5,463 research outputs found
Analysis and approximation of some Shape-from-Shading models for non-Lambertian surfaces
The reconstruction of a 3D object or a scene is a classical inverse problem
in Computer Vision. In the case of a single image this is called the
Shape-from-Shading (SfS) problem and it is known to be ill-posed even in a
simplified version like the vertical light source case. A huge number of works
deals with the orthographic SfS problem based on the Lambertian reflectance
model, the most common and simplest model which leads to an eikonal type
equation when the light source is on the vertical axis. In this paper we want
to study non-Lambertian models since they are more realistic and suitable
whenever one has to deal with different kind of surfaces, rough or specular. We
will present a unified mathematical formulation of some popular orthographic
non-Lambertian models, considering vertical and oblique light directions as
well as different viewer positions. These models lead to more complex
stationary nonlinear partial differential equations of Hamilton-Jacobi type
which can be regarded as the generalization of the classical eikonal equation
corresponding to the Lambertian case. However, all the equations corresponding
to the models considered here (Oren-Nayar and Phong) have a similar structure
so we can look for weak solutions to this class in the viscosity solution
framework. Via this unified approach, we are able to develop a semi-Lagrangian
approximation scheme for the Oren-Nayar and the Phong model and to prove a
general convergence result. Numerical simulations on synthetic and real images
will illustrate the effectiveness of this approach and the main features of the
scheme, also comparing the results with previous results in the literature.Comment: Accepted version to Journal of Mathematical Imaging and Vision, 57
page
On the segmentation of astronomical images via level-set methods
Astronomical images are of crucial importance for astronomers since they
contain a lot of information about celestial bodies that can not be directly
accessible. Most of the information available for the analysis of these objects
starts with sky explorations via telescopes and satellites. Unfortunately, the
quality of astronomical images is usually very low with respect to other real
images and this is due to technical and physical features related to their
acquisition process. This increases the percentage of noise and makes more
difficult to use directly standard segmentation methods on the original image.
In this work we will describe how to process astronomical images in two steps:
in the first step we improve the image quality by a rescaling of light
intensity whereas in the second step we apply level-set methods to identify the
objects. Several experiments will show the effectiveness of this procedure and
the results obtained via various discretization techniques for level-set
equations.Comment: 24 pages, 59 figures, paper submitte
Eulerian-Lagrangian method for simulation of cloud cavitation
We present a coupled Eulerian-Lagrangian method to simulate cloud cavitation
in a compressible liquid. The method is designed to capture the strong,
volumetric oscillations of each bubble and the bubble-scattered acoustics. The
dynamics of the bubbly mixture is formulated using volume-averaged equations of
motion. The continuous phase is discretized on an Eulerian grid and integrated
using a high-order, finite-volume weighted essentially non-oscillatory (WENO)
scheme, while the gas phase is modeled as spherical, Lagrangian point-bubbles
at the sub-grid scale, each of whose radial evolution is tracked by solving the
Keller-Miksis equation. The volume of bubbles is mapped onto the Eulerian grid
as the void fraction by using a regularization (smearing) kernel. In the most
general case, where the bubble distribution is arbitrary, three-dimensional
Cartesian grids are used for spatial discretization. In order to reduce the
computational cost for problems possessing translational or rotational
homogeneities, we spatially average the governing equations along the direction
of symmetry and discretize the continuous phase on two-dimensional or
axi-symmetric grids, respectively. We specify a regularization kernel that maps
the three-dimensional distribution of bubbles onto the field of an averaged
two-dimensional or axi-symmetric void fraction. A closure is developed to model
the pressure fluctuations at the sub-grid scale as synthetic noise. For the
examples considered here, modeling the sub-grid pressure fluctuations as white
noise agrees a priori with computed distributions from three-dimensional
simulations, and suffices, a posteriori, to accurately reproduce the statistics
of the bubble dynamics. The numerical method and its verification are described
by considering test cases of the dynamics of a single bubble and cloud
cavitaiton induced by ultrasound fields.Comment: 28 pages, 16 figure
Playing with Duality: An Overview of Recent Primal-Dual Approaches for Solving Large-Scale Optimization Problems
Optimization methods are at the core of many problems in signal/image
processing, computer vision, and machine learning. For a long time, it has been
recognized that looking at the dual of an optimization problem may drastically
simplify its solution. Deriving efficient strategies which jointly brings into
play the primal and the dual problems is however a more recent idea which has
generated many important new contributions in the last years. These novel
developments are grounded on recent advances in convex analysis, discrete
optimization, parallel processing, and non-smooth optimization with emphasis on
sparsity issues. In this paper, we aim at presenting the principles of
primal-dual approaches, while giving an overview of numerical methods which
have been proposed in different contexts. We show the benefits which can be
drawn from primal-dual algorithms both for solving large-scale convex
optimization problems and discrete ones, and we provide various application
examples to illustrate their usefulness
- …