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Multiple description image coding using natural scene statistics

Multiple description image coding using natural scene statis...

引用

2005 IEEE International conference on Acoustics, Speech, and signal processing, ICASSP '05

作者： Channappayya, Sumohana S. Heath Jr., Robert W. Bovik, Alan C. University of Texas at Austin Department of Electrical and Computer Engineering 1 University Station C0803 Austin TX 78712-0240 United States

ISBN: (纸本)0780388747

The statistics of natural scenes in the wavelet domain are accurately characterized by the Gaussian Scale Mixture (GSM) model. The model lends itself easily to analysis and many applications that use this model are emerging (for e.g., denoising, watermark detection). In this paper, we present an error-resilient image communications application that uses the GSM model and Multiple description coding (MDC) to provide error-resilience. We derive a rate-distortion bound for GSM random variables, derive the redundancy rate distortion function, and finally implement an MD image communication system. © 2005 IEEE.

关键词： image coding

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Architecture of wavelet packet transform for 1-D signal

Architecture of wavelet packet transform for 1-D signal

引用

Canadian conference on Electrical and Computer Engineering 2005

作者： Aroutchelvame, S.M. Raahemifar, K. Dept. of Electrical and Computer Engg. Ryerson University Toronto Ont. Canada

Many signal and image processing applications will be more benefited if the transform gives good spectral and temporal resolution in arbitrary regions of the time-frequency plane that is provided by the Discrete wavelet Packet Transform (DWPT). In this paper, the architecture for lifting scheme based Daubechies 9/7 wavelet is proposed. The proposed architecture performs both forward and inverse transform. The architecture does not require any extra memory/FIFOs to store the intermediate results. The proposed architecture is verified by performing DWPT for images of size 64×64. The architecture has been described in VHDL at the RTL level and simulated successfully using ModelSim simulation environment. © 2005 IEEE.

关键词： wavelet transforms

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Comparison of redundant wavelet schemes for multiple description coding of video sequences

Comparison of redundant wavelet schemes for multiple descrip...

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2005 IEEE International conference on Acoustics, Speech, and signal processing, ICASSP '05

作者： Petrisor, Teodora Tillier, Christophe Pesquet-Popescu, Béatrice Pesquet, Jean-Christophe ENST Signal and Image Processing Department 46 rue Barrault 75634 Paris Cédex 13 France IGM and UMR-CNRS 8049 Université de Marne-la-Vallée 5 Bd Descartes Champs sur Marne 77454 Marne la Vallee Cedex 2 France

ISBN: (纸本)0780388747

Multiple description coding (MDC) recently appeared as a joint source-channel coding technique specifically designed for real-time multimedia applications over best effort switched packet networks such as Internet, in order to cope with packet losses due to transmission errors or network congestion. In this paper we compare several redundant wavelet decompositions in the framework of multiple description of scalable video coding. A special attention is paid to the optimal design of the central decoder. Simulation results are provided for motion-compensated filter banks so as to evaluate the efficiency of the central and side decoding strategies. Compared with other techniques, a key factor of the proposed analysed schemes is their reduced redundancy factor. © 2005 IEEE.

关键词： Video signal processing

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Fingerprint image enhancement using filtering techniques

Fingerprint image enhancement using filtering techniques

引用

IEEE 13th signal processing and Communications applications conference, SIU 2005

作者： Altun, A. Alpaslan Allahverdi, Novruz Koçer, H. Erdinç Yilmaz, Tarik Alan, Selahattin Bilgisayar Sistemleri Öǧretmenliǧi Bölü mü Selçuk Üniversitesi Konya Matematik Uygulama ve Araştirma Merkezi Selçuk Üniversitesi Konya

ISBN: (纸本)0780392396

Minutiae extraction is one of the most important steps for automatic fingerprint identification and classification systems. The performance of minutiae extraction can be changed depends on an image enhancement algorithm. Minutiaes of the fingerprint are local discontinuities in the fingerprint pattern as terminations and bifurcations. In this work, we propose two methods for fingerprint image enhancement. The first one is carried out using local histogram equalization, noise reduction filters, and image binarization. In noise reduction step, linear, median and wiener filters are used. The results are compared with each other with regard to performance. In the second method a contourlet transform is applied. Contourlet transform is a new extension of the wavelet transform in two dimensions using multiscale and directional filter banks for direct grayscale enhancement. The results achieved from this method are compared with those obtained from wavelet transform. Both methods mentioned above are increase the efficiency of minutiae extraction with regard to minutiae specification and process time. ©2005 IEEE.

关键词： image analysis

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Removal of spatially correlated noise by independent component analysis

Removal of spatially correlated noise by independent compone...

引用

conference on applications of Neural Networks and Machine Learning in image processing IX

作者： Zeng, XY Chen, YW Nakao, Z Van Alphen, D Calif State Univ Northridge Dept Elect & Comp Engn Northridge CA 91325 USA

ISBN: (纸本)0819456462

In this paper, we apply independent component analysis (ICA) to the reduction of spatially correlated additive noise in images. We take a degraded image as the mixture of the noise and the original image, which are statistically independent. From a view of blind signal separation, we try to restore the original image from two linear mixtures. Motivated by the fact that autocorrelation exists in the neighborhoods of the image and the noise, we design another mixture using the diffusion equation. Then we employ independent component analysis to separate the image and the noise from the two mixtures. Simulation experiments are carried out to remove the Poisson noise from images. Experimental results indicate an impressive performance of the proposed method. Furthermore, the proposed method can be combined with the wavelet Shrinkage method to improve the denoising performance.

关键词： image denoising spatially correlated noise independent component analysis diffusion equation

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image classifier for the TJ-II Thomson scattering diagnostic: Evaluation with a feed forward neural network

Image classifier for the TJ-II Thomson scattering diagnostic...

引用

1st International Work-conference on the Interplay Between Natural and Artificial Computation

作者： Farias, G Dormido, R Santos, M Duro, N Univ Nacl Educ Distancia ETSI Informat Dpto Informat & Automat Madrid 28040 Spain Univ Complutense Madrid Dpto Arquitectura Comp & Automat Fac CC Fis E-28040 Madrid Spain

ISBN: (纸本)3540263195

There are two big stages to implement in a signal classification process: features extraction and signal classification. The present work shows up the development of an automated classifier based on the use of the wavelet Transform to extract signal characteristics, and Neural Networks (Feed Forward type) to obtain decision rules. The classifier has been applied to the nuclear fusion environment (TJ-II stellarator), specifically to the Thomson Scattering diagnostic, which is devoted to measure density and temperature radial profiles. The aim of this work is to achieve an automated profile reconstruction from raw data without human intervention. Raw data processing depends on the image pattern obtained in the measurement and, therefore, an image classifier is required. The method reduces the 221.760 original features to only 900, being the success mean rate over 90%. This classifier has been programmed in MATLAB.

关键词： signal processing

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Complex Discrete wavelet Transform based motion estimation for vision-based tracking of targets

Complex Discrete Wavelet Transform based motion estimation f...

引用

13th European signal processing conference, EUSIPCO 2005

作者： Yilmaz, Sener Severcan, Mete Image Processing Department MGEO Division ASELSAN Inc. PO. Box: 30 Etlik 06011 Ankara Turkey Department of Electrical and Electronics Engineering Middle East Technical University Inonu Bulvari 06531 Ankara Turkey

ISBN: (纸本)1604238216

For tracking applications, the estimation of the "true" motion vector is crucial. The Complex Discrete wavelet Transform (CDWT) based motion estimation algorithm developed by Magarey and Kingsbury produced superior results for the estimation of the dense flow field. In this work, the use of the CDWT-based motion estimation algorithm for the vision-based tracking of targets has been evaluated. First, a comparison of the results of the CDWT-based ME algorithm with the results of the Lucas and Kanade's (LK) and Horn and Schunk's (HS) motion estimation algorithms is performed. Second, the tracking performances are compared for the cases of CDWT-based and LK-based flow fields. Lastly, the tracking performance of the proposed tracker is evaluated by using a number of test sequences and is compared to the Correlation and Mean Shift Tracker. It is observed that it can successfully track various different targets and is robust to changes of the target signature.

关键词： Motion estimation

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Character recognition by best anisotropic local bases and neural networks

Character recognition by best anisotropic local bases and ne...

引用

conference on image processing - Algorithms and Systems IV

作者： Uzunov, V Gotchev, A Huttunen, H Egiazarian, K Astola, J Tampere Univ Technol Inst Signal Proc FIN-33101 Tampere Finland

ISBN: (纸本)0819456454

The best basis paradigm is a lower cost alternative to the principal component analysis (PCA) for feature extraction in pattern recognition applications. Its main idea is to build a collection of bases and search for the best one in terms of e.g. best class separation. Recently, fast best basis search algorithms have been generalized for anisotropic wavelet packet bases. Anisotropy is preferable for 2-D objects since it helps capturing local image features in a better way. In this contribution, the best anisotropic basis search framework is applied to the problem of recognition of characters captured from gray-scale pictures of car license plates. The goals are to simplify the classifier and to avoid a preliminary binarization stage by extracting features directly from the gray-scale images. The collection of bases is formed by anisotropic wavelet packets. The search algorithm seeks for a basis providing the lowest-dimensional data representation preserving the inter-class separability for given training data set, measured as Euclidean distance between class centroids. The relationship between the feature extractor and classifier complexity is clarified by training neural networks for different local bases. The proposed methodology shows its superiority to PCA as it yields equal and even lower classification error rate with considerably reduced computational costs.

关键词： wavelet packets anisotropy neural networks optical character recognition

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Nonlinear Analyses and Algorithms for Speech processing - International conference on Non-Linear Speech processing, NOLISP 2005, Revised Selected Papers

Nonlinear Analyses and Algorithms for Speech Processing - In...

引用

International conference on Non-Linear Speech processing, NOLISP 2005

ISBN: (纸本)3540312579

The proceedings contain 32 papers. The topics discussed include: Neuro-fuzzy logic in signal processing for communications: from bits to protocols;connected operators for signal and image processing;exploiting high-level information provided by ALISP in speaker recognition;parameter optimization in a text-dependent cryptographic-speech-key generation task;the COST-277 speech database;bispectrum estimators for voice activity detection and speech recognition;on the acoustic-to-electropalatographic mapping;support vector machines applied to the detection of voice disorders;segment boundaries in low latency phonetic recognition;spotting multilingual consonant-vowel units of speech using neutral network models;and method for real-time signal processing via wavelet transform.

关键词： Speech processing

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Performance analysis of wavelet based restoration for passive millimeter-wave images

Performance analysis of wavelet based restoration for passiv...

引用

conference on Passive Millimeter-Wave Imaging Technology viiI

作者： Park, H Kim, SH Singh, MK Choi, JH Lee, HJ Kim, YH GIST Dept Mechatron Kwangju South Korea

ISBN: (纸本)0819457744

A poor inherent resolution capability of the passive millimeter-wave (PMMW) imaging becomes a problem in many applications. The need for efficient post-processing to achieve resolution improvement is being increasingly recognized. To obtain high- and super-resolution PMMW imaging, many restoration methods have been developed and evaluated. In this paper, two recent advanced wavelet based methods are discussed;Fourier-wavelet regularized deconvolution (ForWaRD) and multiscale entropy method. The ForWaRD is a linear deconvolution algorithm that performs noise regularization via scalar shrinkage in both the Fourier and wavelet domains. The ForWaRD has been reported to be efficient and applicable to all ill-conditioned deconvolution problems. The multiscale entropy method, which generalized the wavelet-regularized iterative methods, is advance of the maximum entropy method (MEM), which is more effective and leads to efficient restoration. These two methods have not been applied and analyzed in the PMMW images which were highly blurred and low signal to noise circumstance. We have studied the restoration performance of wavelet-based methods in the PMMW imaging comparing with particular reference to the Lorentzian method. The evaluation has been performed with actual radiometer imaging with the 94 GHz mechanically scanned radiometer as well as simulation. In the actual radiometer imaging, a simple blind restoration method was exploited with blur identification. To compare the restored image fidelity, objective and subjective criteria were used, and the super-resolution capability was also checked. Comparison of the linear and non-linear methods revealed the preferable bandwidth extension of the non-linear methods. In the non-linear methods, the multiscale entropy and Lorentzian, they showed their strength and weakness.

关键词： super-resolution PMMW imaging image restoration wavelet-based deconvolution

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