Marcelo Vénere

A CPU-GPU framework for optimizing the quality of large meshes

The automatic generation of 3D finite element meshes (FEM) is still a bottleneck for the simulation of large fluid dynamic problems. Although today there are several algorithms that can generate good meshes without user intervention, in cases where the geometry changes during the calculation and thousands of meshes must be constructed, the computational cost of this process can exceed the cost of the FEM. There has been a lot of work in FEM parallelization and the algorithms work well in different parallel architectures, but at present there has not been much success in the parallelization of mesh generation methods. This paper will present a massive parallelization scheme for re-meshing with tetrahedral elements using the local modification algorithm. This method is frequently used to improve the quality of elements once the mesh has been generated, but we will show it can also be applied as a regeneration process, starting with the distorted and invalid mesh of the previous step. The parallelization is carried out using OpenCL and OpenMP in order to test the method in a multiple CPU architecture and also in Graphics Processing Units (GPUs). Finally we present the speedup and quality results obtained in meshes with hundreds of thousands of elements and different parallel APIs.

Multi-object segmentation framework using deformable models for medical imaging analysis

AbstractSegmenting structures of interest in medical images is an important step in different tasks such as visualization, quantitative analysis, simulation, and image-guided surgery, among several other clinical applications. Numerous segmentation methods have been developed in the past three decades for extraction of anatomical or functional structures on medical imaging. Deformable models, which include the active contour models or snakes, are among the most popular methods for image segmentation combining several desirable features such as inherent connectivity and smoothness. Even though different approaches have been proposed and significant work has been dedicated to the improvement of such algorithms, there are still challenging research directions as the simultaneous extraction of multiple objects and the integration of individual techniques. This paper presents a novel open-source framework called deformable model array (DMA) for the segmentation of multiple and complex structures of interest in different imaging modalities. While most active contour algorithms can extract one region at a time, DMA allows integrating several deformable models to deal with multiple segmentation scenarios. Moreover, it is possible to consider any existing explicit deformable model formulation and even to incorporate new active contour methods, allowing to select a suitable combination in different conditions. The framework also introduces a control module that coordinates the cooperative evolution of the snakes and is able to solve interaction issues toward the segmentation goal. Thus, DMA can implement complex object and multi-object segmentations in both 2D and 3D using the contextual information derived from the model interaction. These are important features for several medical image analysis tasks in which different but related objects need to be simultaneously extracted. Experimental results on both computed tomography and magnetic resonance imaging show that the proposed framework has a wide range of applications especially in the presence of adjacent structures of interest or under intra-structure inhomogeneities giving excellent quantitative results.

From a Serious Training Simulator for Ship Maneuvering to an Entertainment Simulator

This paper presents a ship-handling entertainment simulator that was developed to be used as a virtual reality experience in science exhibitions. It is a low-cost implementation that allows navigating a ship through a simple interface. Realistic 3D graphics area projected on a three panel screen implemented with computer monitors or HD LED TV. This simulator is an adaptation of a previous set of serious ship handling training simulators -called MELIPAL- that were developed for the Argentina Army. We describe how we adapted the original simulator to the new entertainment version, particularly the system architecture, the hardware, the 3D visualization and the user interface aspects.

A tennis training application using 3d gesture recognition

This paper presents a sport training system which recognizes user movements from data of the Wiimote device with accelerometer technology. Recognizing a new gesture involves the normalization of the Wiimote data and searching in a gesture templates database. The Dynamic Time Warping (DTW) comparison algorithm is used as a correlation function to compare the new gesture with every template. Based on prior training, the system can successfully recognize different sport shots. Particularly the system is instantiated for tennis training. The user visualizes the trajectory of the ball in a three-dimensional environment and he can interact with virtual objects that follow Newton dynamics.