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International IEEE Conference on signal-image Technologies and Internet-Based System

作者： Emad-Ul-Haq Qazi Muhammad Hussain Hatim Aboalsamh Wadood Abdul Saeed Bamatraf Ihsan Ullah Department of Computer Science and Department of Computer Engineering King Saud University Riyadh Saudi Arabia King Saud University Riyadh Riyadh Province SA Department of Science and Technology University of Napoli ‘Parthenope’ Naples Italy

Epilepsy is a neurological disorder disease that affects more than 55 million people in the world. In this paper, we have proposed an efficient intelligent pattern recognition system for the classification of epileptic and non-epileptic electroencephalogram (EEG) signals. For this purpose, we used state-of-the-art machine learning technique, i.e., SVM (support vector machines) to classify epileptic and non-epileptic signals. Two (02) different classes of signals are used in this study, i.e., non-epileptic with open eyes and epileptic in seizure condition. One hundred (100) subjects from each class were employed for extraction of discriminatory features and classification purpose. After pre-processing of EEG signals, we use discrete wavelet transform (DWT) to decompose signals upto level 5. Then various features, i.e., energy, entropy and standard deviation are extracted from wavelet bands. Next, we use these features in the classification of signals. We achieved the classification accuracy of 100 % at delta band (0 to 3 Hz) and theta band (3 to 6 Hz). The comparisons with the previous studies show the significance of this system, which can be utilized in real-time as well as in offline clinical applications.

关键词： Feature extraction Electroencephalography Discrete wavelet transforms Support vector machines Time-frequency analysis Epilepsy Standards

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wavelet Transform Based Fingerprint Recognition 23

Wavelet Transform Based Fingerprint Recognition

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23nd signal processing and Communications applications Conference (SIU)

作者： Caliskan, Abidin Ertugrul, Omer Faruk BATMAN Univ Bilgisayar Muhendisligi Bolumu Batman Turkey BATMAN Univ Elekt & Elekt Muhendisligi Bolumu Batman Turkey

ISBN: (纸本)9781467373869

A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual. Today, fingerprint has a wide field of use in several areas of biometric system. It is especially used in human identification and verification, which is more reliable than the traditional methods used for access. In this work, Gabor wavelet Transform based fingerprint recognition system has been realized. The wavelet features are extracted from the gray-scale fingerprint image. Finally, the k nearest neighbor classifier is used for the recognition of fingerprint images. The proposed algorithm is tested on images from PolyU high resolution fingerprint database. Experimental results have shown that the proposed method can improve the accuracy of existing methods.

关键词： biometric system fingerprint recognition gabor wavelet transform k nearest neighbor

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Bayesian Estimation of the Multifractality Parameter for image Texture Using a Whittle Approximation

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IEEE TRANSACTIONS ON image processing 2015年第8期24卷 2540-2551页

作者： Combrexelle, Sebastien Wendt, Herwig Dobigeon, Nicolas Tourneret, Jean-Yves McLaughlin, Stephen Abry, Patrice Univ Toulouse Inst Rech Informat Toulouse Lab Ecole Natl Super Elect Electrotech Informat Hy Natl Petr Inst F-31062 Toulouse France Heriot Watt Univ Sch Engn & Phys Sci Edinburgh EH14 4AS Midlothian Scotland Ecole Normale Super Lyon Dept Phys F-69007 Lyon France

Texture characterization is a central element in many image processing applications. Multifractal analysis is a useful signal and image processing tool, yet, the accurate estimation of multifractal parameters for image texture remains a challenge. This is due in the main to the fact that current estimation procedures consist of performing linear regressions across frequency scales of the 2D dyadic wavelet transform, for which only a few such scales are computable for images. The strongly non-Gaussian nature of multifractal processes, combined with their complicated dependence structure, makes it difficult to develop suitable models for parameter estimation. Here, we propose a Bayesian procedure that addresses the difficulties in the estimation of the multifractality parameter. The originality of the procedure is threefold. The construction of a generic semiparametric statistical model for the logarithm of wavelet leaders;the formulation of Bayesian estimators that are associated with this model and the set of parameter values admitted by multifractal theory;the exploitation of a suitable Whittle approximation within the Bayesian model which enables the otherwise infeasible evaluation of the posterior distribution associated with the model. Performance is assessed numerically for several 2D multifractal processes, for several image sizes and a large range of process parameters. The procedure yields significant benefits over current benchmark estimators in terms of estimation performance and ability to discriminate between the two most commonly used classes of multifractal process models. The gains in performance are particularly pronounced for small image sizes, notably enabling for the first time the analysis of image patches as small as 64 x 64 pixels.

关键词： Texture characterization multifractal analysis wavelet leaders Bayesian estimation Whittle approximation multiplicative cascades fractional Brownian motion

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Hierarchical multi-resolution decomposition of statistical shape models

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signal image AND VIDEO processing 2015年第6期9卷 1473-1490页

作者： Cerrolaza, Juan J. Villanueva, Arantxa Cabeza, Rafael Univ Publ Navarra Dept Elect & Elect Engn Pamplona 31006 Spain

Active shape models (ASMs) are some of the most actively used model-based segmentation approaches, whose usefulness has been successfully demonstrated in a wide variety of applications. However, they suffer from two important drawbacks: (1) the large number of training samples required to adequately model the object of interest and (2) their dependence on the initialization. Partial solutions for both of these problems have been proposed but come at the expense of a significant increase in computation time, which is of great importance in current applications. Based on the wavelet transform, the present paper proposes a new full multi-resolution framework that allows not only to hierarchically decompose any type of shape into a set of bands of information, but also to create instances of the shape with different degrees of detail. This is a multi-resolution decomposition of the shape space. Combining this new approach with a multi-resolution Gaussian pyramid of the image, the result is a new full multi-resolution hierarchical ASM that reduces both the size of the training set required and the initialization dependence while improving the classical segmentation algorithms accuracy and computational complexity. The advantages of this new algorithm have been successfully verified over two different databases.

关键词： Active shape model Statistical shape models Multi-resolution analysis image segmentation wavelet transform

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Electrical Measurement System in Milling Balance Machine Based on Embedded Optimization 9

Electrical Measurement System in Milling Balance Machine Bas...

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9th International Symposium on Multispectral image processing and Pattern Recognition (MIPPR) - Remote Sensing image processing, Geographic Information Systems, and Other applications

作者： Wang, Yijun Mei, Yushan Changchun Univ Sci & Technol Coll Elect & Informat Engn Changchun 130022 Jilin Peoples R China Changchun China Opt Sci & Technol Museum Changchun 130000 Jilin Peoples R China

ISBN: (纸本)9781510600560

Electrical measurement system in milling balance machine currently consists of micro-controller and peripheral devices. The structure has the problems which include low integration, single signal processing algorithms and great measurement error. Therefore, electrical measurement system in milling balance machine based on embedded optimization is presented in the paper. Firstly, the device control electrical measuring system by ARM subsystem of OMAP dual-core architecture and DSP subsystem realizes digital signal processing and unbalance computing. Also, the low-pass filtering circuit is designed for solving frequency interference. Secondly, the system implement digital band-pass tracking filter based on harmonic wavelet packet. Thirdly, the system extracts any period of weak signal characteristics using the unlimited segmentation features harmonic for wavelet packet signal in the frequency domain. Simulation results show that the system effectively inhibits nearly frequency signal interference, improves signal to noise ratio, and reduces the initial imbalance signal characteristics. And test results improve that precision indexes and technical specifications could meet the design goals.

关键词： signal processing OMAP Harmonic wavelet packet Tracking filter Near frequency interference

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Iris image Compression using wavelets Transform Coding 2

Iris Image Compression using Wavelets Transform Coding

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2nd International Conference on signal processing and Integrated Networks (SPIN)

作者： Paul, Arnob Khan, Tanvir Zaman Podder, Prajoy Ahmed, Rafi Rahman, M. Muktadir Khan, Mamdudul Haque IEM Dept ECE Kolkata India KUET Dept ECE Khulna 9203 Bangladesh

ISBN: (纸本)9781479959914

Iris recognition system for identity authentication and verification is one of the most precise and accepted biometrics in the world. Portable iris system mostly used in law enforcement applications, has been increasing more rapidly. The portable device, however, requires a narrow-bandwidth communication channel to transmit iris code or iris image. Though a full resolution of iris image is preferred for accurate recognition of individual, to minimize time in a narrow-bandwidth channel for emergency identification, image compression should be used to minimize the size of image. This paper has investigated the effects of compression particularly for iris image based on wavelet transformed image, using Spatial-orientation tree wavelet (STW), Embedded Zero tree wavelet (EZW) and Set Partitioning in hierarchical trees (SPIHT), to identify the most suitable image compression. In this paper, Haar wavelet transform is utilized for image compression and image decomposition, by varying the decomposition level. The results have been examined in terms of Peak signal to noise ratio (PSNR), Mean square Error (MSE), Bit per Pixel Ratio (BPP) and Compression ratio (CR). It has been evidently found that wavelet transform is more effective in the image compression, as recognition performance is minimally affected and the use of Haar transform is ideally suited. CASIA, MMU iris database have been used for this purpose.

关键词： iris recognition image compression mean square error peak signal to noise ratio(PSNR) wavelet decomposition

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signal and image processing techniques for VLSI failure analysis

Signal and image processing techniques for VLSI failure anal...

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International Conference on Informatics and Computing (ICIC)

作者： A. Boscaro S. Jacquir K. Sanchez P. Perdu S. Binczak Le2i UMR CNRS 6306 Univ. Bourgogne Franche-Comté Dijon France Centre National d'Études Spatiales Toulouse France Centre National d'Etudes Spatiales Direction des Lanceurs Paris ÃŽle-de-France FR Temasek Laboratories Nanyang Technological University Singapore

ISBN: (纸本)9781509016495

For VLSI, internal electrical measurements are key steps to solve design debug issues and to perform failure analysis. Due to multiple metal layers, active areas of the chip are only accessible from the backside of the die. The ability of optical contactless techniques to operate through the silicon substrate and the few sample preparation required have widely contributed to promote them as unavoidable tools of the defect localization workflow. Timing issue or unusual consumption can be detected by static and dynamic photon emission analysis and Electro Optical Probing. The identification of the emission spots is an essential step of the process. Due to scaling, more and more emission nodes are located within the acquisition area so that large variations of emission intensity can exist and it is difficult to recover a signal with a good signal to Noise Ratio (SNR). In this paper, automated techniques are reported to locate spots in these complex areas and to recover electrical waveforms with a good SNR. We will underline the challenge and define applications boundaries of these techniques. These techniques gives some perspectives for probing applications and allows FA community to use signal processing methods instead of expensive devices.

关键词： Laser beams Photonics wavelet transforms Very large scale integration image segmentation Failure analysis Integrated optics

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Discrete wavelet Transform: A signal processing Approach 1

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2015年

作者： D. Sundararajan

Provides easy learning and understanding of DWT from a signal processing point of view Presents DWT from a digital signal processing point of view, in contrast to the usual mathematical approach, making it highly acc... 详细信息

ISBN: (数字)9781119046073;9781119046080

ISBN: (纸本)9781119046066

Provides easy learning and understanding of DWT from a signal processing point of view

Presents DWT from a digital signal processing point of view, in contrast to the usual mathematical approach, making it highly accessible
Offers a comprehensive coverage of related topics, including convolution and correlation, Fourier transform, FIR filter, orthogonal and biorthogonal filters
Organized systematically, starting from the fundamentals of signal processing to the more advanced topics of DWT and Discrete wavelet Packet Transform.
Written in a clear and concise manner with abundant examples, figures and detailed explanations
Features a companion website that has several MATLAB programs for the implementation of the DWT with commonly used filters

“This well-written textbook is an introduction to the theory of discrete wavelet transform (DWT) and its applications in digital signal and image processing.” -- Prof. Dr. Manfred Tasche - Institut für Mathematik, Uni Rostock Full review at https://***/?q=an:06492561

关键词： Geometric tomography. signal processing. wavelets (Mathematics)

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Shearlet transform based image denoising using histogram thresholding

Shearlet transform based image denoising using histogram thr...

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International Conference on Communication Systems and Networks (ComNet)

作者： T S Anju N R Nelwin Raj Department of ECE Sree Chitra Thirunal College of Engineering Trivandrum Kerala

ISBN: (纸本)9781509033508

This paper presents an efficient image denoising method by incorporating shearlet-based histogram thresholding. Nowadays, digital images are used in wide range of applications but most of these images are degraded during transmission and acquisition process. Removal of noise from images is still a challenging task for many researchers because there is always a trade-off between noise removal and fine edge preservation. This paper is based on image denoising using shearlet transform. Shearlets have excellent features for data analysis and processing, which overcomes the limitation of traditional methods. They are optimally sparse and have multi-scale and multi-directional properties which are optimal in representing image containing edges. In this paper, the proposed method is found to produce superior peak signal-to-noise ratio (PSNR) over the conventional denoising algorithms.

关键词： Histograms image denoising Noise reduction Algorithm design and analysis image edge detection wavelet transforms

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Three Dimensional Data-Driven Multi Scale Atomic Representation of Optical Coherence Tomography

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2015年第5期34卷 1042-1062页

作者： Kafieh, Raheleh Rabbani, Hossein Selesnick, Ivan Isfahan Univ Med Sci Dept Biomed Engn Med Image & Signal Proc Res Ctr Esfahan Iran NYU Polytech Inst Dept Elect & Comp Engn Brooklyn NY 11201 USA

In this paper, we discuss about applications of different methods for decomposing a signal over elementary waveforms chosen in a family called a dictionary (atomic representations) in optical coherence tomography (OCT). If the representation is learned from the data, a nonparametric dictionary is defined with three fundamental properties of being data-driven, applicability on 3D, and working in multi-scale, which make it appropriate for processing of OCT images. We discuss about application of such representations including complex wavelet based K-SVD, and diffusion wavelets on OCT data. We introduce complex wavelet based K-SVD to take advantage of adaptability in dictionary learning methods to improve the performance of simple dual tree complex wavelets in speckle reduction of OCT datasets in 2D and 3D. The algorithm is evaluated on 144 randomly selected slices from twelve 3D OCTs taken by Topcon 3D OCT-1000 and Cirrus Zeiss Meditec. Improvement of contrast to noise ratio (CNR) (from 0.9 to 11.91 and from 3.09 to 88.9, respectively) is achieved. Furthermore, two approaches are proposed for image segmentation using diffusion. The first method is designing a competition between extended basis functions at each level and the second approach is defining a new distance for each level and clustering based on such distances. A combined algorithm, based on these two methods is then proposed for segmentation of retinal OCTs, which is able to localize 12 boundaries with unsigned border positioning error of 9.22 +/- 3.05 mu m, on a test set of 20 slices selected from 13 3D OCTs.

关键词： Denoising dictionary learning diffusion wavelet optical coherence tomography three-dimensional (3D) complex wavelet transform segmentation

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