Technical ReportsGilson Antonio Giraldi
(gilson@lncc.br)
·
1.1 Data Integration Middleware
System for Scientific Visualization (2005)
Authors: Gilson A. Giraldi, Fabio Porto, Bruno
Schulze, Vinicius
Fontes, Marcio L. Dutra
Abstract: In this paper we
focus on distributed scientific visualization on the
grid. Specifically, we firstly find out basic
requirements for distributing graphics applications over a grid environment.
Then, we propose a middleware infrastructure adapted for supporting
scientific visualization applications that meets these requirements. We
claim that we should consider scientific visualization in grid from an
integrated global view of data and programs published by heterogeneous and
distributed data sources. This idea can be implemented by CoDIMS
which is an
environment for the generation of Configurable Data integration Middleware
Systems. CoDIMS adaptive architecture is based on the
integration of special
components managed by a control module that executes users workflows. We
exemplify our proposal with the CoDIMS-G, which is a
middleware that follows
CoDIMS architecture and was designed to provide data
and program integration
service for the grid. An specific configuration of CoDIMS-G
for distributed
particle tracing within a grid environment is also presented.
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·
1.2 Computational Animation of
Fluids through Smoothed Particle Hydrodynamics (2005) - In Portuguese
Authors: ALGEMIRO A. S. NETO, GILSON A. GIRALDI , ANTONIO LOPES APOLINARIO JR.
PAULO S. RODRIGUES
Abstract: This work
presents an implementation of a Computational Fluid
Dynamics (CFD) method for fluid animation. This method is based on
the Smoothed Particle Hydrodynamics (SPH) technique. The fluid
behavior is modeled through Navier-Stokes equations
under
some initial conditions and constrains. The discretization
is performed through SPH
The result is a representation through a particles system which move under the
influence of
forces, such as gravity and pressure. If combined with efficient techniques for
fluid
visualization, this methodology for animation may achieve a high
degree of realism, since it is based on physical principles for flow
representation. Preliminary results obtained reproduced from the literature
show
that this is a promising technique.
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· 1.3
ANIMAÇÃO DE FLUIDOS VIA TÉCNICAS DE VISUALIZAÇÃO CIENTÍFICA E
MECÂNICA
COMPUTACIONAL (2005)
Authors: GILSON A. GIRALDI, ANTONIO LOPES
APOLINARIO JR., ANTONIO A. F. OLIVEIRA
RAUL A. FEIJÓO
Resumo: Este texto tem como tema central a animação de fluidos via
técnicas para visualização de
dados científicos (Visualização Científica) e métodos em Mecânica
Computacional. Por “animação de fluidos”
entendemos a geração de uma seqüência de imagens digitais contendo
fluidos em movimento. Este movimento
deve ser convincente, ou seja, o fluido deve escoar com o grau de realismo
necessário para o contexto do filme
que está sendo gerado. Dentro desta perspectiva, a animação de fluidos tem
natureza multidisciplinar, e seu
desenvolvimento depende da interação entre profissionais das áreas de
computação e engenharia. Objetivamente,
para atingir o grau de realismo necessário, é preciso que as equações de
fluidos sejam convenientemente tratadas e
que as técnicas de visualização de dados utilizadas possam gerar (renderizar) a cena final com a aparência
necessária.
Estas técnicas de visualização são oriundas da computação gráfica. As
equações de fluidos (Navier-Stokes) são
derivadas
de conceitos da mecânica clássica. Neste sentido temos como objetivos a
apresentação de modelos matemáticos de fluidos do ponto
de vista da computação gráfica e a descrição de técnicas de visualização
focadas para rendering de cenas envolvendo fluidos.
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· Um
Modelo de Simulação para Escoamento Superficial de Águas sobre Terrenos Baseado
em GPU
Authors: Bruno Barcellos S. Coutinho, Gilson Antônio Giraldi, Antônio L. Apolinário
Jr., Paulo Sérgio S. Rodrigues.
Abstract: In the last years
a significant number of models to produce a realistic animation of fluids was
proposed. In general, such modelsare based on partial
differential equations and numerical methods. Recently, we proposed a discrete
model based on a particle system, to simulate the superficial fluid flow over a
terrain. A Digital Terrain Model or DTM is used as the basis of the simulation
process. The fluid dynamics is ruled by a particle system based on a cellular
automata. The particles runs through the edges of lattice, depending on terrain
declivity and the behavior of the fluid in the particle'sneighborhood.
In this paper, we present a GPU-based implementation of that model. The terrain
geometrical and physical properties are structured in a way that the whole
simulation process can be done in the GPU. The simplicity of the automata model
allied to the computational power of GPU allow us to generate real-time
animations, as the experimental results
demonstrate.
·
2.1 CONVEXITY ANALYSIS OF SNAKE
MODELS BASED ON HAMILTONIAN FORMULATION (1998)
Authors: Gilson A. Giraldi and Antonio Alberto F.
Oliveira
Abstract: This paper
presents a convexity analysis for the dynamic snake model based on the
Potential Energy functional and the Hamiltonian formulation of the classical
mechanics. First we see the snake model as a dynamical system whose singular
points are the borders we seek. Next we show that a necessary condition for a
singular point to be an attractor is that the energy functional is strictly
convex in a neighborhood of it, that means, if the singular point is a local
minimum of the potential energy. As a consequence of this analysis, a local
expression relating the dynamic parameters and the rate of convergence arises.
Such results link the convexity analysis of the potential energy and the
dynamic snake model and point forward to the necessity of a physical quantity
whose convexity analysis is related to the dynamic and which incorporate the
velocity space. Such a quantity is exactly the (conservative) Hamiltonian of
the system.
·
2.2 Dual and Topologically Adaptable
Snakes and Initialization of Deformable Models (2000)
Authors: G. A. Giraldi and N. Vasconcelos
and E.Strauss and A. F. Oliveira
Abstract: The original
proposal of Active Contour Models, also called snakes, suffers from the strong
sensitivity to the initial contour position
and can not deal with topological changes. In this paper we describe some
techniques to address these limitations. The problem of topological changes is
addressed by the T-Snakes model by embedding the snake in the framework of a simplicial decomposition of the domain. The sensitivity to
initialization is addressed by the Dual method by using two contours going towards
the desired boundary. We integrated these two methods, given a new approach
called Dual-T-Snakes, which maintains the topological capabilities of the
T-Snakes and the ability of avoiding local minima of the Dual Active Contour
model. One advantage of the Dual-T-Snakes is the reduction of the search space,
which allows a more efficient application of a dynamic programing
technique. To initialize the Dual-T-Snakes automatically we developed a
methodology based on a multiresolution scheme and on
the T-Snakes framework. Just as the T-Snakes, our methods (Dual-T-Snakes and
initialization framework) can be extended to 3D. We demonstrate our
methodologies in synthetic and medical images showing also how to incorporate
fuzzy and multi-scale techniques in the Dual-T-Snakes approach. Finally, we
discuss some challenges in active contour models and present the conclusions of
our work.
·
2.3 Mathematical Elements of
Deformable Models for Image Processing (2009)
Authors: G.
A. Giraldi and Paulo S. S. Rodrigues
Abstract: Active Contour
Models, also called Snake models, are powerful techniques for boundary
extraction and segmentation of 2D images. Despite of their abilities, the
non-invariance of the internal energy under affine transformations, the
non-convexity of the model energy functional and the inability to deal with
topological changes are known limitations for most of these methods. In this
book we describe some techniques to address these limitations. The
non-invariance of the internal energy has been addressed in the context of
active shape models and Lie groups. The non-convexity of the model energy can
be addressed through Dual contour approaches, diffusion approaches, as well as an
automatic procedure to initialize the model closer to the desired boundary. The
problem of topological changes is addressed by embedding the snake in the
framework of a simplicial decomposition of the domain
or through implicit formulations. In this text, we offer some background in
parametric snakes, followed by a taxonomy of deformable models for image
processing. Then, we discuss mathematical elements behind the main works that
address the mentioned problems. Next, we survey snake approaches according to
the presented taxonomy. In order to complete the material, we dedicate a
Chapter for extensions to Deformable Surface approaches. Finally, this lecture
ends with a discussion on snake approaches, some drawbacks and future works. We
survey applications for shape recovery in cell and ultrasound images.
3.1 Fourier Analysis for Linear
Filtering and Holographic Representations of 1D and 2D Signals
Authors: G.A. GIRALDI, B.F. MOUTINHO, D.M.L. DE CARVALHO AND J.C. DE
OLIVEIRA
Abstract: In this paper, we
focus on Fourier analysis and holographic transforms for
signal representation. For instance, in the case of image processing, the
holographic representation
has the property that an arbitrary portion of the transformed image enables
reconstruction of the whole image with details missing. We focus on holographic
representation
de[1]ned
through the Fourier Transforms. Thus, we firstly consider the Fourier
analysis in the context of signal processing. Next, we review the Discrete
Holographic
Fourier Transform (DHFT) for image representation. Then, we describe the
contributions
of our work. We show a simple scheme for progressive transmission based on the
DHFT.
Next, we propose the Continuous Holographic Fourier Transform (CHFT) and
discuss
some theoretical aspects of it for 1D signals. Finally,
some testes are presented in the
experimental results.
3.2 Aprendizado de Variedades: Aspectos Geométricos e Aplicações
Resumo:
Muitas
áreas, tais como visão computacional, reconhecimento de padrões e análise de
imagens,
requerem manipulação de uma grande quantidade de dados representados
originalmente
em
espaços de dimensão elevada. Em particular, para um banco de imagens de faces
humanas,
existe um considerável nível de redundância oriundo da própria anatomia (boca,
nariz,
orelhas, olhos, etc). Neste sentido, a redução de
dimensionalidade é extremamente
necessária
para possibilitar a análise dos dados. Supondo que os dados possam ser
ajustados
a
uma variedade de dimensão d contida num espaço Euclideano de dimensão muito maior que d,
podemos
estimar o espaço tangente local e definir uma topologia sobre a variedade. Neste
contexto,
as técnicas de Aprendizado de Variedades (Manifold Learning) surgem como um
recurso
importante que envolve áreas como aprendizagem de máquina e mineração de dados
para
tratar a redução de dimensionalidade recuperando a geometria intrínseca do
conjunto de
dados.
Exemplos clássicos de técnicas de aprendizado de variedades são o ISOMAP e LLE.
Ilustramos
uma aplicação à imagens de faces humanas, onde geometria de aquisição permite
estimar
propriedades da variedade que representa os dados. Uma das técnicas recentes de
aprendizado
de variedades é o RML, a qual visa preservar a geometria intrínseca usando
conceitos
clássicos de Geometria Riemanniana. Esta técnica
produz bons resultados e será
apresentada
neste trabalho. Apresentaremos ainda uma fundamentação teórica para aprendizado
de
variedades e redução de dimensionalidade, seguida de uma revisão das principais
técnicas na área.
Serão
também listados alguns dos principais desafios neste tema.
·
3.1 Quantum Models for Artificial
Neural Networks (2002)
Authors: Gilson A. Giraldi and Jean Faber
Abstract: There has been a
growing interest in artificial neural networks
(ANNs) based on quantum theoretical concepts and techniques due to cognitive
science and computer science aspects. The so called Quantum Neural Networks
(QNNs) is an exciting area of research in the field of quantum computation
and quantum information. However, a key question about QNNs is what such an
architecture will look like as an implementation on quantum hardware. To
look for an answer to this question we firstly review some basic concepts in
ANNs and emphasize their inherent non-linearity. Next, we analyze the main
algorithms and architecture proposed in this field. The main conclusion is
that, up to now, there is no a complete solution for the implementation of
QNNs. We found partial solution in models that deal with nonlinear effects
in quantum computation. The Dissipative Gate (D-Gate) and the Quantum Dot
Neural Network are the focused models in this field. The former is a
theoretical one while the later is a device composed by a quantum dot
molecule coupled to its environment and subject to a time-varying external
field. A discretized version of the Feynman path
integral formulation for
this system can be put into a form that resembles a classical neural
network. Starting from these models, we discuss learning rules
in the context of QNNs. Besides, we present our proposals in
the field of QNNs.