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Classification of artifactual EEG signal and detection of multiple eye movement artifact zones using novel Time-amplitude algorithm

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signal image AND VIDEO processing 2017年第2期11卷 333-340页

作者： Tibdewal, Manish N. Fate, Rohan R. Mahadevappa, M. Ray, Ajoy Kumar Malokar, Monika Indian Inst Technol Sch Med Sci & Technol Kharagpur 721302 W Bengal India Shri Sant Gajanan Maharaj Coll Engn Dept Elect & Telecommun Engn Shegaon 444203 Maharashtra India Indian Inst Technol Dept Elect & Elect Commun Engn Kharagpur 721302 W Bengal India Malokar Hosp Akola 444001 Maharashtra India

Electroencephalogram (EEG) signal has numerous applications in the field of medical science. It is used to diagnose many of the abnormalities, disorders, and diseases related to the human brain. The EEG signal contaminated with ocular artifacts makes it very difficult for analysis and diagnosis. This paper includes work on classification of artifactual/non-artifactual EEG time series and perfect detection of eye movement (EM) artifact contaminated EEG signal along with multiple EM artifactual zones in the same time series. Artificial Neural Network classifier in a simple perceptron model without hidden layer is used for the identification. This study presents a newly developed, simple, robust, and computationally fast statistical Time-Amplitude algorithm. By the application of novel Time-Amplitude algorithm on identified signal, the EM artifactual EEG signal along with multiple zones is automatically detected and marked accurately. Such robust, efficient, real-time and simple algorithm is not ever designed and used for ocular artifact detection by any author. The ROC analysis gives accuracy of the ANN model for classifying the presence of artifacts in the EEG data, which is 97.50 %. The time elapsed for executing the Time-Amplitude algorithm for automatic detection of EM artifact is very less (4.30 msec.) compared to DWT with Haar. It has the capability to detect multiple EM artifactual zones, in the same time, for the montage of 8-second EEG.

关键词： Electroencephalogram Eye Movement (EM) Artifacts ANN Time-Amplitude (TA) Algorithm Discrete wavelet Transform (DWT) ROC parameters

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Digital watermarking scheme for image authentication 6

Digital watermarking scheme for image authentication

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6th IEEE International Conference on Communication and signal processing, ICCSP 2017

作者： Priya, C. V. Lakshmi Raj, N. R. Nelwin SreeChitraThirunal College of Engineering Pappanamcode Thiruvananthapuram India

ISBN: (纸本)9781509038008

Digital image watermarking process is definite as to insert information of digital into digital signal. This is an efficient solution to avoid illegal copying of information from multimedia networks. Many watermarking algorithms have been developed and each of them has its own individuality because of its variety of applications. A new algorithm able to solve most of the practical issues of watermarking is designed. Watermarking alone is not sufficient to prevent the unauthorized manipulations of data unless a proper protection protocol is established. An efficient watermarking algorithm should satisfy an optimal trade-off between three factors such the capacity, robustness and imperceptibility. The proposed algorithm uses the advantage of singular value decomposition (SVD), discrete wavelet transform (DWT) and homomorphic filtering. Finally, the efficiency of the result is evaluated using various quality measurements and the watermarked image is encrypted to increase the security level. © 2017 IEEE.

关键词： Singular value decomposition

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Depth image super-resolution: A review and wavelet perspective

Depth image super-resolution: A review and wavelet perspecti...

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International Conference on Computer Vision and image processing, CVIP 2016

作者： Balure, Chandra Shaker Ramesh Kini, M. Surathkal Mangalore India

ISBN: (纸本)9789811021060

We propose an algorithm which utilizes the Discrete wavelet Transform (DWT) to super-resolve the low-resolution (LR) depth image to a high-resolution (HR) depth image. Commercially available depth cameras capture depth images at a very low-resolution as compared to that of the optical cameras. Having an highresolution depth camera is expensive because of the manufacturing cost of the depth sensor element. In many applications like robot navigation, human-machine interaction (HMI), surveillance, 3D viewing, etc. where depth images are used, the LR images from the depth cameras will restrict these applications, thus there is a need of a method to produce HR depth images from the available LR depth images. This paper addresses this issue using DWT method. This paper also contributes to the compilation of the existing methods for depth image super-resolution with their advantages and disadvantages, along with a proposed method to super-resolve depth image using DWT. Haar basis for DWT has been used as it has an intrinsic relationship with super-resolution (SR) for retaining the edges. The proposed method has been tested on Middlebury and Tsukuba dataset and compared with the conventional interpolation methods using peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) performance metrics. © Springer Science+Business Media Singapore 2017.

关键词： Discrete wavelet transforms

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2017 25th signal processing and Communications applications Conference, SIU 2017

2017 25th Signal Processing and Communications Applications ...

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25th signal processing and Communications applications Conference, SIU 2017

ISBN: (纸本)9781509064946

The proceedings contain 601 papers. The topics discussed include: a comparison among interference approximation methods for OFDM/OQAM;classification of band-specific regional hemispheric connectivity in obsessive compulsive disorder;image enhancement with outlier detection;music genre classification with word and document vectors;using of S-UTD-CH model in coverage mapping and comparison with FEKO software;speaker recognition anti-spoofing using linear prediction residual;location based pricing with Bluetooth low energy in public transportation;optimum phase noise and acquisition time analysis for OFDM receiver structure with QPSK signaling;producing the location information with the kalman filter on the GPS data for autonomous vehicles;and using wavelet transform for cardiotocography signals classification.

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Double-Density and Dual-Tree Based Methods for image Super Resolution 7

Double-Density and Dual-Tree Based Methods for Image Super R...

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Conference on Geospatial Informatics, Fusion, and Motion Video Analytics VII

作者： Giansiracusa, Mike Blasch, Erik Singerman, Paul Ezekiel, Soundararajan Indiana Univ Penn Comp Sci 210 S 10th St Indiana PA 15701 USA US Air Force Res Lab Name 26 Elect Pkwy Rome NY 13441 USA

ISBN: (数字)9781510609006

ISBN: (纸本)9781510608993;9781510609006

When several low-resolution images are taken of the same scene, they often contain aliasing and differing sub-pixel shifts causing different focuses of the scene. Super-resolution imaging is a technique that can be used to construct high-resolution imagery from these low-resolution images. By combining images, high frequency components are amplified while removing blurring and artifacting. Super-resolution reconstruction techniques include methods such as the Non-Uniform Interpolation Approach, which is low resource and allows for real-time applications, or the Frequency Domain Approach. These methods make use of aliasing in low-resolution images as well as the shifting property of the Fourier transform. Problems arise with both approaches, such as limited types of blurred images that can be used or creating non-optimal reconstructions. Many methods of super-resolution imaging use the Fourier transformation or wavelets but the field is still evolving for other wavelet techniques such as the Dual-Tree Discrete wavelet Transform (DTDWT) or the Double-Density Discrete wavelet Transform (DDDWT). In this paper, we propose a super-resolution method using these wavelet transformations for use in generating higher resolution imagery. We evaluate the performance and validity of our algorithm using several metrics, including Spearman Rank Order Correlation Coefficient (SROCC), Pearson's Linear Correlation Coefficient (PLCC), Structural Similarity Index Metric (SSIM), Root Mean Square Error (RMSE), and Peak-signal-Noise Ratio (PSNR). Initial results are promising, indicating that extensions of the wavelet transformations produce a more robust high resolution image when compared to traditional methods.

关键词： image resolution image processing image converters Rendering High definition

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Analysis of fiber optic gyroscope vibration error based on improved local mean decomposition and kernel principal component analysis

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APPLIED OPTICS 2017年第8期56卷 2265-2272页

作者： Song, Rui Chen, Xiyuan Southeast Univ Sch Instrument Sci & Engn Nanjing 210096 Jiangsu Peoples R China Southeast Univ Minist Educ Key Lab Microinertial Instrument & Adv Nav Techno Nanjing 210096 Jiangsu Peoples R China

The fiber optic gyroscope (FOG), one version of an all solid-state rotation sensor, has been widely used in navigation and position applications. However, the elastic-optic effect of fiber will introduce a non-negligible error in the output of FOG in a vibration and shock environment. To overcome the limitations of mechanism structure improvement methods and the traditional nonlinear analysis approaches, a hybrid algorithm of an optimized local mean decomposition- kernel principal component analysis (OLMD-KPCA) method is proposed in this paper. The vibration signal features of higher frequency components are analyzed by OLMD and their energy is calculated to take shape as the input vector of KPCA. In addition, the output data of three axis gyroscopes in an inertial measurement unit (IMU) under vibration experiment are used to validate the effectiveness and generalization ability of the proposed approach. When compared to the wavelet transform(WT), experimental results demonstrate that the OLMD-KPCA method greatly reduces the vibration noise in the FOGoutput. Besides, the Allan variance analysis results indicate the error coefficients could be decreased by one order of magnitude and the algorithm stability of OLMD-KPCA is proven by another two sets of data under different vibration conditions. (C) 2017 Optical Society of America

关键词： Fiber optic gyroscopes image processing Light intensity Phase shift Power spectral density wavelet transforms

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Matrix Quadrature Filterbanks for image processing 60

Matrix Quadrature Filterbanks for Image Processing

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60th IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Lian, Jian-ao Wang, Yonghui Prairie View A&M Univ Dept Math Prairie View TX 77446 USA Prairie View A&M Univ Dept Engn Technol Prairie View TX 77446 USA

ISBN: (纸本)9781509063895

An innovative approach is presented to establish bidimensional (2D) linear-phase matrix quadrature filterbanks (MQF) directly from their matrix identities. It is well-known that two-channel quadrature filterbanks (QMF) have been successfully applied to signal and image processing. Bidimensional QMF filterbanks directly deduced from tensor-products of 1D QMF filterbanks is easy, straightforward, and convenient. They possess both the finite impulse response (FIR) and the perfect reconstruction (PR) properties, which are necessary for filter-banks' alias-free as well as magnitude and phase distortion-free characteristics. However, some additional desirable features are missing. These features include directional features, magnitude preservation, energy preservation, and energy compaction. Direct consideration from matrices yields more freedoms for the design of the new ingenuous MQF. A new MQF is designed and is applied to image compression. The rate-distortion performance shows that the proposed matrix wavelet has promising potential applications in image processing.

关键词： image compression

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Vipimage 2017: Proceedings of the VI ECCOMAS Thematic Conference on Computational Vision and Medical image processing Porto, Portugal, October 18-20

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2017年

作者： Joáo Manuel Rs S Tavares Renato M Natal Jorge

ISBN: (纸本)9783319681948

This book gathers papers presented at the Vipimage 2017-VI ECCOMAS Thematic Conference on Computational Vision and Medical image processing. It highlights invited lecturers and full papers presented at the conference, which was held in Porto, Portugal, on October 1820, 2017. These international contributions provide comprehensive coverage on the state-of-the-art in the following fields: 3D Vision, Computational Bio-Imaging and Visualization, Computational Vision, Computer Aided Diagnosis, Surgery, Therapy and Treatment, Data Interpolation, Registration, Acquisition and Compression, Industrial Inspection, image Enhancement, image processing and Analysis, image Segmentation, Medical Imaging, Medical Rehabilitation, Physics of Medical Imaging, Shape Reconstruction, signal processing, Simulation and Modelling, Software Development for image processing and Analysis, Telemedicine Systems and their applications, Tracking and Analysis of Movement, and Deformation and Virtual Reality. In addition, it explores a broad range of related techniques, methods and applications, including: trainable filters, bilateral filtering, statistical, geometrical and physical modelling, fuzzy morphology, region growing, grabcut, variational methods, snakes, the level set method, finite element method, wavelet transform, multi-objective optimization, scale invariant feature transform, Laws texture-energy measures, expectation maximization, the Markov random fields bootstrap, feature extraction and classification, support vector machines, random forests, decision trees, deep learning, and stereo vision. Given its breadth of coverage, the book offers a valuable resource for academics, researchers and professionals in Biomechanics, Biomedical Engineering, Computational Vision (image processing and analysis), Computer Sciences, Computational Mechanics, signal processing, Medicine and Rehabilitation.

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基于紧框架的二阶总广义变分图像修复模型

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计算机工程与应用 2018年第11期54卷 178-184页

作者：董卫东彭宏京南京工业大学计算机科学与技术学院南京211816

针对传统总广义变分(TGV)小波修复模型采用单一小波基变换,仅对纹理细节信息较少且结构简单的图像有很好修复能力的缺点,提出一种紧框架域下的总广义变分正则化修复模型。不同于经典小波变换,紧框架系统具有冗余、时移不变和线性相位等... 详细信息

针对传统总广义变分(TGV)小波修复模型采用单一小波基变换,仅对纹理细节信息较少且结构简单的图像有很好修复能力的缺点,提出一种紧框架域下的总广义变分正则化修复模型。不同于经典小波变换,紧框架系统具有冗余、时移不变和线性相位等图像处理过程中较为重要的特性。新模型通过引入多层紧框架分解系数的低阶与高阶导数项建立正则化项,获取图像不同尺度多方向上的特征信息来对图像进行约束。模型的数值实现采用分裂技术与原始-对偶方法相结合的优化算法(PDSBA),交替迭代求解两个易于处理的子问题,提高了图像修复过程的处理效率。相比于传统模型,所提模型不仅具有保边性能,而且对含有较多细节或纹理信息的图像也有较好的修复效果。实验结果显示,三个修复性能指标:峰值信噪比(PSNR)、平均绝对误差(MAE)和结构相似测度(SSIM)均获得显著提升。

关键词：总广义变分小波修复紧框架系统多层紧框架分解低阶与高阶导数项分裂技术原始-对偶算法

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3D+T Motion Analysis with Nanosensors 40

3D+T Motion Analysis with Nanosensors

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Conference on applications of Digital image processing XL

作者： Leduc, Jean-Pierre Reliance Core Consulting Alexandria VA 22314 USA

ISBN: (数字)9781510612501

ISBN: (纸本)9781510612501;9781510612495

This paper addresses the problem of motion analysis performed in a signal sampled on an irregular grid spread in 3-dimensional space and time (3D+T). Nanosensors can be randomly scattered in the field to form a sensor network. Once released, each nanosensor transmits at its own fixed pace information which corresponds to some physical variable measured in the field. Each nanosensor is supposed to have a limited lifetime given by a Poisson-exponential distribution after release. The motion analysis is supported by a model based on a Lie group called the Galilei group that refers to the actual mechanics that takes place on some given geometry. The Galilei group has representations in the Hilbert space of the captured signals. Those representations have the properties to be unitary, irreducible and square-integrable and to enable the existence of admissible continuous wavelets fit for motion analysis. The motion analysis can be considered as a so-called inverse problem where the physical model is inferred to estimate the kinematical parameters of interest. The estimation of the kinematical parameters is performed by a gradient algorithm. The gradient algorithm extends in the trajectory determination. Trajectory computation is related to a Lagrangian-Hamiltonian formulation and fits into a neuro-dynamic programming approach that can be implemented in the form of a Q-learning algorithm. applications relevant for this problem can be found in medical imaging, Earth science, military, and neurophysiology.

关键词： Motion Analysis Kinematical Parameters Trajectories Nanosensors Group Representations wavelet Transform Galilean wavelets Inverse Problem Q-Learning

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