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4th IEEE International conference on signal and image processing (ICSIP)

作者： Yang, Jianbin Zhang, Enlin Wang, Cong Hohai Univ Dept Math Coll Sci Nanjing Jiangsu Peoples R China Xidian Univ Sch Electromech Engn Xian Shaanxi Peoples R China

ISBN: (纸本)9781728136608

image segmentation aims at partitioning an image into its constituent parts, which plays a crucial role in practical applications. In this paper, we present a wavelet frame-based model for color images segmentation, which can be regarded as a discretization to the classical Chan-Vese (C-V) model. The advantage of the wavelet frame-based approach is that it has fast algorithm and is able to extract important features of the input images. We then apply the alternating direction method of multipliers (ADAM) algorithm to solve the model. The experiments on some color image segmentation tasks indicate that our algorithm performs favorably against several existing methods.

关键词： wavelet frame image segmentation ADMM algorithm color images

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Combining Scatter Transform and Deep Neural Networks for Multilabel Electrocardiogram signal Classification

Combining Scatter Transform and Deep Neural Networks for Mul...

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Computing in Cardiology conference (CinC)

作者： Oppelt, Maximilian P. Riehl, Maximilian Kemeth, Felix P. Steffan, Jan Fraunhofer IIS Dept Image Proc & Med Engn Wolfsmantel 33 D-91058 Erlangen Germany Johns Hopkins Univ Dept Chem & Biomol Engn Whiting Sch Engn Baltimore MD 21218 USA

ISBN: (纸本)9781728173825

An essential part for the accurate classification of electrocardiogram (ECG) signals is the extraction of informative yet general features, which are able to discriminate diseases. Cardiovascular abnormalities manifest themselves in features on different time scales: small scale morphological features, such as missing P-waves, as well as rhythmical features apparent on heart rate scales. For this reason we incorporate a variant of the complex wavelet transform, called a scatter transform, in a deep residual neural network (ResNet). The former has the advantage of being derived from theory, making it well behaved under certain transformations of the input. The latter has proven useful in ECG classification, allowing feature extraction and classification to be learned in an end-to-end manner. Through the incorporation of trainable layers in between scatter transforms, the model gains the ability to combine information from different channels, yielding more informative features for the classification task and adapting them to the specific domain. For evaluation, we submitted our model in the official phase in the PhysioNet/Computing in Cardiology Challenge 2020. Our (Team Triage) approach achieved a challenge validation score of 0.640, and full test score of 0.485, placing us 4th out of 41 in the official ranking.

关键词： Classification (of information)

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Single image Ear Recognition Using wavelet-Based Multi-Band PCA 27

Single Image Ear Recognition Using Wavelet-Based Multi-Band ...

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27th European signal processing conference (EUSIPCO)

作者： Zarachoff, Matthew Sheikh-Akbari, Akbar Monekosso, Dorothy Leeds Beckett Univ Sch Comp Creat Technol & Engn Leeds W Yorkshire England

ISBN: (纸本)9789082797039

Principal Component Analysis (PCA) has been successfully used for many applications, including ear recognition. This paper presents a 2D wavelet based Multi-Band PCA (2D-WMBPCA) method, inspired by PCA based techniques for multispectral and hyperspectral images, which have shown a significantly higher performance to that of standard PCA. The proposed method performs 2D non-decimated wavelet transform on the input image dividing the image into its subbands. It then splits each resulting subband into a number of bands evenly based on the coefficient values. Standard PCA is then applied on each resulting set of bands to extract the subbands eigenvectors, which are used as features for matching. Experimental results on images of two benchmark ear image datasets show that the proposed 2D-WMBPCA significantly outperforms both the standard PCA method and the eigenfaces method.

关键词： Ear recognition principal component analysis multi-band image creation non-decimated wavelet transform

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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 (IEEE ICSIPA)

作者： Akbari, Somayeh Ghassemian, Hassan Akbari, Zahra Tarbiat Modares Univ Fac Elect & Comp Engn Image Proc & Informat Anal Lab Tehran Iran

ISBN: (纸本)9781728133775

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.

关键词： PCG signal MFCC algorithm power spectrum estimation wavelet transform feature fusion decision fusion

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A new end-to-end image compression system based on convolutional neural networks 42

A new end-to-end image compression system based on convoluti...

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conference on applications of Digital image processing XLII

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

ISBN: (数字)9781510629684

ISBN: (纸本)9781510629684

In this paper, two new end-to-end image compression architectures based on convolutional neural networks are presented. The proposed networks employ 2D wavelet decomposition as a preprocessing step before training and extract features for compression from wavelet coefficients. Training is performed end-to-end and multiple models operating at different rate points are generated by using a regularizer in the loss function. Results show that the proposed methods outperform JPEG compression, reduce blocking and blurring artifacts, and preserve more details in the images especially at low bitrates.

关键词： Learning-based image compression low-rate image compression deep convolutional neural networks discrete wavelet transform

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image compression and enlargement algorithms

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International Journal of Circuits, Systems and signal processing 2021年 15卷 836-846页

作者： Burova, I.G. Dem’yanovich, Yu.K. Terekhov, A.N. Altynova, A.Yu. Satanovskiy, A.D. Babushkin, A.A. St. Petersburg State University 7/9 Universitetskaya nab St.Petersburg199034 Russia

In some cases, there are problems associated with the compression and enlargement of images. The use of splines is quite effective in some cases. In this paper, a new image compression algorithm is presented. The features of increasing the size of an image when using local polynomial or non-polynomial splines are considered. The main method for enlarging an image is based on the use of splines of the second and third order of approximation. Polynomial and trigonometric splines are considered. To speed up the process of enlarging the image, we used the parallelization techniques. © 2021, North Atlantic University Union NAUN. All rights reserved.

关键词： image compression

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image processing by Using Different Types of Discrete wavelet Transform 2nd

Image Processing by Using Different Types of Discrete Wavele...

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2nd International conference on Advanced Informatics for Computing Research (ICAICR)

作者： Thakral, Shaveta Manhas, Pratima Manav Rachna Int Inst Res & Studies ECE Dept FET Faridabad India

ISBN: (纸本)9789811331404;9789811331398

image processing is emerging research area which seeks attention in biomedical field. There are lots of image processing techniques which are not only useful in extracting useful information for analysis purpose but also saves computation time and memory space. Transformation is one such type of image processing technique. Examples of transform techniques are Hilbert transform, Fourier transform, Radon Transform, wavelet transform etc. Transform technique may be chosen based on its advantages, disadvantages and applications. The wavelet transform is a technique which assimilates the time and frequency domains and precisely popular as time-frequency representation of a non stationary signal. In this paper different types of Discrete wavelet transform is applied on an image. Comparative analysis of different wavelets such as Haar, Daubechies and symlet 2 is applied on image and different filters respond are plotted using MATLAB 15.

关键词： Pixel Discrete wavelet transform image Filters Stationary Reference

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BLIND QUALITY ASSESSMENT FOR 3D-SYNTHESIZED imageS BY MEASURING GEOMETRIC DISTORTIONS AND image COMPLEXITY 44

BLIND QUALITY ASSESSMENT FOR 3D-SYNTHESIZED IMAGES BY MEASUR...

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44th IEEE International conference on Acoustics, Speech and signal processing (ICASSP)

作者： Wang, Guangcheng Wang, Zhongyuan Gu, Ke Xia, Zhifang Wuhan Univ Sch Comp Sci Natl Engn Res Ctr Multimedia Software Wuhan Hubei Peoples R China Beijing Univ Technol Fac Informat Technol Beijing Peoples R China State Informat Ctr PR China Beijing Peoples R China

ISBN: (纸本)9781479981311

Free viewpoint video (FVV), owing to its comprehensive applications in immersive entertainment, remote surveillance and distanced education, has received extensive attention and been regarded as a new important direction of video technology development. Depth image-based rendering (DIBR) technologies are employed to synthesize FVV images in the "blind" environment. Therefore, a real-time reliable blind quality assessment metric is urgently required. However, existing stste-of-art quality assessment methods are limited to estimate geometric distortions generated by DIBR. In this research, a novel blind quality metric, measuring Geometric Distortions and image Complexity (GDIC), is proposed for DIBR-synthesized images. Firstly, a DIBR-synthesized image is decomposed into wavelet subbands by using discrete wavelet transform. Then, we adopt canny operator to capture the edge of wavelet subbands and compute the edge similarity between low -frequency subband and high frequency subbands. The edge similarity is used to quantify geometric distortions in DIBR-synthesized images. Secondly, a hybrid filter combining the autoregressive and bilateral filter is adopted to compute image complexity. Finally, the overall quality score is calculated by normalizing geometric distortions via image complexity. Experiments show that our proposed GDIC is superior to prevailing image quality assessment metrics, which were intended for natural and DIBR-synthesized images.

关键词： Depth image-based rendering image quality assessment blind

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image Denoising Techniques: A Brief Survey 4th

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4th International conference on Harmony Search, Soft Computing and applications (ICHSA)

作者： Singh, Lokesh Janghel, Rekhram Natl Inst Technol Raipur Chhattisgarh India

ISBN: (纸本)9789811307614;9789811307607

In digital image processing, noise suppression from the original signal is still considered as biggest challenge till today. image denoising refers to the process in which it evaluates the unknown signal from the available noisy signal. Several algorithms are existing, which are proposed by other authors for denoising of an image like Discrete Cosine Transform (DCT), Discrete wavelet Transform (DWT), etc. This paper contributes itswork by discussing the significant work done in the area of image denoising along with advantages and disadvantages. After a brief discussion, classification of image denoising techniques is explained. A comparative analysis of various image denoising methods is also performed, which will help researchers in the image denoising area. The objective of this review paper is to provide functional knowledge of image denoising methods in a nutshell for applications using images to provide an ease for selecting the ideal strategy according to the necessity.

关键词： image Denoising Discrete Cosine Transform Discrete wavelet Transform

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Design of Symlet wavelet based Illumination Normalization Algorithm and its Comparison with other Relevant Algorithms 42

Design of Symlet Wavelet based Illumination Normalization Al...

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42nd International conference on Telecommunications and signal processing (TSP)

作者： Lamichhane, Kamal Mazumdar, Pramit Univ Roma La Sapienza Rome Italy Univ Studi Roma TRE Rome Italy

ISBN: (纸本)9781728118642

image processing techniques may be used for enhancing edges, boundaries, contrast, etc. of an image through accentuation or sharpening process. Many algorithms are available to normalize illumination impact for different image processing applications. In this contribution, we conduct a comparative study on four different types of illumination normalization algorithms. They are based on discrete wavelet, logarithmic total variation with primal dual algorithm, histogram equalization technique, and morphological operation. In order to obtain better enhancement of image by using discrete wavelet based illumination normalization algorithm, selection of the particular wavelet is very important. Use of histogram equalization function in image preprocessing with other algorithms enhances the overall performance of face detection. This paper illustrates performance of different illumination normalization algorithms in Viola-Jones face detection system based on the extended Yale B database.

关键词： Discrete wavelet transform Face detection Histogram equalization Illumination normalization Viola-Jones algorithm

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