Registration and modeling of shapes are two important problems in computer vision and pattern recognition. Despite enormous progress made over the past decade, these problems are still open. In this paper, we advance the state of the art in both directions. First we consider an efficient registration method that aims to recover a one-to-one correspondence between shapes and introduce measures of uncertainties driven from the data which explain the local support of the recovered transformations. To this end, a free form deformation is used to describe the deformation model. The transformation is combined with an objective function defined in the space of implicit functions used to represent shapes. Once the registration parameters have been recovered, we introduce a novel technique for model building and statistical interpretation of the training examples based on a variable bandwidth kernel approach. The support on the kernels varies spatially and is determined according to the uncertainties of the registration process. Such a technique introduces the ability to account for potential registration errors in the model. Hand-written character recognition and knowledge-based object extraction in medical images are examples of applications that demonstrate the potentials of the proposed framework.

Reliable segmentation of the left ventricle is a long sought objective in medical imaging for automatic retrieval of anatomical and pathological measurements and detection of malfunctions. In this paper, we propose a novel model-constrained approach to address this task. The method is based on an implicit representation of the shape model used in a shape registration framework with a Thin Plate Spline transform to retrieve possible deformations. The main innovation of our approach resides in the use of uncertainties defined on the registered shape to augment the training set and improve the robustness of the statistical deformable model. We use ICA to reduce the dimensionality of the space of deformations and provide a good separation of the different deformable parts of the heart. Furthermore the estimation of uncertainties is also introduced in the segmentation process which is addressed in a variational framework where prior knowledge and visual support are considered. The method lead to very promising qualitative and quantitative experimental results in CT.

In this paper we present a novel approach for mimicking expre ssions in 3D from a monocular video sequence. To this end, first we construct a high res olution semantic mesh model through automatic global and local registration of a low resolution ra ge data. The MPEG-4 standard and radial basis functions are then considered to represent and an imate such a model using a predefined set of control points in a compact fashion. In order to recover th 2D positions of the 3D control points in the observed sequence, we use local cascade Adaboost-dri ven search constrained. The search space is reduced through the use of predictive expression mo deling. The optimal configuration of the Adaboost responses is determined using combinatorial l inear programming which enforces the anthropometric nature of the model defined from these points . 3D position is then deduced and the displacement can be reproduced on any version of the mode l, registered on another face. Our method doesn’t require dense stereo estimation and can then produce realistic animations, using any 3D model. Key-words: Face Reconstruction, Face Animation, Facial Feature Extra ction ∗ Ecole Centrale de Paris † Orange / France Telecom R&D Mime d’Expression : de la Séquence Monoculaire à l’Animation 3D Résumé :Nous présentons dans ce papier une nouvelle approche pour mi me les expressions en 3D à partir d’une séquence monoculaire. Pour cela, nous constru isons un modèle de visage sémantique de haute résolutions grâce à un recalage automatique global et local à partir de données basse résolution. Nous considérons le standard MPEG-4 et les fonctions à base r adiale pour représenter et animer le modèle en utilisant un ensemble de points de contrôle prédéfi nis. Pour retrouver la position 2D de ces points de contrôle 3D dans la séquence observée, nous uti lisons un l’aglorithme de classification Adaboost en cascade. L’espace de recherche est réduit grâce à un modèle de prédiction d’expression. La configuration optimale des réponses de Adaboost est déter minée en utilisant de la programmation linéaire combinatoire, qui contraint la nature anthropome trique du modèle. La position 3D est alors déduite et le déplacement peut être reproduit sur toute vers ion du modèle, recalé sur un autre visage. Notre méthode ne requière pas d’estimation stereo dense et p eut reproduire des animations réalistes, en utilisant n’importe quel modèle 3D. Mots-clés : Reconstruction de Visage, Animation de visage, Extraction de points d’intêret Expression Mimicking 3

Reproduction of facial animation play a fundamental role in applications requiring human-computer interactions The objective of this paper is to introduce a geometric mechanism that exploits a fix number of states and is able to execute a subsequent number of transitions between facial expressions. Standard stereo-based techniques are used to reproduce the geometry and appearance of the most characteristic facial expressions. A novel free-form-deformation technique based on uncertainty driven local geometric registration in the space of distance transforms is used to produce a one-to-one mapping between the surfaces and the associated textures. Standard techniques from image morphing introduce the temporal aspect in the process. Experimental results and comparisons with actual observation demonstrate the potentials of such an approach.

In this chapter, we explore shape representation, registration, and modeling through implicit functions. To this end, we propose novel techniques for global and local registration of shapes through the alignment of the corresponding distance transforms by defining objective functions that minimize metrics between the implicit representations of shapes. Registration methods in the space of implicit functions like the sum of squares differences (SSD), which can account for primitive transformations (similarity), and more advanced methods like mutual information, which are able to handle more generic parametric transformations, are considered. To address local correspondences we also propose an objective function on the space of implicit representations where the displacement field is represented with a free form deformation that can guarantee one-to-one mapping. In order to address outliers as well as introduce confidence in the registration process, we extend our registration paradigm to estimate uncertainties through the formulation of local registration as a statistical inference problem in the space of implicit functions. Validation of the method through various applications is proposed: (i) parametric shape modeling and segmentation through active shapes for medical image analysis, (ii) variable bandwidth non-parametric shape modeling for recognition, and (iii) object extraction through a level set method. Promising results demonstrate the potentials of implicit shape representations.

In this paper we propose a novel variational technique for the knowledge based segmentation of two dimensional objects. One of the elements of our approach is the use of higher order implicit polynomials to represent shapes. The most important contribution is the estimation of uncertainties on the registered shapes, which can be used with a variable bandwidth kernel-based non-parametric density estimation process to model prior knowledge about the object of interest. Such a non-linear model with uncertainty measures is integrated with an adaptive visual-driven data term that aims to separate the object of interest from the background. Promising results obtained for the segmentation of the corpus callosum in MR mid-sagittal brain slices demonstrate the potential of such a framework.

In this paper, we introduce a new technique for shape modelling in the space of implicit polynomials. Registration consists of recovering an optimal one-to-one transformation of a higher order polynomial along with uncertainties measures that are determined according to the covariance matrix of the correspondences at the zero isosurface. In the modelling phase, these measures are used to weight the importance of the training samples phase according to a variable bandwidth non-parametric density estimation process. The selection of the most appropriate kernels to represent the training set is done through the maximum likelihood criterion. Excellent results for patterns of digits, related with the registration and the modelling aspects of our approach demonstrate the potentials of our method

Segmentation of the left ventricle in echocardiographic images is a task with important diagnostic power. We propose a model-based approach that aims at extracting the left ventricle for each frame of the cardiac cycle. Our approach exhibits several novel elements. Modelling consists of two separate components, one for the systolic and one for the diastolic moment. Segmentation is considered in two steps. During the first step a linear combination of the systolic and the diastolic model is to be recovered – that dictates the new model – along with a similarity transformation that projects this model to the desired image features. During the second step, a linear combination of the modes of variation for the systolic and diastolic models is recovered for precise extraction of the endocardium boundaries. The process is considered in the temporal domain where constraints are introduced to couple information across frames and to lead to a smooth solution. Promising results demonstrate the potentials of the presented framework.

In this paper, we propose a robust technique that integrates spatial and temporal information for consistent recovery of the endocardium. To account for the low image quality we introduce a local variant of the Mumford-Shah that is coupled with a model of limited parameters to describe the ventricle, namely an ellipse. The objective function is defined on the implicit space of ellipses, separates locally the blood pool from the heart wall and explores geometric constraints on the deformations of the endocardium to impose temporal consistency. Promising experimental results demonstrate the potentials of our method.

In this paper we present a novel approach for mimicking expressions in 3D from a monocular video sequence. To this end, first we construct a high resolution semantic mesh model through automatic global and local registration of a low resolution range data. Such a model is represented using predefined set of control points in a compact fashion, and animated using radial basis functions. In order to recover the 2D positions of the 3D control points in the observed sequence, we use cascade Adaboost-driven search. The search space is reduced through the use of predictive expression modeling. The optimal configuration of the Adaboost responses is determined using combinatorial linear programming which enforces the anthropometric nature of the model. Then the displacement can be reproduced on any version of the model, registered on another face. Our method doesn’t require dense stereo estimation and can then produce realistic animations, using any 3D model. Promising experimental results demonstrate the potential of our approach.