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2014 International Conference on Wavelet Analysis and Pattern Recognition最新文献

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Human action recognition using action bank and RBFNN trained by L-GEM 利用L-GEM训练的动作库和RBFNN进行人体动作识别
2014 International Conference on Wavelet Analysis and Pattern Recognition Pub Date : 2014-07-13 DOI: 10.1109/ICWAPR.2014.6961286
Zi-Ming Wu, W. W. Ng
{"title":"Human action recognition using action bank and RBFNN trained by L-GEM","authors":"Zi-Ming Wu, W. W. Ng","doi":"10.1109/ICWAPR.2014.6961286","DOIUrl":"https://doi.org/10.1109/ICWAPR.2014.6961286","url":null,"abstract":"Visual surveillance is widely used in monitoring, entertainment and public security in recent years. This arouses the growing demand of automatic analysis system to deal with large amount of data produced by video cameras. Human action recognition is one of the most popular topics in video analysis. However, human activities are extremely complex and the dimensions of features extracted from a video are very large. Hence, the construction of a highly accurate and fast classifier becomes one of the major challenging tasks in human action recognition researches. In this paper, we proposed an action recognition approach using a Radial Basis Function Neural Network (RBFNN) trained by the Localized Generalization Error Model (L-GEM). Representative feature vectors are extracted from videos by the Action Bank and then used as the inputs of the RBFNN. The reduction of uncertainty process is then applied to reduced noise from different classes. In our experiments, the proposed method outperforms SVM for human action recognition.","PeriodicalId":439086,"journal":{"name":"2014 International Conference on Wavelet Analysis and Pattern Recognition","volume":"409 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128685682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
Compression of power system signals with wavelets 电力系统信号的小波压缩
2014 International Conference on Wavelet Analysis and Pattern Recognition Pub Date : 2014-07-13 DOI: 10.1109/ICWAPR.2014.6961300
Chun Sing Lai
{"title":"Compression of power system signals with wavelets","authors":"Chun Sing Lai","doi":"10.1109/ICWAPR.2014.6961300","DOIUrl":"https://doi.org/10.1109/ICWAPR.2014.6961300","url":null,"abstract":"This paper reviews some technical issues in investigating real-time data analytics for future power grid systems. Wavelet transform will be used to demonstrate how current and voltage signals are compressed as a possible solution to data compression, and limitations of the technique will be highlighted.","PeriodicalId":439086,"journal":{"name":"2014 International Conference on Wavelet Analysis and Pattern Recognition","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115882784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
Analysis of the electrical impedance tomography algorithm based on finite element method and Tikhonov regularization 基于有限元法和吉洪诺夫正则化的电阻抗层析成像算法分析
2014 International Conference on Wavelet Analysis and Pattern Recognition Pub Date : 2014-07-13 DOI: 10.1109/ICWAPR.2014.6961287
Lin Lu, Lili Liu, Chun Hu
{"title":"Analysis of the electrical impedance tomography algorithm based on finite element method and Tikhonov regularization","authors":"Lin Lu, Lili Liu, Chun Hu","doi":"10.1109/ICWAPR.2014.6961287","DOIUrl":"https://doi.org/10.1109/ICWAPR.2014.6961287","url":null,"abstract":"Electrical impedance tomography is able to estimate the electrical properties at the interior of a biological tissue from voltage and current measurement data on its boundary. It can be used to achieve functional imaging of tissues. Electrical impedance tomography is composed of a forward problem and an inverse problem. In this paper, a two-dimensional model of electrical impedance tomography is built first. Then, a finite element subdivision of target area is realized to establish finite element equations of forward problem. In consideration of the uncertainty of inverse problem, a Tikhonov regularization method is performed and Gauss-Newton iteration method is adopted to get stable solution. Several simulation experiments are carried out to evaluate the performance of the image reconstruction algorithm based on finite element method and Tikhonov regularization. Results indicate that the reconstructed images already have the ability to show the impedance distribution.","PeriodicalId":439086,"journal":{"name":"2014 International Conference on Wavelet Analysis and Pattern Recognition","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115889839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
The fault diagnosis of rolling bearing in gearbox of wind turbines based on second generation wavelet 基于第二代小波的风电齿轮箱滚动轴承故障诊断
2014 International Conference on Wavelet Analysis and Pattern Recognition Pub Date : 2014-07-13 DOI: 10.1109/ICWAPR.2014.6961288
Yuegang Lv, Ning Guan, Juncheng Liu, Tengqian Cai
{"title":"The fault diagnosis of rolling bearing in gearbox of wind turbines based on second generation wavelet","authors":"Yuegang Lv, Ning Guan, Juncheng Liu, Tengqian Cai","doi":"10.1109/ICWAPR.2014.6961288","DOIUrl":"https://doi.org/10.1109/ICWAPR.2014.6961288","url":null,"abstract":"There are two difficulties when we try to diagnose the fault of rolling bearing in gearbox of wind turbines. Firstly, gearbox typically has multi-level structure and every level produces different speed ratios. The complex structure increases the difficulty of identifying accurately faults of rolling bearing on intermediate shaft. Secondly, it's not easy to determine fault feature frequency components in spectrum. Currently the research of gearbox fault diagnosis is based on the vibration signal collected from box. Obviously there is interference on the signal because the procedure of transmission consists of too many links and sensor has various working conditions. This paper aims at solving the two problems. The first part of this paper mainly deals with rotating speed and speed ratio of shafts in gearbox and the later part processes vibration signal. Wavelet is a widely-used analysis method for vibration signal. Unlike traditional wavelet, second-generation wavelet is independent of Fourier transform and owns self-adaption. It is easier to detect the fault characteristic frequency in noisy signal. Firstly, the original signals are decomposed and reconstructed by second-generation wavelet. Then, the components with the most fault information are selected for Hilbert envelope spectrum analysis according to the parameter Kurtosis. Finally, the fault characteristic frequency is extracted based on the Hilbert envelope spectrum and the fault type is also identified. The result shows that this method is correct and efficient.","PeriodicalId":439086,"journal":{"name":"2014 International Conference on Wavelet Analysis and Pattern Recognition","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115922937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
The de-noising method of threshold function based on wavelet 基于小波的阈值函数去噪方法
2014 International Conference on Wavelet Analysis and Pattern Recognition Pub Date : 2014-07-13 DOI: 10.1109/ICWAPR.2014.6961296
Kun Yang, Caixia Deng, Yu Chen, Li-Xiang Xu
{"title":"The de-noising method of threshold function based on wavelet","authors":"Kun Yang, Caixia Deng, Yu Chen, Li-Xiang Xu","doi":"10.1109/ICWAPR.2014.6961296","DOIUrl":"https://doi.org/10.1109/ICWAPR.2014.6961296","url":null,"abstract":"Image in the process of collection and storage can produce noise. Wavelet threshold de-noising is a method to remove noise effectively. The threshold function is a key in wavelet threshold de-noising method. In view of the hard-threshold function discontinuity and soft-threshold function deviation problem, through the analysis of the existing wavelet threshold de-noising methods, we present the improvement de-noising method of threshold function with the parameters in adjustable. Through experiments, we compare our method with the existing methods of wavelet threshold de-noising. In terms of the evaluation criteria, we find that the improved method of threshold function can remove the noise effectively.","PeriodicalId":439086,"journal":{"name":"2014 International Conference on Wavelet Analysis and Pattern Recognition","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126624625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
Two-dimensional wavelet multi-resolution analysis on turbulent structures of dune wake 沙丘尾流湍流结构的二维小波多分辨率分析
2014 International Conference on Wavelet Analysis and Pattern Recognition Pub Date : 2014-07-13 DOI: 10.1109/ICWAPR.2014.6961307
Yan Zheng, S. Fujimoto, A. Rinoshika
{"title":"Two-dimensional wavelet multi-resolution analysis on turbulent structures of dune wake","authors":"Yan Zheng, S. Fujimoto, A. Rinoshika","doi":"10.1109/ICWAPR.2014.6961307","DOIUrl":"https://doi.org/10.1109/ICWAPR.2014.6961307","url":null,"abstract":"PIV experiments of dune wake were first carried out at a Reynolds number of 4660. Then two-dimensional orthogonal wavelet multi-resolution technique was applied to analyze flow structures of various scales. The instantaneous velocity and vorticity were decomposed into the large-, intermediate- and relatively small-scale components by the two-dimensional wavelet multi-resolution technique. The scale length of each wavelet component is determined by two-point autocorrelation function with the central scales of 36mm, 20mm and 12mm respectively. The turbulent kinetic energy is quantified based on multi-scale velocity. It is found that the separation region is dominated by large-scale vortical structures and its vorticity concentration makes main contribution, and the intermediate vortices in the redeveloped turbulence boundary layer lead to the breakdown process of vortical structures at the downstream of separation region. The turbulent kinetic energy is mainly contributed from the large- and intermediate-scale components, and the large-scale component makes the most contribution.","PeriodicalId":439086,"journal":{"name":"2014 International Conference on Wavelet Analysis and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132766029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
The novel directional selection based on complex discrete wavelet transform 基于复离散小波变换的方向选择方法
2014 International Conference on Wavelet Analysis and Pattern Recognition Pub Date : 2014-07-13 DOI: 10.1109/ICWAPR.2014.6961309
T. Kato, Zhong Zhang, H. Toda, T. Imamura, T. Miyake
{"title":"The novel directional selection based on complex discrete wavelet transform","authors":"T. Kato, Zhong Zhang, H. Toda, T. Imamura, T. Miyake","doi":"10.1109/ICWAPR.2014.6961309","DOIUrl":"https://doi.org/10.1109/ICWAPR.2014.6961309","url":null,"abstract":"This paper proposes the novel directional selection based on the complex discrete wavelet transform (CDWT). CDWT is known as a very useful image processing method because of shift-invariant property. Another property of the CDWT is the directional selection that can detect the edges with different direction in images and the directional selection is expected as a geometric feature extraction method. However, the directional selection of the CDWT does not offer various directional features. Therefore, we propose the novel directional selection based on the CDWT and the directional filters We design the directional filters that can offers many directional features references from the curvelet transform and the contourlet Transform.","PeriodicalId":439086,"journal":{"name":"2014 International Conference on Wavelet Analysis and Pattern Recognition","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114596913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Gabor transformation on the circle 圆上的Gabor变换
2014 International Conference on Wavelet Analysis and Pattern Recognition Pub Date : 2014-07-13 DOI: 10.1109/ICWAPR.2014.6961302
K. Fujita
{"title":"Gabor transformation on the circle","authors":"K. Fujita","doi":"10.1109/ICWAPR.2014.6961302","DOIUrl":"https://doi.org/10.1109/ICWAPR.2014.6961302","url":null,"abstract":"In [2] and [3], we considered the Gabor transform of analytic functionals on the sphere in general dimension and we expressed it by a series expansion with the spherical harmonics and the Bessel functions. In this paper, following our previous results, we will consider the Gabor transform of analytic functionals, especially of square integrable functions on the circle (2-dimensional sphere), in more detail. Then we will construct the inverse Gabor transformation explicitly.","PeriodicalId":439086,"journal":{"name":"2014 International Conference on Wavelet Analysis and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129893916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
A method of image classification based on SIFT-Gabor-Scale descriptors 基于SIFT-Gabor-Scale描述符的图像分类方法
2014 International Conference on Wavelet Analysis and Pattern Recognition Pub Date : 2014-07-13 DOI: 10.1109/ICWAPR.2014.6961292
Mingming Huang, Zhichun Mu, Hui Zeng
{"title":"A method of image classification based on SIFT-Gabor-Scale descriptors","authors":"Mingming Huang, Zhichun Mu, Hui Zeng","doi":"10.1109/ICWAPR.2014.6961292","DOIUrl":"https://doi.org/10.1109/ICWAPR.2014.6961292","url":null,"abstract":"In this paper, we propose a new method of image classification based on SIFT-Gabor-Scale descriptors. At first, we design the patch-based SIFT-Gabor-Scale descriptor by integrating SIFT and Gabor-Scale features. Then a compact image presentation is obtained with the sparse coding spatial pyramid matching (ScSPM) method. Finally, image classification is implemented effectively with the simple linear SVM. Experimental results show that the proposed approach has a better classification performance on Caltech-101 dataset comparing to other methods.","PeriodicalId":439086,"journal":{"name":"2014 International Conference on Wavelet Analysis and Pattern Recognition","volume":"177 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121141683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Face liveness detection by focusing on frontal faces and image backgrounds 基于正面人脸和图像背景的人脸活动性检测
2014 International Conference on Wavelet Analysis and Pattern Recognition Pub Date : 2014-07-13 DOI: 10.1109/ICWAPR.2014.6961297
Libin Yang
{"title":"Face liveness detection by focusing on frontal faces and image backgrounds","authors":"Libin Yang","doi":"10.1109/ICWAPR.2014.6961297","DOIUrl":"https://doi.org/10.1109/ICWAPR.2014.6961297","url":null,"abstract":"Face recognition systems have been deployed in many security applications nowadays. Its popularity attracts the attention of an adversary. One of the common methods is to show a photo or video in front of the camera to mislead the detection of the system. In this paper, we propose a face liveness detection method by investigating the focus distance between the face and the background. The focus distance should be the same for the photo or video, while the distance should be different for a real person. The experimental results illustrate that the performance of the detection is improved significantly by using our method in comparison with the one considering the focus distance between the nose and the ear.","PeriodicalId":439086,"journal":{"name":"2014 International Conference on Wavelet Analysis and Pattern Recognition","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131896097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 14
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