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Wavelets on the Louvain-La-Neuve lake. Just near, people tan because it's a very hot day.

Wavelets on the Louvain-La-Neuve lake. Just near, people tan because it's a very hot day.

Wavelets on the Louvain-La-Neuve lake. Just near, people tan because it's a very hot day.

Wavelets on the Louvain-La-Neuve lake. Just near, people tan because it's a very hot day.

The purpose of this thesis is to introduce the concept of lateral inhibition as a generalized technique for compressing time/frequency representations of electromagnetic and acoustical signals, particularly speech. This requires at least a rudimentary treatment of the theory of frames-which generalizes most commonly known time/frequency distributions--the biology of hearing, and digital signal processing. As such, this material, along with the interrelationships of the disparate subjects, is presented in a tutorial style. This may leave the mathematician longing for more rigor, the neurophysiological psychologist longing for more substantive support of the hypotheses presented, and the engineer longing for a reprieve from the theoretical barrage. Despite the problems that arise when trying to appeal to too wide an audience, I hope this thesis is a cogent analysis of the compression of time/frequency distributions via lateral inhibitory networks. (js)

This report describes the main research achievements during the tine period cited above on the research project in the area of signal processing and telecommunications. The main achievements include the construction of Malvar, wavelets on arbitrary shapes a new system identification method using chirp signals and joint time-frequency analysis method, a new prefiltering for discrete multiwavelet transforms, and some new signal processing methods for telecommunications, and radar applications of jamming resistance.

We have analyzed cortical and subcortical field recordings from spatially distinct neural circuits in order to support the hypothesis that spatially distinct brain locations display correlated ictal activity during epileptic seizures. Field recordings have been obtained from cortex (CTX), anterior thalamic nuclei (AN), posterior thalamus (PT) and hippocampus (HPC) during pentylenetetrazol (PTZ) seizures in anesthetized animals. We use Wavelet Transform Cross-Correlation (WTCC) method in order to quantify the common activity between two recordings at particular bands of interest. In contrary to Fourier Transform Coherence (FTC), we show that WTCC provides a more reliable estimate of baud-specific common activity or cross-coherence between two epileptic sources. Although most of the signal power is located at higher frequencies (15-30Hz), results from WTCC reveal significant mean cross-correlation estimates (0.7-0.8) at primarily the lower regions of the spectrum (0-10Hz). The behavior observed in the brain recordings analyzed in this paper lets us differentiate between local and global behavior, where the global behavior is assumed to be due to a pacemaker function which is a quasi-periodic train of impulse functions that differentially excites various areas of the brain.

Key scientific results in the following four areas: (1) design of prefilters for discrete multiwavelet transform, (2) vector valued wavelets and vector filter banks, (3) a nonlinear operator related to wavelets and refinable function vector and (4) fractional wave packet transform. These results provide fundamental tools and theories for many Air Force and industrial applications, including compression of images/video images.

The new theory of physical wavelets makes it possible to perform radar and sonar analysis directly in the space time domain, based on fundamental principles underlying the emission, reflection, and reception of electromagnetic and acoustic waves. Being independent of the Fourier transform and even of the usual (affine) wavelet transform, this formalism is therefore equally well suited for ultrawideband or short pulse radar as for narrowband or continuous wave radar. However, Fourier analysis does have a natural place in this theory and can be used easily when spectral questions are of interest.

The new theory of physical wavelets makes it possible to perform radar and sonar analysis directly in the space time domain, based on fundamental principles underlying the emission, reflection, and reception of electromagnetic and acoustic waves. Being independent of the Fourier transform and even of the usual (affine) wavelet transform, this formalism is therefore equally well suited for ultrawideband or short pulse radar as for narrowband or continuous wave radar. However, Fourier analysis does have a natural place in this theory and can be used easily when spectral questions are of interest.

Various dynamic data analysis tools for one-dimensional time-domain signals are employed to determine the frequency content of a signal for mechanical analysis. When tied to the fundamental frequency of the various components comprising the machinery being evaluated, this information gives an indication of the state or health of the machine. Current techniques for evaluating dynamic data for potential mechanical problems are primarily centered around the Fast Fourier Transform (FFT) and the Shon Time Fourier Transform (STFT). However, the use of Fourier analysis for frequency component extraction is restricted to bandlimited stationary signals. Because of the stationary requirement, small transients may not be detected due to a smoothing effect of the FFT, or the FFT spectrum may be smeared due to frequency ramping and abrupt incidents or discontinuities in the signal. Various techniques have been employed to overcome the limitations of the FFT for non-stationary data. These techniques include windowed Fourier analysis (including the STFT) and a background in wavelet theory based upon the analyzing function basis approach so that wavelet theory may be contrasted against Fourier analysis. A model signal with stationary and transient characteristics is developed to permit comparisons of various analysis techniques based upon a known analytic signal which resembles a real vibration signal. Some applications of wavelets to other transient signals are also provided.

We develop an exact wavelet transform on the three-dimensional ball (i.e. on the solid sphere), which we name the flaglet transform. For this purpose we first construct an exact transform on the radial half-line using damped Laguerre polynomials and develop a corresponding quadrature rule. Combined with the spherical harmonic transform, this approach leads to a sampling theorem on the ball and a novel three-dimensional decomposition which we call the Fourier-Laguerre transform. We relate this new transform to the well-known Fourier-Bessel decomposition and show that band-limitedness in the Fourier-Laguerre basis is a sufficient condition to compute the Fourier-Bessel decomposition exactly. We then construct the flaglet transform on the ball through a harmonic tiling, which is exact thanks to the exactness of the Fourier-Laguerre transform (from which the name flaglets is coined). The corresponding wavelet kernels are well localised in real and Fourier-Laguerre spaces and their angular aperture is invariant under radial translation. We introduce a multiresolution algorithm to perform the flaglet transform rapidly, while capturing all information at each wavelet scale in the minimal number of samples on the ball. Our implementation of these new tools achieves floating-point precision and is made publicly available. We perform numerical experiments demonstrating the speed and accuracy of these libraries and illustrate their capabilities on a simple denoising example.

Dark energy dominates the energy density of our Universe, yet we know very little about its nature and origin. Although strong evidence in support of dark energy is provided by the cosmic microwave background, the relic radiation of the Big Bang, in conjunction with either observations of supernovae or of the large scale structure of the Universe, the verification of dark energy by independent physical phenomena is of considerable interest. We review works that, through a wavelet analysis on the sphere, independently verify the existence of dark energy by detecting the integrated Sachs-Wolfe effect. The effectiveness of a wavelet analysis on the sphere is demonstrated by the highly statistically significant detections of dark energy that are made. Moreover, the detection is used to constrain properties of dark energy. A coherent picture of dark energy is obtained, adding further support to the now well established cosmological concordance model that describes our Universe.

We introduce a new wide class of p-adic pseudodifferential operators. We show that the basis of p-adic wavelets is the basis of eigenvectors for the introduced operators.

The dimension function D_psi of a band-limited wavelet is bounded by n if the support of its Fourier transform is contained in the interval [-{2^(n+2)/3}pi, {2^(n+2)/3}pi]. For each positive integer n and for each epsilon > 0, we construct a wavelet psi with support of $\hat psi$ contained in [-{2^(n+2)/3}pi, {2^(n+2)/3}pi + epsilon] such that D_psi > n on a set of positive measure, which proves that [-{2^(n+2)/3}pi, {2^(n+2)/3}pi] is the largest symmetric interval for estimating the dimension function by n.

Let $a$, $b$ be two fixed non-zero constants. A measurable set $E\subset \mathbb{R}$ is called a Weyl-Heisenberg frame set for $(a, b)$ if the function $g=\chi_{E}$ generates a Weyl-Heisenberg frame for $L^2(\mathbb{R})$ under modulates by $b$ and translates by $a$, i.e., $\{e^{imbt}g(t-na\}_{m,n\in\mathbb{Z}}$ is a frame for $L^2(\mathbb{R})$. It is an open question on how to characterize all frame sets for a given pair $(a,b)$ in general. In the case that $a=2\pi$ and $b=1$, a result due to Casazza and Kalton shows that the condition that the set $F=\bigcup_{j=1}^{k}([0,2\pi)+2n_{j}\pi)$ (where $\{n_{1}

We define an affine structure on $\ltwor\oplus...\oplus\ltwor$ and, following some ideas developed in \cite{Dut1}, we construct a local trace function for this situation. This trace function is a complete invariant for a shift invariant subspace and it has a variety of properties which make it easily computable. The local trace is then used to give a characterization of super-wavelets and to analyze their multiplicity function, dimension function and spectral function. The "$n\times$" oversampling result of Chui and Shi \cite{CS} is refined to produce super-wavelets.

Lectures from the National Programme on Technology Enhanced Learning - Electronics and Communication Engineering - Adv. Digital Signal Processing - Multirate and wavelets

Lectures from the National Programme on Technology Enhanced Learning - Electronics and Communication Engineering - Adv. Digital Signal Processing - Multirate and wavelets

A representation of solutions of the wave equation with two spatial coordinates in terms of localized elementary ones is presented. Elementary solutions are constructed from four solutions with the help of transformations of the affine Poincar\'e group, i.e., with the help of translations, dilations in space and time and Lorentz transformations. The representation can be interpreted in terms of the initial-boundary value problem for the wave equation in a half-plane. It gives the solution as an integral representation of two types of solutions: propagating localized solutions running away from the boundary under different angles and packet-like surface waves running along the boundary and exponentially decreasing away from the boundary. Properties of elementary solutions are discussed. A numerical investigation of coefficients of the decomposition is carried out. An example of the field created by sources moving along a line with different speeds is considered, and the dependence of coefficients on speeds of sources is discussed.

In the present paper, a fuzzy logic based method is combined with wavelet decomposition to develop a step-by-step dynamic hybrid model for the estimation of financial time series. Empirical tests on fuzzy regression, wavelet decomposition as well as the new hybrid model are conducted on the well known $SP500$ index financial time series. The empirical tests show an efficiency of the hybrid model.

We establish system of equations for single function normalized tight frame wavelets with compact supports associated with $2\times 2$ expansive integral matrices in $L^2(\R^2)$.

Texture analysis and classification remain as one of the biggest challenges for the field of computer vision and pattern recognition. On this matter, Gabor wavelets has proven to be a useful technique to characterize distinctive texture patterns. However, most of the approaches used to extract descriptors of the Gabor magnitude space usually fail in representing adequately the richness of detail present into a unique feature vector. In this paper, we propose a new method to enhance the Gabor wavelets process extracting a fractal signature of the magnitude spaces. Each signature is reduced using a canonical analysis function and concatenated to form the final feature vector. Experiments were conducted on several texture image databases to prove the power and effectiveness of the proposed method. Results obtained shown that this method outperforms other early proposed method, creating a more reliable technique for texture feature extraction.

Texture analysis and classification remain as one of the biggest challenges for the field of computer vision and pattern recognition. On this matter, Gabor wavelets has proven to be a useful technique to characterize distinctive texture patterns. However, most of the approaches used to extract descriptors of the Gabor magnitude space usually fail in representing adequately the richness of detail present into a unique feature vector. In this paper, we propose a new method to enhance the Gabor wavelets process extracting a fractal signature of the magnitude spaces. Each signature is reduced using a canonical analysis function and concatenated to form the final feature vector. Experiments were conducted on several texture image databases to prove the power and effectiveness of the proposed method. Results obtained shown that this method outperforms other early proposed method, creating a more reliable technique for texture feature extraction.

Texture analysis and classification remain as one of the biggest challenges for the field of computer vision and pattern recognition. On this matter, Gabor wavelets has proven to be a useful technique to characterize distinctive texture patterns. However, most of the approaches used to extract descriptors of the Gabor magnitude space usually fail in representing adequately the richness of detail present into a unique feature vector. In this paper, we propose a new method to enhance the Gabor wavelets process extracting a fractal signature of the magnitude spaces. Each signature is reduced using a canonical analysis function and concatenated to form the final feature vector. Experiments were conducted on several texture image databases to prove the power and effectiveness of the proposed method. Results obtained shown that this method outperforms other early proposed method, creating a more reliable technique for texture feature extraction.

Texture analysis and classification remain as one of the biggest challenges for the field of computer vision and pattern recognition. On this matter, Gabor wavelets has proven to be a useful technique to characterize distinctive texture patterns. However, most of the approaches used to extract descriptors of the Gabor magnitude space usually fail in representing adequately the richness of detail present into a unique feature vector. In this paper, we propose a new method to enhance the Gabor wavelets process extracting a fractal signature of the magnitude spaces. Each signature is reduced using a canonical analysis function and concatenated to form the final feature vector. Experiments were conducted on several texture image databases to prove the power and effectiveness of the proposed method. Results obtained shown that this method outperforms other early proposed method, creating a more reliable technique for texture feature extraction.

We present an algorithm for construction step wavelets on local fields of positive characteristic.

In the companion paper, a coherent charge-current distribution for radiating electromagnetic wavelets was constructed on an oblate spheroidal surface S_a. Its main drawback was the necessity of including magnetic along with electric charges, making the sources impossible to realize. Here we show how this difficulty can be overcome by using Hertz potentials to generate a charge-current distribution due solely to bound electric charges. However, this distribution still appears difficult to realize because it consists of multiple surface layers on S_a. We show how it can be replaced by a simple volume distribution on a spheroidal shell. Our method generalizes the usual construction of equivalent Huygens sources, based on boundary conditions on an interface between electromagnetic media, by allowing the transition to be gradual without incurring addition complexity.

We consider wavelets in L^2(R^d) which have generalized multiresolutions. This means that the initial resolution subspace V_0 in L^2(R^d) is not singly generated. As a result, the representation of the integer lattice Z^d restricted to V_0 has a nontrivial multiplicity function. We show how the corresponding analysis and synthesis for these wavelets can be understood in terms of unitary-matrix-valued functions on a torus acting on a certain vector bundle. Specifically, we show how the wavelet functions on R^d can be constructed directly from the generalized wavelet filters.

All wavelets can be associated to a multiresolution like structure, i.e. an incr easing sequence of subspaces of L^2(R). We consider the interaction of a wavel et and the translation operator in terms of which of the subspaces in this multi resolution like structure are invariant under the translation operator. This ac tion defines the notion of the translation invariance property of order n. In this paper we show that wavelets of all levels of translation invariance exist, first for the classic case of dilation by 2, and then for arbitrary integral di lation factors.

We described a wide class of $p$-adic refinable equations generating $p$-adic multiresolution analysis. A method for the construction of $p$-adic orthogonal wavelet bases within the framework of the MRA theory is suggested. A realization of this method is illustrated by an example, which gives a new 3-adic wavelet basis. Another realization leads to the $p$-adic Haar bases which were known before.

Motivated by potential applications in multiplexing and by recent results on Gabor analysis with Hermite windows due to Gr\"{o}chenig and Lyubarskii, we investigate vector-valued wavelet transforms and vector-valued wavelet frames, which constitute special cases of super-wavelets, with a particular attention to the case when the analyzing wavelet vector is related to Fourier transforms of Laguerre functions. We construct an isometric isomorphism between $L^{2}(\mathbb{R}^{+},\mathbf{C}^{n})$ and poly-Bergman spaces, with a view to relate the sampling sequences in the poly-Bergman spaces to the wavelet frames and super-frames with the windows $\Phi_{n}$. One of the applications of the theory is a proof that $b\ln a

Motivated by potential applications in multiplexing and by recent results on Gabor analysis with Hermite windows due to Gr\"{o}chenig and Lyubarskii, we investigate vector-valued wavelet transforms and vector-valued wavelet frames, which constitute special cases of super-wavelets, with a particular attention to the case when the analyzing wavelet vector is related to Fourier transforms of Laguerre functions. We construct an isometric isomorphism between $L^{2}(\mathbb{R}^{+},\mathbf{C}^{n})$ and poly-Bergman spaces, with a view to relate the sampling sequences in the poly-Bergman spaces to the wavelet frames and super-frames with the windows $\Phi_{n}$. One of the applications of the theory is a proof that $b\ln a

Future cosmological surveys will provide 3D large scale structure maps with large sky coverage, for which a 3D Spherical Fourier-Bessel (SFB) analysis in spherical coordinates is natural. Wavelets are particularly well-suited to the analysis and denoising of cosmological data, but a spherical 3D isotropic wavelet transform does not currently exist to analyse spherical 3D data. The aim of this paper is to present a new formalism for a spherical 3D isotropic wavelet, i.e. one based on the SFB decomposition of a 3D field and accompany the formalism with a public code to perform wavelet transforms. We describe a new 3D isotropic spherical wavelet decomposition based on the undecimated wavelet transform (UWT) described in Starck et al. 2006. We also present a new fast Discrete Spherical Fourier-Bessel Transform (DSFBT) based on both a discrete Bessel Transform and the HEALPIX angular pixelisation scheme. We test the 3D wavelet transform and as a toy-application, apply a denoising algorithm in wavelet space to the Virgo large box cosmological simulations and find we can successfully remove noise without much loss to the large scale structure. We have described a new spherical 3D isotropic wavelet transform, ideally suited to analyse and denoise future 3D spherical cosmological surveys, which uses a novel Discrete Spherical Fourier-Bessel Transform. We illustrate its potential use for denoising using a toy model. All the algorithms presented in this paper are available for download as a public code called MRS3D at http://jstarck.free.fr/mrs3d.html

We construct an electromagnetic field whose scalar potential is a pulsed-beam wavelet Psi (an analytic continuation of a classical Huygens wavelet). The vector potential A is determined up to three complex parameters by requiring that (a) it satisfies the Lorenz gauge condition with Psi, (b) its current density is supported on the same disk D as the charge density of Psi, (c) it is axisymmetric, and (d) it has the same retarded time dependence as Psi. By choosing one of the parameters in A appropriately, the electromagnetic field generated by the four-potential (A, Psi) can be made null, meaning that E^2=B^2 and E dot B=0. We call such fields coherent because upon being radiated, they do not loiter around the source, generating electromagnetic inertia (a new concept related to reactive energy) but immediately propagate out at the speed of light. The coherent EM wavelets define a twisting null congruence of light rays in Minkowski space, which we show to be identical to the Kerr congruence associated with the Kerr-Newman metric. The latter represents a black hole due to a time-independent charge-current density on a massive disk D spinning at the angular velocity c/a, where a is the radius of D. By contrast, our coherent wavelets are electromagnetic pulsed beams radiated by pulsed charge-current distributions on D, still spinning at the uniform rate c/a.

In this paper we combine partial-wave (`modal') methods with a wavelet analysis of the CMB bispectrum. Our implementation exploits the advantages of both approaches to produce robust, reliable and efficient estimators which can constrain the amplitude of arbitrary primordial bispectra. This will be particularly important for upcoming surveys such as \emph{Planck}. A key advantage is the computational efficiency of calculating the inverse covariance matrix in wavelet space, producing an error bar which is close to optimal. We verify the efficacy and robustness of the method by applying it to WMAP7 data, finding $\fnllocal=38.4\pm 23.6$ and $\fnlequil=-119.2\pm123.6$.

In this paper we present a general approach to multivariate periodic wavelets generated by scaling functions of de la Vall\'ee Poussin type. These scaling functions and their corresponding wavelets are determined by their Fourier coefficients, which are sample values of a function, that can be chosen arbitrarily smooth, even with different smoothness in each direction. This construction generalizes the one-dimensional de la Vall\'ee Poussin means to the multivariate case and enables the construction of wavelet systems, where the set of dilation matrices for the two-scale relation of two spaces of the multiresolution analysis may contain shear and rotation matrices. It further enables the functions contained in each of the function spaces from the corresponding series of scaling spaces to have a certain direction or set of directions as their focus, which is illustrated by detecting jumps of certain directional derivatives of higher order.

Previous research by the author had demonstrated that the spectral sensitivity of the visual system as measured by the visual evoked potential from the human scalp exhibited discrepancies from the spectral sensitivity function as measured by psychophysical techniques. These discrepancies were in the short wavelength (blue) region of the visible spectrum. Such discrepancies were explained by the discovery that blue lights evoked cortical potentials with longer latencies and implicit times than lights which stimulate the middle and long wavelength sensitive mechanisms. The current research deals with a further elucidation of this finding, by studying the fast wavelets (oscillatory potentials) arising in the retina and measured by means of the electroretinogram. High frequency wavelets were also observed at the scalp in the visual evoked potential.

Compactly supported spline-wavelets are used to process simulated transient signals in a noisy environment. It is found that linear-phase spline-wavelets can be effectively used as near-optimal time-domain windows to localize the signal onsets. This technique does not require the a priori knowledge of the signal. Results are demonstrated numerically by the detection of signal arrivals in noise.

A number of problems in network operations and engineering call for new methods of traffic analysis. While most existing traffic analysis methods are fundamentally temporal, there is a clear need for the analysis of traffic across multiple network links--that is, for spatial traffic analysis. In this paper we give examples of problems that can be addressed via spatial traffic analysis. We then propose a formal approach to spatial traffic analysis based on the wavelet transform. Our approach (graph wavelets) generalizes the traditional wavelet transform so that it can be applied to data elements connected via an arbitrary graph topology. We explore the necessary and desirable properties of this approach and consider some of its possible realizations. We then apply graph wavelets to measurements from an operating network. Our results show that graph wavelets are very useful for our motivating problems; for example, they can be used to form highly summarized views of an entire network's traffic load, to gain insight into a network's global traffic response to a link failure, and to localize the extent of a failure event within the network.

The Wavelet in Signal Detection and Identification: Comparative Signal Processing Technology Evaluation Program has been conducted by Lockheed Missiles & Space Company to develop wavelet based signal detection and classification techniques and compare them to Fourier time-frequency methods. The problem domain is submarine detection and identification using transient passive sonar signals. These nontraditional signals are critical in solving current Anti-Submarine Warfare (ASW) problems which include ultra quiet nuclear submarines and third world diesel submarines operating on batteries in shallow water. The Wavelets program supports the ARPA sponsored Multisensor Fusion System Program (MSF) at Lockheed (ARPA contract N00014-91-C-0237).

The Wavelet in Signal Detection and Identification: Comparative Signal Processing Technology Evaluation Program has been conducted by Lockheed Missiles & Space Company to develop wavelet based signal detection and classification techniques and compare them to Fourier time-frequency methods. The problem domain is submarine detection and identification using transient passive sonar signals. These nontraditional signals are critical in solving current Anti-Submarine Warfare (ASW) problems which include ultra quiet nuclear submarines and third world diesel submarines operating on batteries in shallow water. The Wavelets program supports the ARPA sponsored Multisensor Fusion System Program (MSF) at Lockheed (ARPA contract N00014-91-C-0237).

Wavelet analysis of different patterns reveals some symmetries and singularities otherwise hidden in the pattern. Its general methods are briefly reviewed. Examples from turbulence, cavitation, Cherenkov gluon emission and quark-gluon jets structure in QCD are considered.

In this paper, we propose a new redundant wavelet transform applicable to scalar functions defined on high dimensional coordinates, weighted graphs and networks. The proposed transform utilizes the distances between the given data points. We modify the filter-bank decomposition scheme of the redundant wavelet transform by adding in each decomposition level linear operators that reorder the approximation coefficients. These reordering operators are derived by organizing the tree-node features so as to shorten the path that passes through these points. We explore the use of the proposed transform to image denoising, and show that it achieves denoising results that are close to those obtained with the BM3D algorithm.

This work presents a multiscale framework to solve an inverse reinforcement learning (IRL) problem for continuous-time/state stochastic systems. We take advantage of a diffusion wavelet representation of the associated Markov chain to abstract the state space. This not only allows for effectively handling the large (and geometrically complex) decision space but also provides more interpretable representations of the demonstrated state trajectories and also of the resulting policy of IRL. In the proposed framework, the problem is divided into the global and local IRL, where the global approximation of the optimal value functions are obtained using coarse features and the local details are quantified using fine local features. An illustrative numerical example on robot path control in a complex environment is presented to verify the proposed method.

Typographical corrections to the original paper, intended for publication in Physical Review E.

In this paper we present a lifting wavelet based CBRIR image retrieval system that uses color and texture as visual features to describe the content of a retinal fundus images. Our contribution is of three directions. First, we use lifting wavelets 9/7 for lossy and SPL5/3 for lossless to extract texture features from arbitrary shaped retinal fundus regions separated from an image to increase the system effectiveness. This process is performed offline before query processing, therefore to answer a query our system does not need to search the entire database images; instead just a number of similar class type patient images are required to be searched for image similarity. Third, to further increase the retrieval accuracy of our system, we combine the region based features extracted from image regions, with global features extracted from the whole image, which are texture using lifting wavelet and HSV color histograms. Our proposed system has the advantage of increasing the retrieval accuracy and decreasing the retrieval time. The experimental evaluation of the system is based on a db1 online retinal fundus color image database. From the experimental results, it is evident that our system performs significantly better accuracy as compared with traditional wavelet based systems. In our simulation analysis, we provide a comparison between retrieval results based on features extracted from the whole image using lossless 5/3 lifting wavelet and features extracted using lossless 9/7 lifting wavelet and using traditional wavelet. The results demonstrate that each type of feature is effective for a particular type of disease of retinal fundus images according to its semantic contents, and using lossless 5/3 lifting wavelet of them gives better retrieval results for almost all semantic classes and outperform 4-10% more accuracy than traditional wavelet.

Book Source: Digital Library of India Item 2015.187977 dc.contributor.author: Biswaranjan Behera dc.date.accessioned: 2015-07-07T21:35:42Z dc.date.available: 2015-07-07T21:35:42Z dc.date.digitalpublicationdate: 2005-08-20 dc.identifier.barcode: 1990010086523 dc.identifier.origpath: /rawdataupload/upload/0086/523 dc.identifier.copyno: 1 dc.identifier.uri: http://www.new.dli.ernet.in/handle/2015/187977 dc.description.scannerno: 14 dc.description.scanningcentre: IIIT, Allahabad dc.description.main: 1 dc.description.tagged: 0 dc.description.totalpages: 109 dc.format.mimetype: application/pdf dc.language.iso: English dc.publisher: Indian Institute Of Technology Kanpur dc.rights: Out_of_copyright dc.source.library: Indian Institute Of Technology Kanpur dc.subject.classification: Technology dc.subject.classification: Engineering. Technology In General dc.subject.classification: Mechanical Engineering In General. Nuclear Technology. Electrical Engineering. Machinery dc.subject.keywords: An Aspect Of The Dynamics Of Mechanical Face Seals dc.title: Band Limited Wavelets And Wavelet Packets

Book Source: Digital Library of India Item 2015.187977 dc.contributor.author: Biswaranjan Behera dc.date.accessioned: 2015-07-07T21:35:42Z dc.date.available: 2015-07-07T21:35:42Z dc.date.digitalpublicationdate: 2005-08-20 dc.identifier.barcode: 1990010086523 dc.identifier.origpath: /rawdataupload/upload/0086/523 dc.identifier.copyno: 1 dc.identifier.uri: http://www.new.dli.ernet.in/handle/2015/187977 dc.description.scannerno: 14 dc.description.scanningcentre: IIIT, Allahabad dc.description.main: 1 dc.description.tagged: 0 dc.description.totalpages: 109 dc.format.mimetype: application/pdf dc.language.iso: English dc.publisher: Indian Institute Of Technology Kanpur dc.rights: Out_of_copyright dc.source.library: Indian Institute Of Technology Kanpur dc.subject.classification: Technology dc.subject.classification: Engineering. Technology In General dc.subject.classification: Mechanical Engineering In General. Nuclear Technology. Electrical Engineering. Machinery dc.subject.keywords: An Aspect Of The Dynamics Of Mechanical Face Seals dc.title: Band Limited Wavelets And Wavelet Packets