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Adaptive Edge Orientation Analysis

Adaptive Edge Orientation Analysis

引用

conference on wavelet applications in Industrial processing vii

作者： Van Reeth, Eric Bertolino, Pascal Nicolas, Marina Chassery, Jean-Marc STMicroelect 12 Rue Jules Horowitz Grenoble France GIPSA LAB Grenoble France

ISBN: (纸本)9780819479280

This paper presents a method that detects edge orientations in still images. Edge orientation is a crucial information when one wants to optimize the quality of edges after different processings. The detection is carried out in the wavelet domain to take advantage of the multi-resolution features of the wavelet spaces, and locally adapts the resolution to the characteristics of edges. Our orientation detection method consists of finding the local direction along which the wavelet coefficients are the most regular. To do so, the image is divided in square blocks of varying size, in which Bresenham lines are drawn to represent different directions. The direction of the Bresenham line that contains the most regular wavelet coefficients, according to a criterion defined in the paper, is considered to be the direction of the edge inside the block. The choice of the Bresenham line drawing algorithm is justified in this paper, and we show that it considerably increases the angle precision compared to other methods as for instance, the method used for the construction of bandlet bases. An optimal segmentation is then computed in order to adapt the size of the blocks to the edge localization and to isolate in each block at most one contour orientation. Examples and applications on image interpolation are shown on real images.

关键词： wavelets Bandlets Curvelets Edge Direction image Analysis Multi-resolution

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Kolmogorov Superposition Theorem and wavelets for image compression

Kolmogorov Superposition Theorem and Wavelets for image comp...

引用

conference on wavelet applications in Industrial processing vii

作者： Leni, Pierre-Emmanuel Fougerolle, Yohan D. Truchetet, Frederic Univ Bourgogne Lab LE2I CNRS UMR 5158 F-71200 Le Creusot France

ISBN: (纸本)9780819479280

We propose a new compression approached based on the decomposition of images into continuous monovariate functions, which provide adaptability over the quantity of information taken into account to define the monovariate functions: only a fraction of the pixels of the original image have to be contained in the network used to build the correspondence between monovariate functions. The Kolmogorov Superposition Theorem (KST) stands that any multivariate functions can be decomposed into sums and compositions of monovariate functions. The implementation of the decomposition proposed by Igelnik, and modified for image processing, is combined with a wavelet decomposition, where the low frequencies will be represented with the highest accuracy, and the high frequencies representation will benefit from the adaptive aspect of our method to achieve image compression. Our main contribution is the proposition of a new compression scheme, in which we combine KST and multiresolution approach. Taking advantage of the KST decomposition scheme, we use a decomposition into simplified monovariate functions to compress the high frequencies. We detail our approach and the different methods used to simplify the monovariate functions. We present the reconstruction quality as a function of the quantity of pixels contained in monovariate functions, as well as the image reconstructions obtained with each simplification approach.

关键词： image compression wavelets image quality Superposition image restoration Reconstruction algorithms image processing Stochastic processes image encryption image resolution

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Singularity Detection by wavelet Approach: application to Electrocardiogram signal

Singularity Detection by Wavelet Approach: application to El...

引用

conference on wavelet applications in Industrial processing vii

作者： Jalil Bushra Beya Ouadi Fauvet Eric Laligant Olivier CNRS LE2I UMR 5158 F-71200 Le Creusot France

ISBN: (纸本)9780819479280

In signal processing, the region of abrupt changes contains the most of the useful information about the nature of the signal. The region or the points where these changes occurred are often termed as singular point or singular region. The singularity is considered to be an important character of the signal, as it refers to the discontinuity and interruption present in the signal and the main purpose of the detection of such singular point is to identify the existence, location and size of those singularities. Electrocardiogram (ECG) signal is used to analyze the cardiovascular activity in the human body. However the presence of noise due to several reasons limits the doctor's decision and prevents accurate identification of different pathologies. In this work we attempt to analyze the ECG signal with energy based approach and some heuristic methods to segment and identify different signatures inside the signal. ECG signal has been initially denoised by empirical wavelet shrinkage approach based on Steins Unbiased Risk Estimate (SURE). At the second stage, the ECG signal has been analyzed by Mallat approach based on modulus maximas and Lipschitz exponent computation. The results from both approaches has been discussed and important aspects has been highlighted. In order to evaluate the algorithm, the analysis has been done on MIT-BIH Arrhythmia database;a set of ECG data records sampled at a rate of 360 Hz with 11 bit resolution over a 10mv range. The results have been examined and approved by medical doctors.

关键词： wavelet Transform wavelet shrinkage Singularity detection Modulus Maxima Lipschitz Exponent Electrocardiogram

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A SPARSITY-DISTORTION-OPTIMIZED MULTISCALE REPRESENTATION OF GEOMETRY

A SPARSITY-DISTORTION-OPTIMIZED MULTISCALE REPRESENTATION OF...

引用

IEEE International conference on image processing

作者： Sezer, Osman G. Altunbasak, Yucel Guleryuz, Onur G. Georgia Inst Technol Ctr Signal & Image Proc Atlanta GA 30308 USA DoCoMo Comm Lab USA Inc Palo Alto CA 94304 USA

ISBN: (纸本)9781424479948

This paper describes the construction of a new multiresolutional decomposition with applications to image compression. The proposed method designs sparsity-distortion-optimized orthonormal transforms applied in wavelet domain to arrive at a multiresolutional representation which we term the Sparse Multiresolutional Transform (SMT). Our optimization operates over sub-bands of given orientation and exploits the inter-scale and intra-scale dependencies of wavelet coefficients over image singularities. The resulting SMT is substantially sparser than the wavelet transform and leads to compaction that can be exploited by well-known coefficient coders. Our construction deviates from the literature, which mainly focuses on model-based methods, by offering a data-driven optimization of wavelet representations. Simulation experiments show that the proposed method consistently offers better performance compared to the original wavelet-representation and can reach up to 1dB improvements within state-of-the-art coefficient coders.

关键词： sparse multiscale representations sparse orthonormal transforms wavelet transform sparse dictionary

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Recent Advances in Multiresolution Analysis of 3D Meshes and their applications

Recent Advances in Multiresolution Analysis of 3D Meshes and...

引用

conference on wavelet applications in Industrial processing vii

作者： Roy, Michael Foufou, Sebti Truchetet, Frederic Univ Bourgogne Lab Elect Informat & Image CNRS UMR 5158 F-89000 Auxerre France

ISBN: (纸本)9780819479280

3D meshes are widely used in computer graphics applications for approximating 3D models. When representing complex shapes in raw data format, meshes consume a large amount of space. applications calling for compact and fast processing of large 3D meshes have motivated a multitude of algorithms developped to process these datasets efficiently. The concept of multiresolution analysis proposes an efficient and versatile tool for digital geometric processing allowing for numerous applications. In this paper, we survey recent developments in multiresolution methods for 3D triangle meshes. We also show some results of these methods through various applications.

关键词： Multiresolution analysis levels of detail 3D triangle mesh applications

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Real-Time wavelet Based Blur Estimation on Cell BE platform

Real-Time Wavelet Based Blur Estimation on Cell BE platform

引用

conference on wavelet applications in Industrial processing vii

作者： Lukic, Nemanja Platisa, Ljiljana Pizurica, Aleksandra Philips, Wilfried Temerinac, Miodrag Univ Novi Sad Fac Tech Sci Trg Dositeja Obradov 6 Novi Sad Serbia Univ Ghent TELIN IPI IBBT Dept TElecommun & Informat Proc Ghent Belgium

ISBN: (纸本)9780819479280

We propose a real-time system for blur estimation using wavelet decomposition. The system is based on an emerging multi-core microprocessor architecture (Cell Broadband Engine, Cell BE) known to outperform any available general purpose or DSP processor in the domain of real-time advanced video processing solutions. We start from a recent wavelet domain blur estimation algorithm which uses histograms of a local regularity measure called average cone ratio (ACR). This approach has shown a very good potential for assessing the level of blur in the image yet some important aspects remain to be addressed in order for the method to become a practically working one. Some of these aspects are explored in our work. Furthermore, we develop an efficient real-time implementation of the novelty metric and integrate it into a system that captures live video. The proposed system estimates blur extent and renders the results to the remote user in real-time.

关键词： blur estimation image quality wavelets real-time multi-core Cell BE

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Laguerre Gauss analysis for image retrieval based on color texture

Laguerre Gauss analysis for image retrieval based on color t...

引用

conference on wavelet applications in Industrial processing vii

作者： Costantini, Luca Sita, Paolo Capodiferro, Licia Neri, Alessandro Univ Roma TRE Italy Dept Appl Elect Via Vasca Navale 84 I-00146 Rome Italy Fondazione Ugo Bordoni Rome Italy

ISBN: (纸本)9780819479280

In this work a novel technique for color texture representations and classifications is presented. We assume that a color texture can be mainly characterized by two components: structure and color. Concerning the structure, it is analyzed by using the Laguerre-Gauss circular harmonic wavelet decomposition of the luminance channel. At this aim, the marginal density of the wavelet coefficients is modeled by Generalized Gaussian Density (GGD), and the similarity is based on the Kullback-Leibler divergence (KLD) between two GGDs. The color is characterized by the moments computed on the chromatic channels, and the similarity is evaluated by using the Euclidean distance. The overall similarity is obtained by linearly combining the two individual measures. Experimental results on a data set of 640 color texture images, extracted from the "Vision Texture" database, show that the retrieval rates is about 81% when only the structural component is employed, and it rises up to 87% when using both structural and color components.

关键词： image retrieval Laguerre-Gauss functions wavelet analysis color texture Kullback-Leibler divergence

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wavelet Packets for multi- and hyper-spectral imagery

Wavelet Packets for multi- and hyper-spectral imagery

引用

conference on wavelet applications in Industrial processing vii

作者： Benedetto, J. J. Czaja, W. Ehler, M. Flake, C. Hirn, M. Univ Maryland Dept Math Norbert Wiener Ctr College Pk MD 20742 USA

ISBN: (纸本)9780819479280

State of the art dimension reduction and classification schemes in multi- and hyper-spectral imaging rely primarily on the information contained in the spectral component. To better capture the joint spatial and spectral data distribution we combine the wavelet Packet Transform with the linear dimension reduction method of Principal Component Analysis. Each spectral band is decomposed by means of the wavelet Packet Transform and we consider a joint entropy across all the spectral bands as a tool to exploit the spatial information. Dimension reduction is then applied to the wavelet Packets coefficients. We present examples of this technique for hyper-spectral satellite imaging. We also investigate the role of various shrinkage techniques to model non-linearity in our approach.

关键词： wavelets Principal component analysis Dimension reduction Satellite imaging image classification Data modeling Discrete wavelet transforms Hyperspectral systems Visualization Reconstruction algorithms

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Empirical Modal Decomposition applied to cardiac signals analysis

Empirical Modal Decomposition applied to cardiac signals ana...

引用

conference on wavelet applications in Industrial processing vii

作者： Beya, O. Jalil, B. Fauvet, E. Laligant, O. CNRS LE2I IUT UMR 5185 F-71200 Le Creusot France

ISBN: (纸本)9780819479280

In this article, we present the method of empirical modal decomposition (EMD) applied to the electrocardiograms and phonocardiograms signals analysis and denoising. The objective of this work is to detect automatically cardiac anomalies of a patient. As these anomalies are localized in time, therefore the localization of all the events should be preserved precisely. The methods based on the Fourier Transform (TFD) lose the localization property [13] and in the case of wavelet Transform (WT) which makes possible to overcome the problem of localization, but the interpretation remains still difficult to characterize the signal precisely. In this work we propose to apply the EMD (Empirical Modal Decomposition) which have very significant properties on pseudo periodic signals. The second section describes the algorithm of EMD. In the third part we present the result obtained on Phonocardiograms (PCG) and on Electrocardiograms (ECG) test signals. The analysis and the interpretation of these signals are given in this same section. Finally, we introduce an adaptation of the EMD algorithm which seems to be very efficient for denoising. Lastly, prospects and a conclusion complete this work.

关键词： EMD Phonocardiograms Electrocardiograms signal processing

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NO-REFERENCE image QUALITY ASSESSMENT OF wavelet CODED imageS

NO-REFERENCE IMAGE QUALITY ASSESSMENT OF WAVELET CODED IMAGE...

引用

IEEE International conference on image processing

作者： Khan, Mohd Haroon Moinuddin, Athar A. Khan, Ekram Ghanbari, Mohammed Aligarh Muslim Univ Dept Elect Engn Aligarh Uttar Pradesh India Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ Essex England

ISBN: (纸本)9781424479948

In the modern era of Internet, many user-end applications require the estimation of quality of images directly from the bitstreams, as the original image may not be available. This is a challenging issue. In this paper, we propose a novel approach of no-reference (NR) objective quality assessment of wavelet coded images. The proposed method is based on estimation of the contribution of each received bit in an embedded bitstream in the reduction of mean-square error (MSE), assuming initial image with all zero pixel values. Mathematical analysis is carried out to estimate the quantization errors in terms of MSE reduction, in wavelet-based SPIHT coding algorithm. The simulation results demonstrate the effectiveness of the proposed method.

关键词： No-reference quality estimation MSE PSNR wavelet transform SPIHT

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