The aim of this work is to automatically extract quantitative parameters from time sequences of 3D images (4D images) suited to heart pathology diagnosis. In this paper, we propose a framework for the reconstruction of the left ventricle motion from 4D images based on 4D deformable surface models. These 4D models are represented as a time sequence of 3D meshes whose deformation are correlated d...

Up to now, most of the research efforts to improve performance of programs based on hardware transactions were devoted to designing new hybrid transactional memories and transactional lock elision algorithms to speedup software fallback paths. Unfortunately hardware transactions remain insufficiently exploited, limited especially on microarchitectures where the access set is particularly limite...

In this paper we consider the interval selection problem, namely, finding a maximum cardinality subset of disjoint intervals from a given collection of intervals on the real line. A celebrated algorithm of Gavril [17] that picks the intervals greedily in increasing order of their right endpoint yields an optimal solution in the classic setting where the complete set of intervals is known to the...

generally, the measured secondary field data is inverted into resistivity using two principal models; the homogeneous half-space model and the layered half-space model. while the homogeneous half-space inversion uses single frequency data, the inversion is done individually for each of the frequencies used, the multi-layer 1d inversion is able to take the data of all frequencies available into ...

This paper presents a task-level control methodology for the general class of holonomically constrained multibody systems. As a point of departure, the general formulation of constrained dynamical systems is reviewed with respect to multiplier and minimization approaches. Subsequently, the operational space framework is considered and the underlying symmetry between constrained dynamics and ope...

Tracking 3D people from monocular video is often poorly constrained. To mitigate this problem, prior information can be exploited. In learning the prior stage, most algorithms think representing high-dimensional pose space in low-dimensional space as dimension reduction procedure, without considering the geometrical relation or time correlation in pose space. Therefore, the prior loses physical...

In this thesis, we present a new theory of discrete constrained optimization using Lagrange multipliers and an associated first-order search procedure (DLM) to solve general constrained optimization problems in discrete, continuous and mixed-integer space. The constrained problems are general in the sense that they do not assume the differentiability or convexity of functions. Our proposed theo...