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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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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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Low SNR Sonar image Restoration Based on Mixed Probability Statistical Model in wavelet Domain

Low SNR Sonar Image Restoration Based on Mixed Probability S...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Ping Xia Qiang Ren Dong-xia Shi Bang-jun Lei Yao-bin Zou Guang-zhu Xv Hubei Key Laboratory of Intelligent Vision based Monitoring for Hydroelectric Engineering College of Computer and Information Technology Three Gorges University YiChang China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Since it is difficult to get texture details for sonar image denoising with strong noise and weak feature information. A sonar image enhancement algorithm in wavelet domain based on Gaussian mixture model and Gaussian mixture model is proposed to preserve the weak feature information of the sonar image. Under strong noise interference, the traditional enhancement method has certain difficulties in measuring the similarity of the details of the sonar image. For this reason, wavelet multi-scale analysis is performed on the sonar image to extract the weak feature information of each resolution. Secondly, a directed probability map model between adjacent scales in the wavelet domain is constructed to realize the similar weak feature information association. According to the parent node state probability and the transition probability matrix, the state values of the corresponding child nodes are determined, and the relationship between the state information of parent and child node is constructed. Thirdly, the Gaussian mixture model is used to fit the wavelet coefficient state distribution, and the neighborhood coefficient correlation is used to describe the relationship between the weak feature information in the scale. Finally, the wavelet coefficient estimation of the restored image signal is calculated by the state probability obtained in the expectation maximization (EM) algorithm, and the sonar image is reconstructed. The results of the contrast experiment are verified by visual effects and objective evaluation. The proposed algorithm can preserve the weak edge and contour information while suppressing the noise of the sonar image. It has better peak signal-to-noise ratio, signal-to-noise ratio and Structural similarity.

关键词： Sonar wavelet coefficients wavelet domain Hidden Markov models image enhancement Feature extraction wavelet analysis

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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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High performance VLSI Architecture for 3-D DWT (Discrete wavelet Transform) 2

High performance VLSI Architecture for 3-D DWT (Discrete Wav...

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2nd International conference on Inventive Systems and Control (ICISC)

作者： Karthikeyan, R. Hegde, Ganapathi Amrita Vishwa Vidyapeetham Dept Elect & Commun Engn Amrita Sch Engn Bengaluru India

ISBN: (纸本)9781538608074

In the image and video processing industry, Video Compression is the major concern to take care of, which efficiently deals many applications include Medical image processing, Broadcasting Scenarios and many more. This kind of compression takes much processing compare to the normal image compression methods, because video processing contains lots of frames associated with it and large amount of memory is required to process with as well as time taken is more to processing the videos. For these issues, a new methodology is required to handle such problems, three dimensional(3-D) discrete wavelet Transform (DWT) is introduced over here to decompose the video into frames substantially with proper signal levels as well as make them as a set of finite rules. This 3-D DWT methodology is mainly used in this approach to reduce the difficult processing delay and improves the throughput efficiency. In this paper, Lifting Approach is used to process the input in more refined manner, which reduces the memory consumption over processing as well as minimize the difficult path-delay over DWT orientations. That is the reason with this approach, the combination of both 3-D DWT and Lifting Scheme are used to attain higher efficiency, lowest delay and highest throughput compare to several past analysis.

关键词： 3-D DWT DWT with Lifting Scheme VLSI Video Compression wavelet Transformation Throughput

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An image Watermarking Algorithm Based on Energy Scheme in the wavelet Transform Domain 3

An Image Watermarking Algorithm Based on Energy Scheme in th...

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3rd IEEE International conference on image, Vision and Computing (ICIVC)

作者： Liu, Jinhua Shangrao Normal Univ Sch Math & Comp Sci Shangrao Peoples R China

ISBN: (纸本)9781538649916

With the increasing demands of copyright protection, digital watermarking has been paid more and more attention. In the design of a watermarking method, the modeling of signal by a general parametric family of statistical distributions plays an important role in many image watermarking applications. In this paper, the probability density function of wavelet coefficients is modeled by the generalized Gaussian distribution (GGD), and the decision threshold is obtained by the Neyman-Pearson (NP) criterion. In the procedure of watermark embedding, the energy of image block is considered in the watermark embedding. Only those blocks whose energy exceeds a predetermined threshold are used to embed the watermark data. Its improved robustness is due to embedding in the significant wavelet coefficients based on the energy scheme and control of its strength factor from the variance of coefficient. Experimental results demonstrate that the effectiveness of the presented watermarking and its robustness against common image processing and some kinds of geometric attacks.

关键词： watermarking generalized gaussian distribution energy wavelets neyman-pearson

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COMPARISON OF OPTIMIZED MATHEMATICAL METHODS IN THE IMPROVEMENT OF RASTER DATA AND MAP DISPLAY RESOLUTION OF SENTINEL-2 imageS 25

COMPARISON OF OPTIMIZED MATHEMATICAL METHODS IN THE IMPROVEM...

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25th IEEE International conference on image processing (ICIP)

作者： Bratsolis, Emmanuel Panagiotopoulou, Antigoni Stefouli, Marianthi Charou, Eleni Madamopoulos, Nicholas Perantonis, Stavros NCSR Demokritos Inst Informat & Telecommun Aghia Paraskevi 15310 Greece Greek Inst Geol & Mineral Explorat IGME Acharne 13677 Greece Hellen Air Force Acad Dept Aeronaut Sci Tga 1010 Dhekeleia Greece

ISBN: (纸本)9781479970612

High-Resolution (HR) satellite images are a prerequisite in many applications such as astronomy, remote sensing, geoscience and geographical information systems, not only for providing better visualization but also for extracting extra information details. In the present work a comparative study of different single image resolution enhancement techniques is carried out on Sentinel-2 images of bands B2, B3, B4 and B8. The authors describe the stochastic regularized super-resolution (SR) reconstruction technique and compare with others. The techniques under comparison are stochastic regularized SR reconstruction (SRSR), spatial-wavelet SR reconstruction (SWSR) and the conventional interpolation techniques nearest neighbor (NN), bilinear (BL), bicubic (BC) and spline (SP). These techniques are tested against each other in terms of Root Mean Square Error (RMSE), Xydeas and Petrovich (XP), and Correlation Coefficient (CC). Simulated experiments of single image resolution increase take place.

关键词： Stochastic regularized technique spatial-wavelet transform super-resolution reconstruction interpolation satellite image resolution enhancement

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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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Speech signal De-noising using wavelet Transform and Different Standard Softwares: Performance Evaluation and Comparisons Study 5

Speech Signal De-noising using Wavelet Transform and Differe...

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5th IEEE-Uttar-Pradesh-Section International conference on Electrical, Electronics and Computer Engineering (UPCON)

作者： Lavi Ahuja, Amit Kumar Meerut Inst Engn & Technol Dept Elect & Commun Engn NH-58Baghpat Rd Meerut 250005 Uttar Pradesh India

ISBN: (纸本)9781538650028

Speech signal processing is used in many applications. There exist situations where conversation takes place under high noise environment. A specific example is the telephonic conversations held at airport with background aeroplane sound. Other examples are the conversation held in industrial environment with high noise machines, in railway stations etc. In all of these conditions, conversation (voice signal) get corrupted by background sound (noise) reducing its intelligibility. Thus, the need arises for an effective method that helps in reducing the background noise thereby increasing clarity of the conversation. wavelet Transform (WT) is widely used for audio, image and video signal processing. This paper presents a wavelet-based approach to reduce the effect of background noise. This approach is compared with the standard, freely available softwares such as Audacity, Wavepad and Ocenaudio which are widely used for the purpose of noise reduction. The AWGN (Additive White Gaussian Noise) is assumed to be a model of background noise. Using extensive simulation study and analysis which is carried out using MATLAB (R) 2016a, WT based approach is found to perform better than the other standard softwares that are considered here. Correlation co-efficient is considered as a measure of performance improvement by the WT approach over the other software. The work has useful implications in various applications in which speech signal gets degraded by background noise.

关键词： Audacity Ocenaudio Wavepad wavelet Transform Noise removal AWGN

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Theory and applications of Time-Frequency Methods for Analysis of Non-Stationary Vibration and Seismic signal 2018

Theory and Applications of Time-Frequency Methods for Analys...

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2nd International conference on Vision, image and signal processing (ICVISP)

作者： Kumar, Roshan Zhao, Wei Henan Univ Miami Coll Dept Elect Kaifeng 475004 Henan Peoples R China

ISBN: (纸本)9781450365291

The classical Fourier transform method does not reveal the temporal information of the signal, whereas the time-frequency methods provide the comprehensive information of non-stationary signal over the time-frequency plane. Based on the typical non-stationary signals several time-frequency methods such as short time Fourier transform, wavelet transform, Wigner -Ville distribution, Pseudo Wigner -Ville distribution, Hilbert Huang transform are evaluated. In this paper, these time-frequency methods are compared to the performance of Gabor -Wigner transform which proves the effectiveness of the GWT in comparison to other methods. The efficacy of this method is validated by two different numerical case studies, and it is further applied for damage detection of buildings which provides a significant information pertaining to damage to the building.

关键词： Fourier transform Time-frequency Cross-term Damage Gabor-Wigner Transform

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