Conformal geometric algebra (CGA) is a framework that allows the representation of objects, such as points, planes and spheres, deformations, translations, rotations dilations uniform vectors, called multivectors. In this work, we demonstrate merits multivector usage with novel, integrated rigged character simulation based on CGA. framework, for first time, one may perform real-time cuts tears ...

We present the work that has been done on segmentation of abdominal aortic aneurysm (AAA) by Image Processing Group on Faculty of Electrical Engineering and Computing in Zagreb. We investigated performance of two types of deformable models: parametric and geometric deformable models both operating on three-dimensional data sets obtained by computer tomography angiography (CTA). Both inner and o...

The class of geometric deformable models, so-called level sets, has brought tremendous impact to medical imagery. In this paper we present yet another application of level sets to medical imaging. The method we give here will in a way modify the speed term in the standard level sets equation of motion. To do so we build a potential based on the distance and the gradient of the image we study. I...

Deformable geometric models fit very naturally into the context of Bayesian analysis. The prior probability of boundary shapes is taken to proportional to the negative exponential of the deformation energy used to control the boundary. This probabilistic interpretation is demonstrated using a Markov-Chain Monte-Carlo (MCMC) technique, which permits one to generate configurations that populate t...

Topology-preserving geometric deformable models (TGDMs) are used to segment objects that have a known topology. Their accuracy is inherently limited, however, by the resolution of the underlying computational grid. Although this can be overcome by using fine-resolution grids, both the computational cost and the size of the resulting surface increase dramatically. In order to maintain computatio...

Deformable geometric models fit very naturally into the context of Bayesian analysis. The prior probability of boundary shapes is taken to proportional to the negative exponential of the deformation energy used to control the boundary. This probabilistic interpretation is demonstrated using a Markov-Chain Monte-Carlo (MCMC) technique, which permits one to generate configurations that populate t...

Background and Objectives: Gestational Diabetes Mellitus (GDM) is the most common metabolic disorder in pregnancy. In case of early detection, some of its complications can be prevented. The aim of this study was to investigate early prediction of GDM by logistic regression (LR), discriminant analysis (DA), decision tree (DT) and perceptron artificial neural network (ANN) and to compare these m...

Christian Lauterbach: Interactive Ray Tracing of Massive and Deformable Models. (Under the direction of Dinesh Manocha.) Ray tracing is a fundamental algorithm used for many applications such as computer graphics, geometric simulation, collision detection and line-of-sight computation. Even though the performance of ray tracing algorithms scales with the model complexity, the high memory requir...

We propose an efficient image-matching method for deformable-object image matching using discrimination of deformable objects and geometric similarity clustering between feature-matching pairs. A deformable transformation maintains a particular form in the whole image, despite local and irregular deformations. Therefore, the matching information is statistically analyzed to calculate the possib...

We describe a method for analyzing the shape variability of images, called geometric PCA. Our approach is based on the use of deformation operators to model the geometric variability of images around a reference mean pattern. This leads to a new algorithm for estimating shape variability. Some numerical experiments on real images are proposed to highlight the benefits of this approach. The cons...