but is a tool for the efficient recovery of the scaffold itself and can lead to exact reconstruction. We present examples of this approach for synthetic data, as well as for sherd data from the domain of digital archaeology.

Bayesian Pot-Assembly from Fragments as Problems in Perceptual-Grouping and Geometric-Learning

A heretofore unsolved problem of great archaeological importance is the automatic assembly of pots made on a wheel from the hundreds (or thousands) of sherds found at an excavation site. An approach is presented to the automatic estimation of mathematical models of such pots from 3D measurements of sherds. A Bayesian approach is formulated beginning with a description of the complete set of geometric parameters that determine the distribution of the sherd measurement data. Matching of fragments and aligning them geometrically into configurations is based on matching break-curves (curves on a pot surface separating fragments), estimated axis and profile curve pairs for individual fragments and configurations of fragments, and a number of features of groups of break-curves. Pot assembly is a bottom-up maximum likelihood performance-based search. Experiments are illustrated on pots which were broken for the purpose, and on sherds from an archaeological dig located in Petra, Jordan. The performance measure can also be an aposteriori probability, and many other types of information can be included, e.g., pot wall thickness, surface color, patterns on the surface, etc. This can also be viewed as the problem of learning a geometric object from an unorganized set of free-form fragments of the object and of clutter, or as a problem of perceptual grouping.

3D shape registration using regularized medial scaffolds

This work proposes a method for global registration based on matching 3D medial structures of unorganized point clouds or triangulated meshes. Most practical known methods are based on the iterative closest point (ICP) algorithm, which requires an initial alignment close to the globally optimal solution to ensure convergence to a valid solution. Furthermore, it can also fail when there are points in one dataset with no corresponding matches in the other dataset. The proposed method automatically finds an initial alignment close to the global optimal by using the medial structure of the datasets. For this purpose, we first compute the medial scaffold of a 3D dataset: a 3D graph made of special shock curves linking special shock nodes. This medial scaffold is then regularized exploiting the known transitions of the 3D medial axis under deformation or perturbation of the input data. The resulting simplified medial scaffolds are then registered using a modified graduated assignment graph matching algorithm. The proposed method shows robustness to noise, shape deformations, and varying surface sampling densities.

Towards surface regularization via medial axis transitions

The reconstruction of objects from data in practical applications often leads to surfaces with small perturbations and other artifacts, which make the detection of their ridges and generalized axes difficult. We propose an approach to smoothing small structures while preserving ridges, which is based on the medial axis structure of the surface. The medial axis of the surface is organized as a graph structure and the closeness of the medial axis graph to points of instability (transitions) is used to identify those structures, which are most likely due to perturbations. The removal of these structures is our approach to regularizing both the medial axis and the surface. This paper focuses on a subset of medial transitions arising from protrusions and the method is illustrated for a few synthetic and real images.

REALISE: reconstruction of REALity from Image SEquences

REALISE was designed to extract from sequences of images, acquired with a moving camera, the information necessary for determining the 3D (CAD-like) structure of a real-life scene together with information about the radiometric signatures of surfaces bounding the extracted 3D objects (e.g. reflectance behaviour). The retrieved information is then integrated in a virtual reality (VR) software environment. The R&D work is been performed principally in the following areas of computer vision and computer graphics: structure from motion, recovery of geometries, recovery of photometric and texture information, highly realistic rendering on the basis of empirically-based reflectance models, and the design and development of improved rendering processes together with a new VR system. Beside this innovative R&D work another key aspect of REALISE is to have computer vision & computer graphics cooperate to produce realistic 3D data efficiently.

Generative choreography: animating in real-time dancing avatars

In this paper we introduce a novel approach to dance choreographies in virtual worlds. We present a dance performed by avatars in a virtual world, where a computational ecosystem provides a mechanism driving the actions and movements of the avatars. First, we discuss the background and motivations, and describe the performance. Then, we describe the technical aspects of the algorithm driving the choreographic movements. Finally we discuss its critical aspects and contextualize the work with regards to dance practice and evolutionary art history. In the process of this discussion, we emphasize the advantages of the AI model of computational ecosystems for the animation of non-player-characters.

Shape Features Using Curvature Morphology

The notion of curvature of planar curves has emerged as one of the most powerful for the representation and interpretation of objects in an image. Although curvature extraction from a digitized object contour would seem to be a rather simple task, few methods exist that are at the same time easy to implement, fast, and reliable in the presence of noise. In this paper we first briefly present a scheme for obtaining the discrete curvature function of planar contours based on the chain-code representation of a boundary. Secondly, we propose a method for extracting important features from the curvature function such as extrema or peaks, and segments of constant curvature. We use mathematical morphological operations on functions to achieve this. Finally, on the basis of these morphological operations, we suggest a new scale-space representation for curvature named the Morphological Curvature Scale-Space. Advantages over the usual scale-space approaches are shown.