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NASA Technical Reports Server (Ntrs) 19910014474: image wavelet Decomposition and applications

2017年

[Auto Generated] 1 Introduction. 3 2 The multiresolution concept. 2.1 Motivation ..................................... 2.2 The theory of wavelets ............................... 3 The multiorientation concept. 4 The decomposition process. 3 5 3 4 4 5 4.1 Monodimensional dyadic wavelet decomposition ................. 5 4.2 Bidimensional dyadic wavelet decomposition ................... 10 4.3 Energy-zero-crossings representation ....................... 13 Completeness of the representations. 17 5.

关键词： (ntrs) 19910014474: applications bajcsy, r. bandpass filters computer vision decomposition edge detection hierarchies image image processing image reconstruction image resolution imaging techniques low pass filters mallat, s. nasa technical reports server (ntrs) pattern recognition pixels reports signal processing treil, n.

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Infrared and Visible image Fusion Using NSST and Phase Stretch Transform 2

Infrared and Visible Image Fusion Using NSST and Phase Stret...

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2nd IEEE International Conference on Wireless Communications, signal processing and Networking (WiSPNET)

作者： Vishwakarma, Amit Bhuyan, M. K. Indian Inst Technol Dept Elect & Elect Engn Gauhati 781039 India

ISBN: (纸本)9781509044429

Due to wide image processing applications, image fusion techniques are the field of extensive research. The visible and infrared images obtained from two different sensors. The image fusion technique can be applied to fuse these source images to get a more informative fused image. The proposed method uses nonsubsampled shearlet transform (NSST) for the decomposition of source images into low-and high-frequency bands. The low-frequency bands are fused using weighted saliency-based fusion rule, and the high-frequency bands are fused using phase stretch transform based fusion rule. Then, inverse NSST is applied to obtain the fused image. The objective and subjective comparison show that the proposed method provides the better results compared with the existing methods.

关键词： image edge detection image fusion Discrete wavelet transforms Measurement Conferences Fuses

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强噪声背景信号的Perona-Malik扩散滤波算法

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机械科学与技术 2018年第8期37卷 1190-1194页

作者：毋文峰陈小虎中国人民武装警察部队警官学院成都610213 中国人民解放军火箭军工程大学西安710025

为了提取强噪声背景下机械振动信号的微弱故障特征,提出利用Perona-Malik非线性各向异性扩散滤波模型来实现强噪声背景信号降噪的方法。首先阐述了偏微分方程和Perona-Malik扩散滤波模型在图像降噪中的应用;其次分析了小波变换等传统信... 详细信息

为了提取强噪声背景下机械振动信号的微弱故障特征,提出利用Perona-Malik非线性各向异性扩散滤波模型来实现强噪声背景信号降噪的方法。首先阐述了偏微分方程和Perona-Malik扩散滤波模型在图像降噪中的应用;其次分析了小波变换等传统信号降噪方法的不足;最后基于图像降噪和信号降噪原理的相似性,利用Perona-Malik扩散滤波模型来实现机械振动信号的降噪,将其用于轴承振动仿真信号和实测信号。实验表明,与小波阈值去噪算法等传统信号降噪方法相比,Perona-Malik扩散滤波模型更适用于强噪声背景信号降噪,同时兼顾了信号去噪和保留信号细节特征的双重要求。

关键词：小波变换偏微分方程 Perona-Malik模型信号处理信号降噪

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Multiscale Directional Transforms based on Cosine-Sine Modulated Filter Banks for Sparse Directional image Representation 9

Multiscale Directional Transforms based on Cosine-Sine Modul...

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9th Annual Summit and Conference of the Asia-Pacific-signal-and-Information-processing-Association (APSIPA ASC)

作者： Nomura, Yusuke Ogawa, Ryutaro Kyochi, Seisuke Suzuki, Taizo Univ Kitakyushu Fukuoka Japan Univ Tsukuba Ibaraki Japan

ISBN: (纸本)9781538615423

This paper proposes multiscale directional transforms (MDTs) based on cosine-sine modulated filter banks (CSMFBs). Sparse image representation by directional transforms is necessary for image analysis and processing tasks and has been extensively studied. Conventionally, cosine-sine modulated filter banks (CSMFBs) have been proposed as one of separable directional transforms (SepDTs). Their computational cost is much lower than non-SepDTs, and they can work better than other SepDTs, e.g., dual-tree complex wavelet transforms (DTCWTs) in image processing applications. One drawback of CSMFBs is a lack of multiscale directional selectivity, i.e., it cannot provide multiple scale directional atoms as in the DTCWT frame, and thus flexible image representation cannot be achieved. In this work, we show a design method of multiscale CSMFBs by extending modulated lapped transforms, which are a subclass of CSMFBs. We confirm its effectiveness in nonlinear approximation and image denoising as a practical application.

关键词： Transforms Two dimensional displays Prototypes image representation image denoising Delays Laplace equations

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Text language identification using signal processing techniques 3

Text language identification using signal processing techniq...

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3rd Iranian Conference on signal processing and Intelligent Systems, ICSPIS 2017

作者： Nezhadi, Mohammad. M. Alyan Forghani, Majid Hassanpour, Hamid Image Processing and Data Mining Lab Shahrood University of Technology Shahrood Iran Institute of Natural Sciences and Mathematics Ural Federal University Yekaterinburg Russia

ISBN: (纸本)9781538649725

Human is often able to recognize spoken languages even if the meaning could not be understood. Text language determination is an important requirement in any text processing system. In this paper, a novel text language identification based on signal processing techniques is presented. In each language, there is a dependency between components of a sentence as well as components those construct the words. Considering the text as a time series, this dependency can be observed using signal processing techniques. The proposed method recognizes the language of a text, in a three-stage manner, using wavelet packet and neural networks. First the preprocessing section that prepares the text for signal processing via adding some additional spaces between consecutive words, then represents the text using UTF8 coding system. In the second stage, the wavelet packet is applied on the coded text, i.e. time-series, and a feature vector is extracted from wavelet packet coefficients of sub-bands. Finally the classification section applies a neural network classifier on extracted feature vector. The proposed method has been tested on the database gathered from Wikipedia with seven different languages (Arabic, English, French, Germany, Italian, Persian and Russian). The proposed method earned the accuracy above 97%. The proposed method is enough fast that makes it suitable to use in real-time applications. © 2017 IEEE.

关键词： Neural networks

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Comparative Analysis of Hyperspectral Feature Extraction Methods in Vegetation Classification 25

Comparative Analysis of Hyperspectral Feature Extraction Met...

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25th signal processing and Communications applications Conference (SIU)

作者： Ocal, Mertalp Ergun, Kazim Akar, Gozde Bozdagi Orta Dogu Tekn Univ Elekt Elekt Muhendisligi Ankara Turkey

ISBN: (纸本)9781509064946

To perform an accurate vegetation classification in hyperspectral data, feature extraction process prior to classification is very important. Success rates of classifiers in vegetation are rather limited compared to classification of other types of materials. Therefore, it is required to perform an effective feature extraction before classification. Principle Component Analysis(PCA) is a common and easily applicable method for this purpose. However, PCA is not an optimal method for distinguishing between different plant species. In this study, the reasons for PCA not being an adequate method for this purpose arc discussed and alternative useful feature extraction methods in classification of plant species are examined. Tests were performed for Spectrally Segmented PCA(SSPCA), Discrete wavelet Transform(DWT) and Genetic Algorithm(GA) feature extraction methods, their effects on classifier performances were compared and it was observed that all of the mentioned alternatives had noticable improvements in classification performances.

关键词： hyperspectral imaging feature extraction remote sensing vegetation hyperspectral image classification principal component analysis

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Spline and Spline wavelet Methods with applications to signal and image processing: Volume I Periodic Splines

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2016年

作者： Amir Z Averbuch Pekka Neittaanmäki Valery A Zheludev

ISBN: (纸本)9789402405620

This volume provides universal methodologies accompanied by Matlab software to manipulate numerous signal and image processing applications. It is done with discreteand polynomial periodic splines. Various contributions of splines to signal and image processing from a unified perspective are presented. This presentation is based on Zak transform and on Spline Harmonic Analysis (SHA) methodology. SHA combines approximation capabilities of splines with the computational efficiency of the Fast Fourier transform. SHA reduces the designof different spline types such as splines, spline wavelets (SW), wavelet frames (SWF)and wavelet packets (SWP)and their manipulations by simple operations. Digital filters, produced by wavelets design process, give birth to subdivision schemes. Subdivision schemes enable to performfast explicit computation of splines' values at dyadic and triadic rational points. This is used for signals and imagesup sampling. In addition tothe design of a diverse library of splines, SW, SWP and SWF, this book describes their applications topractical problems. The applications include up sampling, image denoising, recovery from blurred images, hydro-acoustic target detection, to name a few. The SW Fare utilized for image restoration that was degraded by noise, blurring and loss of significant number of pixels. The bookis accompanied by Matlab based software that demonstrates and implements all the presented algorithms. The book combines extensive theoretical exposurewith detailed description of algorithms, applications and software. The Matlab software can be downloaded from http://***

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Overview of Artifacts Detection and Elimination Methods for BCI Using EEG

Overview of Artifacts Detection and Elimination Methods for ...

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International Conference on image, Vision and Computing (ICIVC)

作者： Clifford Maswanganyi Chunling Tu Pius Owolawi Shengzhi Du Department of Computer Systems Engineering Tshwane University of Technology Pretoria Gauteng RSA Department of Electrical Engineering Tshwane University of Technology Pretoria Gauteng RSA

ISBN: (纸本)9781538649923

Electroencephalogram (EEG) is the most commonly utilized method of mapping brain wave patterns from the scalp. It is a process of extracting electrical activities of the brain in the form of EEG signals using non-invasive methods. In EEG based techniques, a challenge exists in the form of signal contamination caused by internal and external factors in the recorded EEG signals., such as the artifacts, interferences among multiple EEG sources, and noise. signal pre-processing is the most significant phase for EEG based applications, for instance, the brain computer interface (BCI) systems. This is because the quality of data acquired from the brain through EEG measurements is dependent on the effectiveness of artifact detection and removal methods. During EEG recording there are several factors that influence the quality of data. One of the factors of signal contamination occurs as the result of physiological and non-physiological artifacts. To detect and remove such contaminations from EEG signals, there are several efficient artifact detection and elimination techniques based on certain pre-processing methods. The main objective of this study is to demonstrate various types of artifacts in EEG recordings as results of external and internal factors. Furthermore, the study will also present an extensive review of existing artifacts identification and elimination methods applied on EEG signals.

关键词： Electroencephalography Physiology Discrete wavelet transforms Electrodes Contamination Electric potential

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image Compression and Retrieval Using Prediction and wavelet - Based Techniques

Image Compression and Retrieval Using Prediction and Wavelet...

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作者： Ahamed Ayoobkhan, Mohamed Uvaze Multimedia University (Malaysia)

学位级别：Ph.D.

The topic of image compression and retrieval has become one of the most researched areas in the recent years due to the acute demand for storage and transmission of large volume of image data that are generated in the Internet and other applications. When compressing an image, it is necessary to satisfy two conflicting requirements, namely, compression ratio (CR) and the image quality which is usually measured by the parameter, peak signal-to- noise ratio (PSNR). In this thesis, several lossless and lossy image compression techniques as well as an integrated image retrieval system are proposed using prediction and wavelet based techniques. Employing prediction errors instead of the actual image pixels for compression and retrieval processes ensures data security. A lossless algorithm (LLA) is proposed which uses neural network predictors and entropy encoding. Classification is performed as a pre-processing step to improve the compression ratio. For this purpose, classification algorithm1(CL1) and classification algorithm2(CL2) which make use of wavelet based contourlet transform coefficients and Fourier descriptors as features are proposed. Two identical artificial neural networks (ANNs) are employed at the compression (sending) and decompression (receiving) sides to carry out the prediction. The prediction error which is the difference between the original and the predicted pixel values is used instead of the actual image pixels. The prediction is performed in a lossless manner by rounding-off the predicted values to the nearest integer values at both sides.

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Improving compressive sensing in MRI with separate magnitude and phase priors

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MULTIDIMENSIONAL SYSTEMS AND signal processing 2017年第4期28卷 1109-1131页

作者： Zibetti, Marcelo V. W. De Pierro, Alvaro R. Fed Univ Technol Parana UTFPR DMAEC CPGEI Curitiba Parana Brazil CNPq Campinas SP Brazil

Compressive sampling/compressed sensing (CS) has shown that it is possible to perfectly reconstruct non-bandlimited signals sampled well below the Nyquist rate. Magnetic Resonance Imaging (MRI) is one of the applications that has benefited from this theory. Sparsifying operators that are effective for real-valued images, such as finite difference and wavelet transform, also work well for complex-valued MRI when phase variations are small. As phase variations increase, even if the phase is smooth, the sparsifying ability of these operators for complex-valued images is reduced. If the phase is known, it is possible to remove it from the complex-valued image before applying the sparsifying operator. Another alternative is to use the sparsifying operator on the magnitude of the image, and use a different operator for the phase, i.e., one related to a smoothness enforcing prior. The proposed method separates the priors for the magnitude and for the phase, in order to improve the applicability of CS to MRI. An improved version of previous approaches, by ourselves and other authors, is proposed to reduce computational cost and enhance the quality of the reconstructed complex-valued MR images with smooth phase. The proposed method utilizes penalty for the transformed magnitude, and a modified penalty for phase, together with a non-linear conjugated gradient optimization. Also, this paper provides an extensive set of experiments to understand the behavior of previous methods and the new approach.

关键词： Compressed sensing image reconstruction Magnetic resonance imaging Regularization image prior

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