This paper reviews model based methods for non rigid shape analysis and recognition These methods model match and classify non rigid shapes which are generally problematic for conventional algorithms using rigid models Issues including deformable model represen tation optimization criteria formulation model matching and classi cation are examined in detail The emphasis of this paper is on D def...

Previous approaches to deformable model shape estimation and tracking have assumed a fixed class of shapes representation (e.g., deformable superquadrics), initialized prior to tracking. Since the shape coverage of the model is fixed, such approaches do not directly accommodate incremental representation discovery during tracking. As a result, model shape coverage is decoupled from tracking, th...

OBJECTIVE To investigate whether there is a seasonal variation in the incidence of gestational diabetes mellitus (GDM). RESEARCH DESIGN AND METHODS This retrospective cohort study of 60 306 eligible South Australian live-born singletons during 2007-2011 recorded in the South Australian Perinatal Statistics Collection (SAPSC) examined the incidence of GDM in relation to estimated date of conce...

OBJECTIVE We aimed to compare the discriminative power of prognostic models for early prediction of women at risk for the development of gestational diabetes mellitus (GDM) using four currently recommended diagnostic criteria based on the 75-g oral glucose tolerance test (OGTT). We also described the potential effect of application of the models into clinical practice. RESEARCH DESIGN AND MET...

OBJECTIVE To compare cord blood leptin concentrations between normal pregnancy, pregnancy induced hypertension (PIH), and gestational diabetes mellitus (GDM). DESIGN Cross-sectional study. SETTING Academic institutes and a tertiary care maternal hospital. METHOD 48 newborns of normal pregnancies (N=18), pregnancy induced hypertension (N=16), and gestational diabetes mellitus (N=14) were s...

Shape Matching and Object Recognition by Alexander Christiansen Berg Doctor of Philosophy in Computer Science University of California, Berkeley Professor Jitendra Malik, Chair We address comparing related, but not identical shapes in images following a deformable template strategy. At the heart of this is the notion of an alignment between the shapes to be matched. The transformation necessary...

We present efficient and accurate algorithms for interference detection among objects undergoing polynomial deformation. The scope of our algorithms include physically-based models undergoing dynamic simulation subject to non-penetration constraints, variational models, deformable models used in soft object animation, geometric models including polygonal meshes, parametric surfaces such as Bézi...

The segmentation of pelvic structures in magnetic resonance (MR) images of the female pelvic cavity is a challenging task. This paper proposes the use of three novel geometric deformable models to segment the bladder, vagina and rectum in axial MR images. The different imaging appearances and prior shape knowledge are combined into a level set framework as segmentation cues. The movements of th...

Feature-based analysis is becoming a very popular approach for geometric shape analysis. Following the success of this approach in image analysis, there is a growing interest in finding analogous methods in the 3D world. Maximally stable component detection is a low computation cost and high repeatability method for feature detection in images.In this study, a diffusion-geometry based framework...

OBJECTIVE Elevated pre-pregnancy BMI, excessive gestational weight gain (GWG), and gestational diabetes mellitus (GDM) are known determinants of fetal growth. The role of placental weight is unclear. We aimed to examine the extent to which placental weight mediates the associations of pre-pregnancy BMI, GWG, and GDM with birth weight-for-gestational age, and whether the relationships differ by ...