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In this paper, we formulate the Canonical Correlation Analysis (CCA) problem on matrix manifolds. This framework provides a natural way for dealing with matrix constraints and tools for building efficient algorithms even in an adaptive setting. Finally , an adaptive CCA algorithm is proposed and applied to a change detection problem in EEG signals.
This paper presents the main approaches used to synthesize talking faces, and provides greater detail on a handful of these approaches. No system is described exhaustively, however, and, for purposes of conciseness, not all existing systems are reviewed. An attempt is made to distinguish between facial synthesis itself (i.e the manner in which facial(More)
The aim of craniofacial reconstruction is to estimate the shape of a face from the shape of the skull. Few works in machine-assisted facial reconstruction have been conducted so far, probably due to technical (poor machine performance and data availability) and theoretical (complexity) reasons. Therefore, the main works in the literature consist in manual(More)
This paper is devoted to the construction of a complete database which is intended to improve the implementation and the evaluation of automated facial reconstruction. This growing database is currently composed of 85 head CT-scans of healthy European subjects aged 20-65 years old. It also includes the triangulated surfaces of the face and the skull of each(More)
In the context of computer assist surgical techniques , a new elastic registration method of 3D meshes is presented. In our applications, one mesh is a high density mesh (30000 vertexes), the second is a low density one (1000 ver-texes). Registration is based upon the minimisation of a symmetric distance between both meshes, defined on the vertexes, in a(More)
In this paper, we present a computer-assisted method for facial reconstruction. This method provides an estimation of the facial shape associated with unidentified skeletal remains. Current computer-assisted methods using a statistical framework rely on a common set of extracted points located on the bone and soft-tissue surfaces. Most of the facial(More)
In this paper, we deal with the problem of partially observed objects. These objects are defined by a set of points and their shape variations are represented by a statistical model. We present two models in this paper: a linear model based on PCA and a non-linear model based on KPCA. The present work attempts to localize of non visible parts of an object,(More)
This paper presents an alternative to the supervised KPCA based approach for learning a Multilayer Kernel Machine (MKM) [1]. In our proposed procedure, the hidden layers are learnt in a supervised fashion based on kernel partial least squares regression. The main interest resides in a simplified learning scheme as the obtained hidden features are(More)
In this paper, we present an algorithm for aligning and matching unlabeled sets of points and a method for building a statistical model composed of a mean observation and associated variability. This model is used to solve the cephalomet-ric problem. The main idea of this paper consists in using a dual step strategy: estimate the pose and then the(More)