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International Conference on image and signal processing and their applications (ISPA)

作者： Redouane KAIBOU Mohamed Salah AZZAZ École Militaire Polytechnique Laboratoire Systèmes Électroniques et Numériques Alger Algérie

ISBN: (纸本)9781728131573

This paper presents a research study leading to a robust chaos-based scheme for digital image watermarking applications in the transform domain. The steps undergone include combining, in a repeating process, three performant conventional mathematical transforms of Direct Cosine Transform (DCT), Discrete wavelet Transform (DWT) and variants with three different state-of-the-art advanced techniques namely Discrete Curvelet Transform (DCuT), Discrete Radon Transform (DRaT) and Finite Ridgelet Transform (FRT). Results have shown a neat improvements in watermarking imperceptibility and robustness each time especially after including Singular Value Decomposition (SVD) which has enhanced embedding capability as well. Methods evaluation has followed an exhaustive series of performed tests that applied a range of evaluation metrics for imperceptibility. In addition, Robustness of the final scheme has also been confirmed by applying a variety of attacks on the resulting images. Finally, security aspect has also been improved after a comparative implementations of popular four chaotic models namely Arnold Cat Map (ACM), Lorenz Cat Map (LCM), Rossler Cat Map (RCM) and Genesio-Tesi Cat Map (GTCM) respectively producing secure images encryption levels. All Implementations have been validated using a sufficent number of color image from a large dataset.

关键词： image watermarking Transform domain Advanced techniques Chaotic encryption digital image watermarking advanced technologies chaotic encryption Transform Cats MAPPING(MATHEMATICAL) maps color image Singular value decomposition

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Auto Center Find Density Peaks Algorithm and its Application in Face image Clustering

Auto Center Find Density Peaks Algorithm and its Application...

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signal, Information and Data processing (ICSIDP), IEEE International Conference on

作者： GuiYan Wang ChangJiang Bu YueSheng Luo Harbin Finance University Harbin China College of Automation Harbin Engineering University Harbin China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

Face image clustering is one of the most important applications in digital signal process. Here, Find Density Peaks clustering algorithm with auto choosing centers is proposed and applied into the face image clustering problem. In the clustering process, the Complex wavelet structural Similarity Index is introduced to quantize the difference between two face images. The deciding mechanism of two key parameters are modified. Avoiding the false centers, the true centers of each cluster are auto defined. And the face images dataset are properly clustered. The face images are dimensionality reduced and visualized via T-SNE as scatter points in 2D space. The results show a good partition of face images dataset. And the proposed algorithm shows practical values in most applications required with clustering.

关键词： Clustering FDP CW-SSIM T-SNE face images

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image Fusion using Multi Resolution transforms with Human Visual System

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Journal of Physics: Conference Series 2021年第1期1921卷

作者： Radhika Vadhi Kilari Veera Swamy Department of ECE Srinivasa Institute of Engineering and Technology Cheyyeru Amalapuram A.P India Department of ECE Vasavi College of Engineering Ibrahimbagh Hyderabad Telangana India

The Quality of vision is a practical objective in the image *** of the applications of image processing procedures is image Fusion. To get the subjective vision of a image by gathering the best data from source images of a similar scene/picture and spot them in a solitary void image is the image Fusion process. A simple and versatile approaches are statistical measures, that are applicable in any kind of image/signal processing techniques. In the first step, image is decomposed using Discrete wavelet Transform (DWT). Mathematical computation of Smoothness is proposed in transform domain. This metric is used to select important information from multiple source images resulting to fused image. Further, Human Visual System (HVS) is also explored for fusion. Here all sub bands of DWT are multiplied with HVS weights. Highest response sub band is identified from various sub bands of multiple images using HVS. These sub bands are selected to get the fused image. Smoothness based fusion technique identifies the good texture information and leave the noise affected portions from the multiple source images. HVS based fusion technique identifies the visually important information from the multiple source images. The registered multi-focus and medical images are considered as source images. The experimental results shows that proposed fusion techniques are good in terms of popular fusion metrics.

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Classification of human heart signals by novel feature extraction techniques for rescue application

Classification of human heart signals by novel feature extra...

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International Conference on image Information processing

作者： Rajesh Saha Ratnesh Kumar Singh Rajeev Kumar Ghanapriya Singh Tushar Goel Pankaj Kumar Pal NIT Uttarakhand Srinagar Garhwal India

In the paper, the methodology of detecting the heart rate signal wirelessly with regards to use it as an applications as an emerging tool for health care in emergency and surveillance application is presented. A novel feature extraction technique is presented to detect human heart signal or electrocardiogram (ECG) signals wirelessly and classify the signal as normal or abnormal. By the analysis of the heart rate signal the presence of an alive human person below the debris can be detected. After detection of heart rate signals via sensors, the challenge lies in classifying the human heart rate signals as normal or abnormal. Heart signals or Electrocardiogram (ECG) is the primary signal used to diagnose and detection of heart disease abnormality. In this work, ECG signals are de-noised and then analyzed to extract feature from it by a novel technique which is combining Discrete wavelet Transform (DWT) and nonlinear, linear statistical feature. To verify the efficiency of extracted feature, here used various Machine Learning (ML) methods namely as Decision Trees (DT), Discriminant Analysis (DA), Logistic Regression (LR), Nearest Neighbor (KNN), Support Vector Machine (SVM) and Ensemble have been used as a classifiers and their results are compared. KNN reports the lowest accuracy of 81.60% on MIT- BIH Arrythmia Database. SVM and Ensemble reports the highest accuracy of 97.40%.

关键词： Electrocardiography Feature extraction Heart Discrete wavelet transforms Databases Sensors

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Underwater image Enhancement by Rayleigh Stretching in Time and Frequency Domain

Underwater Image Enhancement by Rayleigh Stretching in Time ...

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International Congress on image and signal processing, BioMedical Engineering and Informatics

作者： Yu-Ting Peng Chi-Weng Chang Mong-Shu Lee National Taiwan Ocean University Kee-Lung Taiwan

image restoration and enhancement play an important role in underwater image applications. How to solve the negative gloomy underwater effects resulting from scattering and color distortion has become the key issues. In this study, we proposed a Rayleigh stretching-based image enhancement method. The method includes a few steps which are background color correction, Rayleigh stretching in time domain, discrete wavelet transform with Contrast Limited Adaptive Histogram Equalization (CLAHE), contrast adjustment, and sharpening. Experimental results through both the qualitative and quantitative analyses demonstrate that our approach greatly improves visibility and contrast and obtains best values with respect to entropy and underwater image quality measure (UIQM), respectively.

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Systolic Murmurs Diagnosis Improvement by Feature Fusion and Decision Fusion

Systolic Murmurs Diagnosis Improvement by Feature Fusion and...

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IEEE International Conference on signal and image processing applications (ICSIPA)

作者： Somayeh Akbari Hassan Ghassemian Zahra Akbari Image processing and Information Analysis Lab. Faculty of Electrical and Computer Eng. Tarbiat Modares University Tehran Iran

Acoustic sound generated by the heart mechanical activity, can provide useful information about the condition of heart valves. The heart sound auscultation is the fundamental tool in the evaluation of the cardiovascular system. The advantage of this method is fast, inexpensive and noninvasive. Due to human auscultatory limitation and non-stationary characteristics of phonocardiogram signals (PCG), diagnosis based on sounds that are heard via a stethoscope is difficult skill, therefor it requires a lot of practice. This study has proposed a biomedical automatic system for classification of PCG signals, which, recorded by a digital stethoscope. In order to extract various characteristics of PCG signals, the power spectrum estimation, wavelet transform (WT) and Mel frequency Cepstrum coefficients (MFCC) have been used in feature extraction step. Features are given to four classifiers: support vector machine (SVM), k-nearest neighbor (k-NN), multilayer perceptron (MLP) and maximum likelihood (ML). The majority voting combination rule is utilized for fusion of different classifiers. The proposed method has been examined on dataset of 90 PCG records containing healthy and three types of cardiac valve diseases (pulmonary stenosis (PS), Atrial Septal Defect (ASD) and Ventricular Septal Defect (VSD)). The experimental results demonstrate that the classifier fusion rule significantly increases the diagnostic accuracy of abnormal PCG. Our proposed method can be used for online classification of PCG in intelligent diagnosis systems.

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wavelet Analysis of Multi-mode Behavior of an Etched Fiber Bragg Grating Sensor Operating in Sucrose Solutions 8

Wavelet Analysis of Multi-mode Behavior of an Etched Fiber B...

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Conference on Advanced Sensor Systems and applications viii

作者： Molardi, C. Ayupova, T. Sypabekova, M. Bekmurzayeva, A. Shaimerdenova, M. Tosi, D. Nazarbayev Univ Sch Engn Astana 010000 Kazakhstan Natl Lab Astana Lab Biosensors & Bioinstruments Astana 010000 Kazakhstan

ISBN: (纸本)9781510622418

Fiber optic sensors represent an attractive alternative in chemical, bio-chemical and medical applications. Their success can be retrieved in their peculiar properties such as: electromagnetic interference immunity, fast response, high sensitivity, and small size. In this context, Fiber Bragg Gratings (FBGs) play a key role in applications like measurements of temperature and strain. The mechanism of FBGs is related to the dependence between the characteristic wavelength reflected by the FBG and the effective index of the modes propagating inside the fibers. This property can be exploited to engineer a new and inexpensive class of FBG devices for measuring refractive index of solutions. By reducing the cladding thickness of the single mode fiber, where the FBG is inscribed, the structure becomes three layers and the modes guidance properties become more and more dependent on the external environment, including the refractive index of the solution to measure. In this work, an FBG has been etched by a solution of HF acid and immersed in different solutions of water and sucrose. Results show a strong multi-modal behavior induced by the guidance properties of the three-layer system. The reflected spectra, characterized by a large band whose width, are strictly dependent on the sucrose concentration in solution. As the sucrose increases, the refractive index of the solution increases. The bandwidth reduces, showing a wavelength shift toward longer wavelength. While the wavelength shift is not so relevant the variation of the bandwidth is significant, suggesting an effective interrogation method based on wavelet signal processing.

关键词： Evanescent field sensors Fiber Bragg Grating (FBG) Optical Fiber Sensors (OFS) Refractive index sensors wavelet density estimation

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A new objective metric to predict image quality using deep neural networks 41

A new objective metric to predict image quality using deep n...

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Conference on applications of Digital image processing XLI

作者： Akyazi, Pinar Ebrahimi, Touradj Ecole Polytech Fed Lausanne Multimedia Signal Proc Grp MMSPG CH-1015 Lausanne Switzerland

ISBN: (数字)9781510620766

ISBN: (纸本)9781510620766

Quality assessment of images is of key importance for mulmedia applications. In this paper we present a new full reference objective metric to predict the quality of images using deep neural networks. The network makes use of both the color as well as frequency information extracted from reference and distorted images. Our method comprises of extracting a number of equal sized random patches from the reference image and the corresponding patches from the distorted image, then feeding the patches themselves as well as their 3-scale wavelet transform coe ffi cients as input to a neural network. The architecture of the network consists of four branches, with the fi rst three generating frequency features and the fourth extracting color features. Feature extraction is carried out using 12 to 15 convolutional layers and one pooling layer, while two fully connected layers are used for regression. The overall image quality is computed as a weighted sum of patch scores, where local weights are also learned by the network using two additional fully connected layers. The network was trained using TID2013 and tested on TID2013, CSIQ and LIVE image databases. Our results show high correlations with subjective test scores, are generalizable for certain types of distortions and are competitive with respect to the state-of-the-art methods.

关键词： Objective image quality assessment full reference image quality metric deep convolutional neural networks discrete wavelet transform

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Cyclic Coding and Discrete wavelet Transform Based Audio signal processing

Cyclic Coding and Discrete Wavelet Transform Based Audio Sig...

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International Conference on Artificial Intelligence and Data processing (IDAP)

作者： Yildiz, Dogan Karagol, Serap Ondokuz Mayis Univ Elekt Elekt Muhendisligi Bolumu Samsun Turkey

ISBN: (纸本)9781538668788

Forward Error Correction (FEC) is a digital signal processing technique used to increase the reliability of data transmitted over noisy communication channels and to provide error control. Error checking is performed using residual data, which is called error correction code before data transmission. Cyclic coding is one of the FEC techniques used to solve messages properly in modern wireless communication systems. wavelet Transform is also used extensively in audio, image and video processing applications. In the first part of this study, the Cyclic Coding Method was used to transmit the audio data recorded in. wav format for 5 seconds in MATLAB environment with the least possible error. Binary Phase Shift Keying (BPSK) modulation is used as the modulation method. In the generated communication system, Additive White Gaussian Noise (AWGN) channel is coded with Cyclic (7,4), Cyclic (15,7), Cyclic (17,8) and Cyclic (31,21) codes respectively. The Mean Square Error (MSE) performances of these codes were then analyzed. In the second part of the study, it was researched which wavelet family is suitable for audio transmission with minimum error. Four wavelet transform families of different levels are applied to the audio signal for Cyclic (7,4), Cyclic (15,7), Cyclic (17,8) coded channels and their performances are compared.

关键词： Additive White Gaussian Noise Channel Coding Cyclic Codes Forward Error Correcting wavelet Transform

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A secure blind discrete wavelet transform based watermarking scheme using two-level singular value decomposition

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EXPERT SYSTEMS WITH applications 2018年 112卷 208-228页

作者： Araghi, Tanya Koohpayeh Abd Manaf, Azizah Araghi, Sagheb Kohpayeh Univ Teknol Malaysia Adv Informat Sch Kuala Lumpur 54100 Malaysia Iranian Social Secur Org Arak *** Iran Multimedia Univ Malaysia Fac Engn Cyberjaya 63100 Selangor Malaysia

In this paper, a secure blind image watermarking scheme is proposed to improve efficiency of hybrid Discrete wavelet Transform (DWT) and Singular Value Decomposition (SVD) based schemes. The main contribution of this research is using two-level singular value decomposition in hybrid DWT+SVD watermarking schemes results to high improvement in both imperceptibility and robustness. Also, security is ensured in this scheme by proposing a two-level authentication system for watermark extraction in which not only the false positive problem is eliminated, when the attacks are very severe, the false negative effect is also detected and prevented. In the proposed scheme, the cover image is initially transformed to DWT and the high frequency sub bands are selected. Subsequently, High (HH) sub band is divided into 8 x 8 non overlapping blocks and the SVD transform is applied to each of them. In SVD transform, the highest energy compaction is in the first row and the first column of the matrix of singular values. These singular values for each block will be collected in a separate matrix and then the second SVD transform will be exerted on the produced matrix. The singular values of the watermark image after exerting one level DWT, is embedded into the singular values of this matrix. Experimental result shows when the scheme is subjected to ten most common types of signal processing and geometric attacks, using the second level of SVD makes it highly robust and imperceptible while it is independent from type of image. So, the proposed scheme is tested and certified for both medical and normal images. This scheme is compared to a range of hybrid schemes using DWT and one level SVD, and experimental results show high improvements in imperceptibility with the average of 46.6950 db and robustness more than 99% and proven efficiency in security. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Digital image Watermarking DWT SVD Medical images Digital signature

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