Pub Date : 2009-07-12DOI: 10.1109/ICWAPR.2009.5207492
Sheng-bing Che, Bin Ma, Zuguo Che, Quiangbo Huang
In this paper, a wavelet-based digital image watermarking algorithm is putforward, advantage of the zero-watermark. In proposed algorithm, the key is generated by XOR operation.This algorithm has very srong robustness, because of the pixel point are hardly changed. At the same time it has very strong invisibility because it dose not modify the data of the original image. Experiments show that a better robustness to the image processing such as JPEG compression, noise adding and smoothing filtering can be achieved. Meanwhile, embedding infomation in a large amount, the number of data bits reaches to a quarter that of the original image pixels. What's more, it can ascertain the position of vicious attack exactly.
{"title":"A wavelet-based method of zero-watermark","authors":"Sheng-bing Che, Bin Ma, Zuguo Che, Quiangbo Huang","doi":"10.1109/ICWAPR.2009.5207492","DOIUrl":"https://doi.org/10.1109/ICWAPR.2009.5207492","url":null,"abstract":"In this paper, a wavelet-based digital image watermarking algorithm is putforward, advantage of the zero-watermark. In proposed algorithm, the key is generated by XOR operation.This algorithm has very srong robustness, because of the pixel point are hardly changed. At the same time it has very strong invisibility because it dose not modify the data of the original image. Experiments show that a better robustness to the image processing such as JPEG compression, noise adding and smoothing filtering can be achieved. Meanwhile, embedding infomation in a large amount, the number of data bits reaches to a quarter that of the original image pixels. What's more, it can ascertain the position of vicious attack exactly.","PeriodicalId":424264,"journal":{"name":"2009 International Conference on Wavelet Analysis and Pattern Recognition","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130912305","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}
Pub Date : 2009-07-12DOI: 10.1109/ICWAPR.2009.5207434
Ming Zhang, Kaicheng Li
The power quality (PQ) signals are traditionally analyzed in the time-domain by skilled engineers. However, PQ disturbances may not always be obvious in the original time-domain signal. Fourier analysis transforms signals into frequency domain, but has the disadvantage that time characteristics will become unobvious. Wavelet analysis, which provides both time and frequency information, can overcome this limitation. In this paper, PQ signals were examined. There were two stages in analyzing PQ signals: feature extraction and disturbances classification. To extract features from PQ signals, wavelet packet transform (WPT) was first applied and feature vectors of relative wavelet log-energy entropy were constructed. Least square support vector machines (LS-SVM) was applied to these feature vectors to classify PQ disturbances. Simulation results show that the proposed method possesses high recognition rate, so it is suitable to the monitoring and classifying system for PQ disturbances.
{"title":"The application of wavelet energy entropy and LS-SVM to classify power quality disturbances","authors":"Ming Zhang, Kaicheng Li","doi":"10.1109/ICWAPR.2009.5207434","DOIUrl":"https://doi.org/10.1109/ICWAPR.2009.5207434","url":null,"abstract":"The power quality (PQ) signals are traditionally analyzed in the time-domain by skilled engineers. However, PQ disturbances may not always be obvious in the original time-domain signal. Fourier analysis transforms signals into frequency domain, but has the disadvantage that time characteristics will become unobvious. Wavelet analysis, which provides both time and frequency information, can overcome this limitation. In this paper, PQ signals were examined. There were two stages in analyzing PQ signals: feature extraction and disturbances classification. To extract features from PQ signals, wavelet packet transform (WPT) was first applied and feature vectors of relative wavelet log-energy entropy were constructed. Least square support vector machines (LS-SVM) was applied to these feature vectors to classify PQ disturbances. Simulation results show that the proposed method possesses high recognition rate, so it is suitable to the monitoring and classifying system for PQ disturbances.","PeriodicalId":424264,"journal":{"name":"2009 International Conference on Wavelet Analysis and Pattern Recognition","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124210793","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}
Pub Date : 2009-07-12DOI: 10.1109/ICWAPR.2009.5207462
H. Toda, Zhong Zhang
The complex discrete wavelet transform having perfect translation invariance has already been proposed. However, due to complication of frequency divisions with wavelet packets, it is difficult to design a complex wavelet packet transform having perfect translation invariance. In this paper, a useful theorem for achieving perfect translation invariance is proved, and a novel complex wavelet packet transform is disigned to create this perfect translate invariance. This complex wavelet packet transform is based on a Meyer wavelet, which has the important characteristic of having a wide range of shapes. Therefore, the complex wavelet packet transform having perfect translation invariance can be designed with the optimized shapes of the Meyer wavelet. One of them is based on a single Meyer wavelet and the other is based on a number of different shapes of the Meyer wavelets to create good localization of complex wavelet packets.
{"title":"Perfectly translation-invariant complex wavelet packet transforms","authors":"H. Toda, Zhong Zhang","doi":"10.1109/ICWAPR.2009.5207462","DOIUrl":"https://doi.org/10.1109/ICWAPR.2009.5207462","url":null,"abstract":"The complex discrete wavelet transform having perfect translation invariance has already been proposed. However, due to complication of frequency divisions with wavelet packets, it is difficult to design a complex wavelet packet transform having perfect translation invariance. In this paper, a useful theorem for achieving perfect translation invariance is proved, and a novel complex wavelet packet transform is disigned to create this perfect translate invariance. This complex wavelet packet transform is based on a Meyer wavelet, which has the important characteristic of having a wide range of shapes. Therefore, the complex wavelet packet transform having perfect translation invariance can be designed with the optimized shapes of the Meyer wavelet. One of them is based on a single Meyer wavelet and the other is based on a number of different shapes of the Meyer wavelets to create good localization of complex wavelet packets.","PeriodicalId":424264,"journal":{"name":"2009 International Conference on Wavelet Analysis and Pattern Recognition","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126030536","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}
Pub Date : 2009-07-12DOI: 10.1109/ICWAPR.2009.5207442
Liying Jiang, Yan Zhang, Zhong-Hai Li, Yibo Li
Independent signal is stricter than the non-correlated signal in math. Independent Component Analysis (ICA) can extract independent signals, so it is better than Principal Component Analysis (PCA) when they are used to diagnose faults. However ICA isn't suited for no-obvious faults which are caused by inputs' small changes. In order to solve this problem, multi-scale ICA (MSICA) is investigated in this paper, which is applied to aero-engine fault diagnosis. MSICA is used to extract independent components are used to train Support Vector Machine (SVM) for classification. Experiments demonstrate the benefits of this representation.
{"title":"Aero-engine fault diagnosis based on multi-scale Independent Component Analysis","authors":"Liying Jiang, Yan Zhang, Zhong-Hai Li, Yibo Li","doi":"10.1109/ICWAPR.2009.5207442","DOIUrl":"https://doi.org/10.1109/ICWAPR.2009.5207442","url":null,"abstract":"Independent signal is stricter than the non-correlated signal in math. Independent Component Analysis (ICA) can extract independent signals, so it is better than Principal Component Analysis (PCA) when they are used to diagnose faults. However ICA isn't suited for no-obvious faults which are caused by inputs' small changes. In order to solve this problem, multi-scale ICA (MSICA) is investigated in this paper, which is applied to aero-engine fault diagnosis. MSICA is used to extract independent components are used to train Support Vector Machine (SVM) for classification. Experiments demonstrate the benefits of this representation.","PeriodicalId":424264,"journal":{"name":"2009 International Conference on Wavelet Analysis and Pattern Recognition","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116661445","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}
The linear frequency modulation (LFM) signal is the main signal form of the high resolution radar. By analysis for the principle of the LFM radar signal to realize the high range resolution profile and the impact of clutter and noise over target echo signal, we proposes the method of using spectrum estimation based on the characteristic decomposition to estimate the frequency and realize the LFM signal range profile. The simulation results prove that this method to the goal 1-D range profile is effective under low signal-to-noise ratio conditions.
{"title":"A radar ranging algorithm based on characteristic decomposition power spectrum estimation","authors":"Jian-Zhong Xu, Zulin Wang, Xu-Jing Guo, Yi-Huan Zhao","doi":"10.1109/ICWAPR.2009.5207488","DOIUrl":"https://doi.org/10.1109/ICWAPR.2009.5207488","url":null,"abstract":"The linear frequency modulation (LFM) signal is the main signal form of the high resolution radar. By analysis for the principle of the LFM radar signal to realize the high range resolution profile and the impact of clutter and noise over target echo signal, we proposes the method of using spectrum estimation based on the characteristic decomposition to estimate the frequency and realize the LFM signal range profile. The simulation results prove that this method to the goal 1-D range profile is effective under low signal-to-noise ratio conditions.","PeriodicalId":424264,"journal":{"name":"2009 International Conference on Wavelet Analysis and Pattern Recognition","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121558695","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}
Pub Date : 2009-07-12DOI: 10.1109/ICWAPR.2009.5207420
Jing-Zhi Cai, Ming-xin Zhang, Jin-yi Chang
In current common research reports, salient regions are usually defined as those regions that could present the main meaningful or semantic contents, However, there are no uniform saliency metrics that could describe the saliency of implicit image regions. Most common metrics take those regions as salient regions, which have many abrupt changes or some unpredictable characteristics. But, this metric will fail to detect those salient useful regions with flat textures. In fact, according to human semantic perceptions, color and texture distinctions are the main characteristics that could distinct different regions. Thus, we present a novel saliency metric coupled with color and texture features, and its corresponding salient region extraction methods. In order to evaluate the corresponding saliency values of implicit regions in one image, three main colors and multi-resolution Gabor features are respectively used for color and texture features. For each region, its saliency value is actually to evaluate the total sum of its Euclidean distances for other regions in the color and texture spaces. A special synthesized image and several practical images with main salient regions are used to evaluate the performance of the proposed saliency metric and other several common metrics, i.e., scale saliency, wavelet transform modulus maxima point density, and important index based metrics. Experiment results verified that the proposed saliency metric could achieve more robust performance than those common saliency metrics.
{"title":"A novel salient region extraction based on color and texture features","authors":"Jing-Zhi Cai, Ming-xin Zhang, Jin-yi Chang","doi":"10.1109/ICWAPR.2009.5207420","DOIUrl":"https://doi.org/10.1109/ICWAPR.2009.5207420","url":null,"abstract":"In current common research reports, salient regions are usually defined as those regions that could present the main meaningful or semantic contents, However, there are no uniform saliency metrics that could describe the saliency of implicit image regions. Most common metrics take those regions as salient regions, which have many abrupt changes or some unpredictable characteristics. But, this metric will fail to detect those salient useful regions with flat textures. In fact, according to human semantic perceptions, color and texture distinctions are the main characteristics that could distinct different regions. Thus, we present a novel saliency metric coupled with color and texture features, and its corresponding salient region extraction methods. In order to evaluate the corresponding saliency values of implicit regions in one image, three main colors and multi-resolution Gabor features are respectively used for color and texture features. For each region, its saliency value is actually to evaluate the total sum of its Euclidean distances for other regions in the color and texture spaces. A special synthesized image and several practical images with main salient regions are used to evaluate the performance of the proposed saliency metric and other several common metrics, i.e., scale saliency, wavelet transform modulus maxima point density, and important index based metrics. Experiment results verified that the proposed saliency metric could achieve more robust performance than those common saliency metrics.","PeriodicalId":424264,"journal":{"name":"2009 International Conference on Wavelet Analysis and Pattern Recognition","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126935351","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}
Pub Date : 2009-07-12DOI: 10.1109/ICWAPR.2009.5207418
Y. Zheng, Taiping Zhang, Bin Fang, Yuanyan Tang
In this paper we proposed a novel supervised dimensionality reduction method, named Discriminant Isometric projection. The aim is to compact the data points from the same cluster on high-dimension manifold to make them closer in the low-dimension space, and to make the ones from the different cluster further, which is beneficial to preserve the homogeneous characteristics for classification. We compared our method with other three methods for dimensionality reduction over the ORL face dataset and experiments show that Discriminant Isometric projection produces stable performance and good precision.
{"title":"Discriminant Isomap projection","authors":"Y. Zheng, Taiping Zhang, Bin Fang, Yuanyan Tang","doi":"10.1109/ICWAPR.2009.5207418","DOIUrl":"https://doi.org/10.1109/ICWAPR.2009.5207418","url":null,"abstract":"In this paper we proposed a novel supervised dimensionality reduction method, named Discriminant Isometric projection. The aim is to compact the data points from the same cluster on high-dimension manifold to make them closer in the low-dimension space, and to make the ones from the different cluster further, which is beneficial to preserve the homogeneous characteristics for classification. We compared our method with other three methods for dimensionality reduction over the ORL face dataset and experiments show that Discriminant Isometric projection produces stable performance and good precision.","PeriodicalId":424264,"journal":{"name":"2009 International Conference on Wavelet Analysis and Pattern Recognition","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124526674","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}
Pub Date : 2009-07-12DOI: 10.1109/ICWAPR.2009.5207455
Meijuan Gao, Jingwen Tian, Shi-Ru Zhou
An actual physical simulation model is constructed to simulate the course of oil and water migration. Under certain physical property conditions, we simulated the water injection well and the oil well on the physical simulation model, and continuous measured online the oil and water content of different area of model in three-dimensional space using the 512 routes resistively measuring circuit, then we can obtain large numbers of simulation samples. Considering the issues that the relationship between the remaining oil and every parameters of water displacing oil is a complicated and nonlinear and the advantages of wavelet neural network (WNN), in this paper, the wavelet neural network is used to establish the oil and water migration model. Moreover, we adopt a algorithm of reduce the number of the wavelet basic function by analysis the sparseness property of sample data which can optimize the wavelet network in a large extent, and the network learning algorithm is studied. The simulation results show that this method is feasible and effective.
{"title":"Simulation study of oil and water migration modeling based on wavelet neural network","authors":"Meijuan Gao, Jingwen Tian, Shi-Ru Zhou","doi":"10.1109/ICWAPR.2009.5207455","DOIUrl":"https://doi.org/10.1109/ICWAPR.2009.5207455","url":null,"abstract":"An actual physical simulation model is constructed to simulate the course of oil and water migration. Under certain physical property conditions, we simulated the water injection well and the oil well on the physical simulation model, and continuous measured online the oil and water content of different area of model in three-dimensional space using the 512 routes resistively measuring circuit, then we can obtain large numbers of simulation samples. Considering the issues that the relationship between the remaining oil and every parameters of water displacing oil is a complicated and nonlinear and the advantages of wavelet neural network (WNN), in this paper, the wavelet neural network is used to establish the oil and water migration model. Moreover, we adopt a algorithm of reduce the number of the wavelet basic function by analysis the sparseness property of sample data which can optimize the wavelet network in a large extent, and the network learning algorithm is studied. The simulation results show that this method is feasible and effective.","PeriodicalId":424264,"journal":{"name":"2009 International Conference on Wavelet Analysis and Pattern Recognition","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127809917","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}
Pub Date : 2009-07-12DOI: 10.1109/ICWAPR.2009.5207486
Xiao-Mei Ye, Xiao-he Liu
The paper presents a harmonic detection method based on wavelet transform and FFT for electric arc furnaces system. The method not only overcomes the drawbacks of conventional Fourier transform, analyzing transient, non-stationary or time-varying event invalidated, but also avoids the disadvantages that only use wavelet transform can not obtain the precise value at a particular harmonic frequency. Simulation results of MATLAB have proved that the given method is reasonable valid.
{"title":"The harmonic detection based on wavelet transform and FFT for electric ARC furnaces","authors":"Xiao-Mei Ye, Xiao-he Liu","doi":"10.1109/ICWAPR.2009.5207486","DOIUrl":"https://doi.org/10.1109/ICWAPR.2009.5207486","url":null,"abstract":"The paper presents a harmonic detection method based on wavelet transform and FFT for electric arc furnaces system. The method not only overcomes the drawbacks of conventional Fourier transform, analyzing transient, non-stationary or time-varying event invalidated, but also avoids the disadvantages that only use wavelet transform can not obtain the precise value at a particular harmonic frequency. Simulation results of MATLAB have proved that the given method is reasonable valid.","PeriodicalId":424264,"journal":{"name":"2009 International Conference on Wavelet Analysis and Pattern Recognition","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133953533","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}
Pub Date : 2009-07-12DOI: 10.1109/ICWAPR.2009.5207479
Rushi Lan, Jianwei Yang, Y. Tang
A method, referred to as Composite of Central and Ring Projection (CCRP), is proposed to extract features with rotation invariant property. It reduces the dimensionality of a two-dimensional pattern by performing both central projection (CP) and ring projection (RP). A dissimilarity function is developed and used to distinguish different patterns. This function makes use of both similarity corrections of RP and CP. Information along both circles and polar angles can be retained from the original pattern. Some experiments have been conducted, in which a set of ambiguous printed Chinese characters which are partially damaged or polluted by noise were classified. The experiments have satisfying results.
{"title":"A Composite Of Central And Ring Projection","authors":"Rushi Lan, Jianwei Yang, Y. Tang","doi":"10.1109/ICWAPR.2009.5207479","DOIUrl":"https://doi.org/10.1109/ICWAPR.2009.5207479","url":null,"abstract":"A method, referred to as Composite of Central and Ring Projection (CCRP), is proposed to extract features with rotation invariant property. It reduces the dimensionality of a two-dimensional pattern by performing both central projection (CP) and ring projection (RP). A dissimilarity function is developed and used to distinguish different patterns. This function makes use of both similarity corrections of RP and CP. Information along both circles and polar angles can be retained from the original pattern. Some experiments have been conducted, in which a set of ambiguous printed Chinese characters which are partially damaged or polluted by noise were classified. The experiments have satisfying results.","PeriodicalId":424264,"journal":{"name":"2009 International Conference on Wavelet Analysis and Pattern Recognition","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132093227","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}
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