27,841 research outputs found
HERMITIAN CLIFFORD-HERMITE WAVELETS
The one-dimensional continuous wavelet transform is a successful tool for signal and image analysis, with applications in physics and engineering. Clifford analysis offers an appropriate framework for taking wavelets to higher dimension. In the usual orthogonal case Clifford analysis focusses on monogenic functions, i.e. null solutions of the rotation invariant vector valued Dirac operator ∂, defined in terms of an orthogonal basis for the quadratic space Rm underlying the construction of the Clifford algebra R0,m. An intrinsic feature of this function theory is that it encompasses all dimensions at once, as opposed to a tensorial approach with products of one-dimensional phenomena. This has allowed for a very specific construction of higher dimensional wavelets and the development of the corresponding theory, based on generalizations of classical orthogonal polynomials on the real line, such as the radial Clifford-Hermite polynomials introduced by Sommen. In this paper, we pass to the Hermitian Clifford setting, i.e. we let the same set of generators produce the complex Clifford algebra C2n (with even dimension), which we equip with a Hermitian conjugation and a Hermitian inner product. Hermitian Clifford analysis then focusses on the null solutions of two mutually conjugate Hermitian Dirac operators which are invariant under the action of the unitary group. In this setting we construct new Clifford-Hermite polynomials, starting in a natural way from a Rodrigues formula which now involves both Dirac operators mentioned. Due to the specific features of the Hermitian setting, four different types of polynomials are obtained, two types of even degree and two types of odd degree. These polynomials are used to introduce a new continuous wavelet transform, after thorough investigation of all necessary properties of the involved polynomials, the mother wavelet and the associated family of wavelet kernels
Wavelets and their use
This review paper is intended to give a useful guide for those who want to
apply discrete wavelets in their practice. The notion of wavelets and their use
in practical computing and various applications are briefly described, but
rigorous proofs of mathematical statements are omitted, and the reader is just
referred to corresponding literature. The multiresolution analysis and fast
wavelet transform became a standard procedure for dealing with discrete
wavelets. The proper choice of a wavelet and use of nonstandard matrix
multiplication are often crucial for achievement of a goal. Analysis of various
functions with the help of wavelets allows to reveal fractal structures,
singularities etc. Wavelet transform of operator expressions helps solve some
equations. In practical applications one deals often with the discretized
functions, and the problem of stability of wavelet transform and corresponding
numerical algorithms becomes important. After discussing all these topics we
turn to practical applications of the wavelet machinery. They are so numerous
that we have to limit ourselves by some examples only. The authors would be
grateful for any comments which improve this review paper and move us closer to
the goal proclaimed in the first phrase of the abstract.Comment: 63 pages with 22 ps-figures, to be published in Physics-Uspekh
The multi-fractal structure of contrast changes in natural images: from sharp edges to textures
We present a formalism that leads very naturally to a hierarchical
description of the different contrast structures in images, providing precise
definitions of sharp edges and other texture components. Within this formalism,
we achieve a decomposition of pixels of the image in sets, the fractal
components of the image, such that each set only contains points characterized
by a fixed stregth of the singularity of the contrast gradient in its
neighborhood. A crucial role in this description of images is played by the
behavior of contrast differences under changes in scale. Contrary to naive
scaling ideas where the image is thought to have uniform transformation
properties \cite{Fie87}, each of these fractal components has its own
transformation law and scaling exponents. A conjecture on their biological
relevance is also given.Comment: 41 pages, 8 figures, LaTe
Retinal Vessel Segmentation Using the 2-D Morlet Wavelet and Supervised Classification
We present a method for automated segmentation of the vasculature in retinal
images. The method produces segmentations by classifying each image pixel as
vessel or non-vessel, based on the pixel's feature vector. Feature vectors are
composed of the pixel's intensity and continuous two-dimensional Morlet wavelet
transform responses taken at multiple scales. The Morlet wavelet is capable of
tuning to specific frequencies, thus allowing noise filtering and vessel
enhancement in a single step. We use a Bayesian classifier with
class-conditional probability density functions (likelihoods) described as
Gaussian mixtures, yielding a fast classification, while being able to model
complex decision surfaces and compare its performance with the linear minimum
squared error classifier. The probability distributions are estimated based on
a training set of labeled pixels obtained from manual segmentations. The
method's performance is evaluated on publicly available DRIVE and STARE
databases of manually labeled non-mydriatic images. On the DRIVE database, it
achieves an area under the receiver operating characteristic (ROC) curve of
0.9598, being slightly superior than that presented by the method of Staal et
al.Comment: 9 pages, 7 figures and 1 table. Accepted for publication in IEEE
Trans Med Imag; added copyright notic
Phase Harmonic Correlations and Convolutional Neural Networks
A major issue in harmonic analysis is to capture the phase dependence of
frequency representations, which carries important signal properties. It seems
that convolutional neural networks have found a way. Over time-series and
images, convolutional networks often learn a first layer of filters which are
well localized in the frequency domain, with different phases. We show that a
rectifier then acts as a filter on the phase of the resulting coefficients. It
computes signal descriptors which are local in space, frequency and phase. The
non-linear phase filter becomes a multiplicative operator over phase harmonics
computed with a Fourier transform along the phase. We prove that it defines a
bi-Lipschitz and invertible representation. The correlations of phase harmonics
coefficients characterise coherent structures from their phase dependence
across frequencies. For wavelet filters, we show numerically that signals
having sparse wavelet coefficients can be recovered from few phase harmonic
correlations, which provide a compressive representationComment: 26 pages, 8 figure
Multiresolution analysis using wavelet, ridgelet, and curvelet transforms for medical image segmentation
Copyright @ 2011 Shadi AlZubi et al. This article has been made available through the Brunel Open Access Publishing Fund.The experimental study presented in this paper is aimed at the development of an automatic image segmentation system for classifying region of interest (ROI) in medical images which are obtained from different medical scanners such as PET, CT, or MRI. Multiresolution analysis (MRA) using wavelet, ridgelet, and curvelet transforms has been used in the proposed segmentation system. It is particularly a challenging task to classify cancers in human organs in scanners output using shape or gray-level information; organs shape changes throw different slices in medical stack and the gray-level intensity overlap in soft tissues. Curvelet transform is a new extension of wavelet and ridgelet transforms which aims to deal with interesting phenomena occurring along curves. Curvelet transforms has been tested on medical data sets, and results are compared with those obtained from the other transforms. Tests indicate that using curvelet significantly improves the classification of abnormal tissues in the scans and reduce the surrounding noise
The curvelet transform for image denoising
We describe approximate digital implementations of two new mathematical transforms, namely, the ridgelet transform and the curvelet transform. Our implementations offer exact reconstruction, stability against perturbations, ease of implementation, and low computational complexity. A central tool is Fourier-domain computation of an approximate digital Radon transform. We introduce a very simple interpolation in the Fourier space which takes Cartesian samples and yields samples on a rectopolar grid, which is a pseudo-polar sampling set based on a concentric squares geometry. Despite the crudeness of our interpolation, the visual performance is surprisingly good. Our ridgelet transform applies to the Radon transform a special overcomplete wavelet pyramid whose wavelets have compact support in the frequency domain. Our curvelet transform uses our ridgelet transform as a component step, and implements curvelet subbands using a filter bank of a` trous wavelet filters. Our philosophy throughout is that transforms should be overcomplete, rather than critically sampled. We apply these digital transforms to the denoising of some standard images embedded in white noise. In the tests reported here, simple thresholding of the curvelet coefficients is very competitive with "state of the art" techniques based on wavelets, including thresholding of decimated or undecimated wavelet transforms and also including tree-based Bayesian posterior mean methods. Moreover, the curvelet reconstructions exhibit higher perceptual quality than wavelet-based reconstructions, offering visually sharper images and, in particular, higher quality recovery of edges and of faint linear and curvilinear features. Existing theory for curvelet and ridgelet transforms suggests that these new approaches can outperform wavelet methods in certain image reconstruction problems. The empirical results reported here are in encouraging agreement
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