2,198 research outputs found
Self-similar prior and wavelet bases for hidden incompressible turbulent motion
This work is concerned with the ill-posed inverse problem of estimating
turbulent flows from the observation of an image sequence. From a Bayesian
perspective, a divergence-free isotropic fractional Brownian motion (fBm) is
chosen as a prior model for instantaneous turbulent velocity fields. This
self-similar prior characterizes accurately second-order statistics of velocity
fields in incompressible isotropic turbulence. Nevertheless, the associated
maximum a posteriori involves a fractional Laplacian operator which is delicate
to implement in practice. To deal with this issue, we propose to decompose the
divergent-free fBm on well-chosen wavelet bases. As a first alternative, we
propose to design wavelets as whitening filters. We show that these filters are
fractional Laplacian wavelets composed with the Leray projector. As a second
alternative, we use a divergence-free wavelet basis, which takes implicitly
into account the incompressibility constraint arising from physics. Although
the latter decomposition involves correlated wavelet coefficients, we are able
to handle this dependence in practice. Based on these two wavelet
decompositions, we finally provide effective and efficient algorithms to
approach the maximum a posteriori. An intensive numerical evaluation proves the
relevance of the proposed wavelet-based self-similar priors.Comment: SIAM Journal on Imaging Sciences, 201
Spectrum Analysis of Speech Recognition via Discrete Tchebichef Transform
Speech recognition is still a growing field. It carries strong potential in the near future as computing power grows.
Spectrum analysis is an elementary operation in speech recognition. Fast Fourier Transform (FFT) is the traditional
technique to analyze frequency spectrum of the signal in speech recognition. Speech recognition operation requires
heavy computation due to large samples per window. In addition, FFT consists of complex field computing. This paper
proposes an approach based on discrete orthonormal Tchebichef polynomials to analyze a vowel and a consonant in
spectral frequency for speech recognition. The Discrete Tchebichef Transform (DTT) is used instead of popular FFT.
The preliminary experimental results show that DTT has the potential to be a simpler and faster transformation for
speech recognition
Construction of Hilbert Transform Pairs of Wavelet Bases and Gabor-like Transforms
We propose a novel method for constructing Hilbert transform (HT) pairs of
wavelet bases based on a fundamental approximation-theoretic characterization
of scaling functions--the B-spline factorization theorem. In particular,
starting from well-localized scaling functions, we construct HT pairs of
biorthogonal wavelet bases of L^2(R) by relating the corresponding wavelet
filters via a discrete form of the continuous HT filter. As a concrete
application of this methodology, we identify HT pairs of spline wavelets of a
specific flavor, which are then combined to realize a family of complex
wavelets that resemble the optimally-localized Gabor function for sufficiently
large orders.
Analytic wavelets, derived from the complexification of HT wavelet pairs,
exhibit a one-sided spectrum. Based on the tensor-product of such analytic
wavelets, and, in effect, by appropriately combining four separable
biorthogonal wavelet bases of L^2(R^2), we then discuss a methodology for
constructing 2D directional-selective complex wavelets. In particular,
analogous to the HT correspondence between the components of the 1D
counterpart, we relate the real and imaginary components of these complex
wavelets using a multi-dimensional extension of the HT--the directional HT.
Next, we construct a family of complex spline wavelets that resemble the
directional Gabor functions proposed by Daugman. Finally, we present an
efficient FFT-based filterbank algorithm for implementing the associated
complex wavelet transform.Comment: 36 pages, 8 figure
Application of invariant moments for crowd analysis
The advancement in technology such as the use of CCTV has improved the effects of monitoring crowds. However, the drawback of using CCTV is that the observer might miss some information because monitoring crowds through CCTV system is very laborious and cannot be performed for all the cameras simultaneously. Hence, integrating the image processing techniques into the CCTV surveillance system could give numerous key advantages, and is in fact the only way to deploy effective and affordable intelligent video security systems. Meanwhile, in monitoring crowds, this approach may provide an automated crowd analysis which may also help to improve the prevention of incidents and accelerate action triggering. One of the image processing techniques which might be appropriate is moment invariants. The moments for an individual object have been used widely and successfully in lots of application such as pattern recognition, object identification or image reconstruction. However, until now, moments have not been widely used for a group of objects, such as crowds. A new method Translation Invariant Orthonormal Chebyshev Moments has been proposed. It has been used to estimate crowd density, and compared with two other methods, the Grey Level Dependency Matrix and Minkowski Fractal Dimension. The extracted features are classified into a range of density by using a Self Organizing Map. A comparison of the classification results is done to determine which method gives the best performance for measuring crowd density by vision. The Grey Level Dependency Matrix gives slightly better performance than the Translation Invariant Orthonormal Chebyshev Moments. However, the latter requires less computational resources
Near Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
Suppose we are given a vector in . How many linear measurements do
we need to make about to be able to recover to within precision
in the Euclidean () metric? Or more exactly, suppose we are
interested in a class of such objects--discrete digital signals,
images, etc; how many linear measurements do we need to recover objects from
this class to within accuracy ? This paper shows that if the objects
of interest are sparse or compressible in the sense that the reordered entries
of a signal decay like a power-law (or if the coefficient
sequence of in a fixed basis decays like a power-law), then it is possible
to reconstruct to within very high accuracy from a small number of random
measurements.Comment: 39 pages; no figures; to appear. Bernoulli ensemble proof has been
corrected; other expository and bibliographical changes made, incorporating
referee's suggestion
Wavelet transforms and their applications to MHD and plasma turbulence: a review
Wavelet analysis and compression tools are reviewed and different
applications to study MHD and plasma turbulence are presented. We introduce the
continuous and the orthogonal wavelet transform and detail several statistical
diagnostics based on the wavelet coefficients. We then show how to extract
coherent structures out of fully developed turbulent flows using wavelet-based
denoising. Finally some multiscale numerical simulation schemes using wavelets
are described. Several examples for analyzing, compressing and computing one,
two and three dimensional turbulent MHD or plasma flows are presented.Comment: Journal of Plasma Physics, 201
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