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检索条件"任意字段=Conference on Wavelet Applications in Signal and Image Processing VII"

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wavelet applications in signal and image processing III. Part 1 (of 2)

Wavelet Applications in Signal and Image Processing III. Par...

引用

wavelet applications in signal and image processing III. Part 1 (of 2)

This conference proceedings contain 76 papers. Topics covered include: Theoretical developments;wavelet design;Gabor transforms;signal analysis;image processing;Denoising;Fractals;Compression;Edge representation;Fast ... 详细信息

ISBN: (纸本)0819419281

关键词： wavelet transforms

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Group-normalized wavelet packet signal processing

Group-normalized wavelet packet signal processing

引用

4th conference on wavelet applications

作者： Shi, Z Bao, Z Xidian Univ. (China)

ISBN: (纸本)0819424935

Since the traditional wavelet and wavelet packet coefficients do not exactly represent the strength of signal components at the very time(space)-frequency tilling, Group-Normalized wavelet Packet Transform (GNWPT), is presented for nonlinear signal filtering and extraction from the clutter or noise, together with the space(time)frequency masking technique. The extended l(p)-entropy improves the performance of GNWPT. For perception based image, soft-logic masking (different from Donoho's method) is emphasized to remove the aliasing with edge preserved. Lawton's method for complex valued wavelets construction is extended to generate the complex valued compactly supported wavelet packets for radar signal extraction. This kind of wavelet packets are symmetry and unitary orthogonal. Well-defined wavelet packets are chosen by the analysis remarks on their time-frequency characteristics. For real valued signal processing, such as images and ECG signal, the compactly supported spline or biorthogonal wavelet packets are preferred for perfect de-noising and filtering qualities.

关键词： group-normalization space(time)-frequency masking soft logic complex wavelet packets

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Performance analysis of a wavelet-based hybrid neurosystem for signal classification

Performance analysis of a wavelet-based hybrid neurosystem f...

引用

4th conference on wavelet applications in signal and image processing

作者： Nguyen, CT Gong, KF USN UNDERSEA WARFARE CTRDIV NEWPORTNEWPORTRI 02841

ISBN: (纸本)0819422134

This paper is concerned with the problem of determining performance of a wavelet-based hybrid neurosystem trained to provide efficient feature extraction and signal classification. The hybrid network consists of a parallel array of neurosystems. Each neurosystem is constructed with three single neural networks; two of which are feature extraction networks, and the other is a classification network, are provided with magnitude and location information of the wavelet transform coefficients, respectively, and are trained with self-organizing rules. Their outputs are then presented to the classification network for pattern recognition. Based on the topological maps provided by the feature extraction neural networks, the back-propagation algorithm is used to train the third network for pattern recognition. The combination of wavelet, wavelet transform, and hybrid neural network architecture and advanced training algorithms in the design makes the system unique and provides high classification accuracy. In this paper, system performance is shown to be intrinsically related to basis kernel function used in feature extraction. A method for selecting the optimal basis function and a performance analysis using simulated data under various noise condition are presented and compared against other pattern recognition techniques.

关键词： wavelets hybrid neurosystem hybrid architecture signal classifications pattern recognition

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Adaptive Kalman filtering deconvolution via dyadic wavelet transform

Adaptive Kalman filtering deconvolution via dyadic wavelet t...

引用

conference on wavelet applications in signal and image processing viiI

作者： Dong, EQ Liu, GZ Zhang, ZP Xian Jiao Tong Univ Xian 710049 Peoples R China

ISBN: (纸本)0819437646

A new approach of adaptive Kalman filtering deconvolution (AKFD) is developed basing on dyadic wavelet transform. The technique discards the assumption of stationarity for signals in predictive deconvolution, and overcomes improving resolution at the price of decreasing signal-to-noise rate (SNR) obviously. The technique can well compress the reflection waveforms, but the noises are not variable in substance. So it has a better ability of resistance noise. Suppressing false reflections in dyadic wavelet transform domain is better than by applying AKFD in time domain. In addition the technique also has the characteristic of adaptive Kalman filtering in every band for a signal respectively, it enhances the adaptation of Kalman filtering, so the resolution is obvious higher than that one in time domain. A great deal of numerical models and real seismic data indicate that the technique has obvious effect. At the same time, the technique also overcomes the drawback of increasing the low-frequency component of AKFD in time domain. A great deal of numerical models and real seismic data indicate that the technique has obvious effect. The approach not only suits for seismic data, but also can be used for reference to another similar signal processing.

关键词： dyadic wavelet transform adaptive Kalman filtering deconvolution nonstationary signal

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Experimental Approach On Thresholding Using Reverse Biorthogonal wavelet Decomposition For Eye image

Experimental Approach On Thresholding Using Reverse Biorthog...

引用

3rd IEEE International conference on signal and image processing applications (ICSIPA)

作者： Abidin, Zaheera Zainal Manaf, Mazani Shibhgatullah, Abdul Samad Univ Teknikal Malaysia Melaka UTeM Hang Tuah Jaya 76100 Melaka Malaysia Univ Teknol MARA Selangor 40450 Malaysia

ISBN: (纸本)9781479902699;9781479902675

This study focus on compression in wavelet decomposition for security in biometric data. The objectives of this research are two folds: a) to investigate whether compressed human eye image differ with the original eye and b) to obtain the compression ratio values using proposed methods. The experiments have been conducted to explore the application of sparsity-norm balance and sparsity-norm balance square root techniques in wavelet decomposition. The eye image with [320x280] dimension is used through the wavelet 2D tool of Matlab. The results showed that, the percentage of coefficients before compression energy was 99.65% and number of zeros were 97.99%. However, the percentage of energy was 99.97%, increased while the number of zeros was same after compression. Based on our findings, the impact of the compression produces different ratio and with minimal lost after the compression. The future work should imply in artificial intelligent area for protecting biometric data.

关键词： data compression iris recognition security of data wavelet transforms

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A wavelet to DCT progressive image transcoder

A wavelet to DCT progressive image transcoder

引用

IEEE International conference on image processing (ICIP 2000)

作者： Hu, PC Kaveh, M Zhang, ZL Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA

ISBN: (纸本)0780362985

A transcoder design is proposed in which the transcoder loads the pre-encoded embedded wavelet coefficients and computes the DCT coefficients. The resulting DCT coefficients are quantized and sorted by multi-grid embedded (MGE) coding to output a compressed DCT bitstream. This transcoder has two operational modes: open-loop and closed-loop. The open-loop scheme is designed for direct transcoding, targeted to applications in which receivers are connected to the transcoder through a fast network and demand the same rate. In contrast, the closed-loop scheme is designed for fully progressive transcoding, targeted to receivers which are connected to the transcoder through a slow network connection,: and may demand heterogeneous rates. The performance of the transcoder is evaluated in both open-loop mode and close-loop mode.

关键词： image coding

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Determination of Benign and Malign Lesions by Fusion of The Different Phases of Liver MR 25

Determination of Benign and Malign Lesions by Fusion of The ...

引用

25th signal processing and Communications applications conference (SIU)

作者： Ervural, Saim Ceylan, Murat KTO Karatay Univ Elekt Elekt Muhendisligi Bolumu Akabe Mh Alaaddin Kap Cd 130 Konya Turkey Selcuk Univ Elekt Elekt Muhendisligi Bolumu Alaeddin Keykubat Yerleskesi Konya Turkey

ISBN: (纸本)9781509064946

In this study, different phases of T1-weighted, dynamic contrast-enhanced liver magnetic resonance (MR) images were combined with wavelet-based image fusion to support decisions of radiologists. Used images has labelled as 6 different focal lesion types which focal nodular hyperplasia (FNH), hemangioma, cyst, colangiocellular carcinoma (CCC), hepatocellular carcinoma (HCC) and liver metastases. In application used images are taken by 4 different phases called pre-contrasted, arterial, portal venous, and delay venous from 30 patient. images registered with efficient subpixel registration by cross correlation method. Discrete wavelet transform(DWT) based image fusion algorithm used and maximum selection method applied as fusion rule. As result 180 fused images obtained The performances of fusion results compared with structural similarity index (SSIM), peak to noise ratio (PSNR) and fusion factor (FF) metrics. In the fusion of portal venous phase and delay venous phase images, 98.7% SSIM and 74.95 dB PSNR values were obtained, respectively. FF value in the fusion of pre-contrast phase & arterial phase images measured as 7.258. In comparison of lesion types were represented with 98.5% SSIM

关键词： image fusion liver MR imaging wavelet transform

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Classification of Cylindrical Targets by wavelet Transform and ROC Analysis

Classification of Cylindrical Targets by Wavelet Transform a...

引用

IEEE 16th signal processing and Communications applications conference

作者： Makal, Senem Durak, Luetfiye Kizilay, Ahmet Yildiz Tekn Univ Elekt & Haberlesme Muhendisligi Bolumu Istanbul Turkey

ISBN: (纸本)9781424419982

A set of features are derived from scattered fields calculated by using the image technique formulation and Method of Moment (MoM) and a database is formed by using two cylindrical targets at certain angles. After the application of wavelet transform for feature extraction from this database, the coefficients of the signal are used as the inputs of the artificial neural networks. The real performances of the networks are investigated by ROC (Receiver Operating Characteristic) analysis. This work aims to diminish the size of the database smaller by wavelet transform for finding the corresponding cylindrical target from the scattered field values.

关键词： wavelet transforms

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Tight frame approximations for Gabor and wavelet frames

Tight frame approximations for Gabor and wavelet frames

引用

conference on wavelets - applications in signal and image processing IX

作者： Han, DG Univ Cent Florida Dept Math Orlando FL USA

ISBN: (纸本)0819441929

Given a window function which generates a Gabor (resp. wavelet) frame. We consider the best approximation by those window functions that generate normalized tight (or just tight) frames. Using a parameterizations of window functions by certain class of operators in the von Neumann algebras associated with shift operators in time and frequency over certain lattices, we are able to prove that for any window function of a Gabor frame, there exists a unique window function which generates a tight Gabor frame and is the best approximation (among all the tight Gabor frames) for the given window function. More generally, we show that this is true for any frame induced by a projective unitary representation for a group. However, this result is not valid for wavelet frames. We will provide a restricted approximation result for semi-orthogonal wavelet frames.

关键词： frame Gabor and wavelet frames unitary systems operator algebra frame aproximation

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signal/background separation by wavelet packets for detection of microcalcifications in mammograms

Signal/background separation by wavelet packets for detectio...

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4th conference on wavelet applications in signal and image processing

作者： Yoshida, H Nishikawa, RM Giger, ML Doi, K UNIV CHICAGO DEPT RADIOLKURT ROSSMANN LABS RADIOL IMAGE RESCHICAGOIL 60637

ISBN: (纸本)0819422134

We developed a method of weighted wavelet packets for separation of small, low contrast signals from large, inhomogeneous background. Our method was applied to the enhancement of microcalcifications on digital mammograms, which appear as small bright spots superimposed on the background representing the structure of breast tissue, for improvement of the performance of our computer-aided diagnosis scheme for detection of clustered microcalcifications. Our method first approximates signals if interest by a set of wavelets that are extracted from a wavelet packet dictionary by means of the matching pursuit algorithm. The selected set of wavelets is then subjected to a supervised learning process for optimization of the weights assigned to individual time-frequency tiles for enhancement of the microcalcifications and suppression of the background structures. In an analysis of 82 regions of interest extracted from our mammographic database, our new method showed a sensitivity of 92 percent and a specificity of 75 percent. Our new method is shown to perform better than our previous method based on the fixed-weight, orthogonal wavelet transform.

关键词： wavelet packets computer-aided diagnosis digital mammograms microcalcifications

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