The term Deformable Model describes a group of computer algorithms and techniques widely used in computer vision today. They all share the common characteristic that they model the variability of a certain class of objects (In biometrics this could be the class of all faces, or of all hands, or all eyes, etc.). Today, different representations of the object classes are commonly used. Earlier al...

Deformable geometric models can be used in the context of Bayesian analysis to solve ill-posed tomographic reconstruction problems. The uncertainties associated with a Bayesian analysis may be assessed by generating a set of random samples from the posterior, which may be accomplished using a Markov-Chain Monte-Carlo (MCMC) technique. We demonstrate the combination of these techniques for a rec...

BACKGROUND Gestational diabetes mellitus (GDM) is associated with an elevated risk of adverse health outcomes such as type 2 diabetes and cardiovascular diseases. Carotid intima-media thickness (cIMT) is increasingly used as a noninvasive marker for subclinical atherosclerosis. Whether there is a direct correlation between GDM and elevated cIMT is still controversial. METHODS PubMed, Embase a...

Segmentation of vascular structures is a difficult and challenging task. In this article, we present an algorithm devised for the segmentation of such structures. Our technique consists in a geometric deformable model with associated energy functional that incorporates high-order multiscale features in a non-parametric statistical framework. Although the proposed segmentation method is generic,...

The early detection of prostate cancer plays a significant role in the success of treatment and outcome. To detect prostate cancer, imaging modalities such as TransRectal UltraSound (TRUS) and Magnetic Resonance Imaging (MRI) are relied on. MRI images are more comprehensible than TRUS images which are corrupted by noise such as speckles and shadowing. However, MRI screening is costly, often una...

Deformable geometric models can be used in the context of Bayesian analysis to solve ill-posed tomographic reconstruction problems. The uncertainties associated with a Bayesian analysis may be assessed by generating a set of random samples from the posterior, which may be accomplished using a Markov-Chain Monte-Carlo (MCMC) technique. We demonstrate the combination of these techniques for a rec...

Deformable models often vary in object boundary representation and the external force field used. Conventional methods, i.e. geodesic active contour model [1], have difficulties in dealing with boundary concavities, weak edges and image noise. Some approaches such as the generalized gradient vector flow (GGVF) [3] showed some improvements but have convergence issues caused by saddle or stationa...

ÐIn this paper, we develop integrated techniques that unify physics-based modeling with geometric subdivision methodology and present a scheme for dynamic manipulation of the smooth limit surface generated by the (modified) butterfly scheme using physics-based aforceo tools. This procedure-based surface model obtained through butterfly subdivision does not have a closed-form analytic formulatio...

In Group Decision Making (GDM) the automatic consensus models are guided by different consensus measures which usually are obtained by aggregating similarities observed among experts’ opinions. Most GDM problems based on linguistic approaches use symmetrically and uniformly distributed linguistic term sets to express experts’ opinions. However, there exist problems whose assessments need to be ...

face expression recognition is useful for designing new interactive devices offering the possibility of new ways for human to interact with computer systems. in this paper we develop a facial expressions analysis and synthesis system. the analysis part of the system is based on the facial features extracted from facial feature points (ffp) in frontal image sequences. selected facial feature poi...