3,484 research outputs found
A new numerical strategy with space-time adaptivity and error control for multi-scale streamer discharge simulations
This paper presents a new resolution strategy for multi-scale streamer
discharge simulations based on a second order time adaptive integration and
space adaptive multiresolution. A classical fluid model is used to describe
plasma discharges, considering drift-diffusion equations and the computation of
electric field. The proposed numerical method provides a time-space accuracy
control of the solution, and thus, an effective accurate resolution independent
of the fastest physical time scale. An important improvement of the
computational efficiency is achieved whenever the required time steps go beyond
standard stability constraints associated with mesh size or source time scales
for the resolution of the drift-diffusion equations, whereas the stability
constraint related to the dielectric relaxation time scale is respected but
with a second order precision. Numerical illustrations show that the strategy
can be efficiently applied to simulate the propagation of highly nonlinear
ionizing waves as streamer discharges, as well as highly multi-scale nanosecond
repetitively pulsed discharges, describing consistently a broad spectrum of
space and time scales as well as different physical scenarios for consecutive
discharge/post-discharge phases, out of reach of standard non-adaptive methods.Comment: Support of Ecole Centrale Paris is gratefully acknowledged for
several month stay of Z. Bonaventura at Laboratory EM2C as visiting
Professor. Authors express special thanks to Christian Tenaud (LIMSI-CNRS)
for providing the basis of the multiresolution kernel of MR CHORUS, code
developed for compressible Navier-Stokes equations (D\'eclaration d'Invention
DI 03760-01). Accepted for publication; Journal of Computational Physics
(2011) 1-2
Spatial image polynomial decomposition with application to video classification
International audienceThis paper addresses the use of orthogonal polynomial basis transform in video classification due to its multiple advantages, especially for multiscale and multiresolution analysis similar to the wavelet transform. In our approach, we benefit from these advantages to reduce the resolution of the video by using a multiscale/multiresolution decomposition to define a new algorithm that decomposes a color image into geometry and texture component by projecting the image on a bivariate polynomial basis and considering the geometry component as the partial reconstruction and the texture component as the remaining part, and finally to model the features (like motion and texture) extracted from reduced image sequences by projecting them into a bivariate polynomial basis in order to construct a hybrid polynomial motion texture video descriptor. To evaluate our approach, we consider two visual recognition tasks, namely the classification of dynamic textures and recognition of human actions. The experimental section shows that the proposed approach achieves a perfect recognition rate in the Weizmann database and highest accuracy in the Dyntex++ database compared to existing methods
Efficient multiscale regularization with applications to the computation of optical flow
Includes bibliographical references (p. 28-31).Supported by the Air Force Office of Scientific Research. AFOSR-92-J-0002 Supported by the Draper Laboratory IR&D Program. DL-H-418524 Supported by the Office of Naval Research. N00014-91-J-1004 Supported by the Army Research Office. DAAL03-92-G-0115Mark R. Luettgen, W. Clem Karl, Alan S. Willsky
Stochastic uncertainty models for the luminance consistency assumption
International audienceIn this paper, a stochastic formulation of the brightness consistency used in many computer vision problems involving dynamic scenes (motion estimation or point tracking for instance) is proposed. Usually, this model which assumes that the luminance of a point is constant along its trajectory is expressed in a differential form through the total derivative of the luminance function. This differential equation links linearly the point velocity to the spatial and temporal gradients of the luminance function. However when dealing with images, the available informations only hold at discrete time and on a discrete grid. In this paper we formalize the image luminance as a continuous function transported by a flow known only up to some uncertainties related to such a discretization process. Relying on stochastic calculus, we define a formulation of the luminance function preservation in which these uncertainties are taken into account. From such a framework, it can be shown that the usual deterministic optical flow constraint equation corresponds to our stochastic evolution under some strong constraints. These constraints can be relaxed by imposing a weaker temporal assumption on the luminance function and also in introducing anisotropic intensity-based uncertainties. We in addition show that these uncertainties can be computed at each point of the image grid from the image data and provide hence meaningful information on the reliability of the motion estimates. To demonstrate the benefit of such a stochastic formulation of the brightness consistency assumption, we have considered a local least squares motion estimator relying on this new constraint. This new motion estimator improves significantly the quality of the results
Convolutional Neural Network on Three Orthogonal Planes for Dynamic Texture Classification
Dynamic Textures (DTs) are sequences of images of moving scenes that exhibit
certain stationarity properties in time such as smoke, vegetation and fire. The
analysis of DT is important for recognition, segmentation, synthesis or
retrieval for a range of applications including surveillance, medical imaging
and remote sensing. Deep learning methods have shown impressive results and are
now the new state of the art for a wide range of computer vision tasks
including image and video recognition and segmentation. In particular,
Convolutional Neural Networks (CNNs) have recently proven to be well suited for
texture analysis with a design similar to a filter bank approach. In this
paper, we develop a new approach to DT analysis based on a CNN method applied
on three orthogonal planes x y , xt and y t . We train CNNs on spatial frames
and temporal slices extracted from the DT sequences and combine their outputs
to obtain a competitive DT classifier. Our results on a wide range of commonly
used DT classification benchmark datasets prove the robustness of our approach.
Significant improvement of the state of the art is shown on the larger
datasets.Comment: 19 pages, 10 figure
Visual Importance-Biased Image Synthesis Animation
Present ray tracing algorithms are computationally intensive, requiring hours of computing time for complex scenes. Our previous work has dealt with the development of an overall approach to the application of visual attention to progressive and adaptive ray-tracing techniques. The approach facilitates large computational savings by modulating the supersampling rates in an image by the visual importance of the region being rendered. This paper extends the approach by incorporating temporal changes into the models and techniques developed, as it is expected that further efficiency savings can be reaped for animated scenes. Applications for this approach include entertainment, visualisation and simulation
Optical flow computation via multiscale regularization
"EDICS category 1.11."Includes bibliographical references (p. 38-40).Supported by the Air Force Office of Scientific Research. AFOSR-88-0032 Supported by the Draper Laboratory IR&D Program. DL-H-418524 Supported by the Office of Naval Research. N00014-91-J-1004 Supported by the Army Research Office. DAAL03-36-K-0171Mark R. Luettgen, W. Clem Karl, Alan S. Willsky
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