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2010 International Conference on Wavelet Analysis and Pattern Recognition最新文献

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Kernel subspace LDA with convolution kernel function for face recognition 基于卷积核函数的核子空间LDA人脸识别
2010 International Conference on Wavelet Analysis and Pattern Recognition Pub Date : 2010-07-11 DOI: 10.1109/ICWAPR.2010.5576309
Wensheng Chen, P. Yuen, Zhen Ji
{"title":"Kernel subspace LDA with convolution kernel function for face recognition","authors":"Wensheng Chen, P. Yuen, Zhen Ji","doi":"10.1109/ICWAPR.2010.5576309","DOIUrl":"https://doi.org/10.1109/ICWAPR.2010.5576309","url":null,"abstract":"It is well-known that most wavelet functions are un-symmetrical and thus fail to satisfy Fourier criterion. These kinds of wavelets cannot be utilized to construct Mercer kernel directly. Based on convolution technique, this paper proposes a novel framework on Mercer kernel construction. The proposed methodology indicates that any of wavelets can generate a wavelet-like kernel basis function, which has zero vanishing moment. An example on convolution Mercer kernel construction is given by using Haar wavelet. The self-constructed Haar wavelet convolution kernel (HWCK) function is then applied to kernel subspace linear discriminant analysis (SLDA) approach for face classification. The CMU PIE human face dataset is selected for evaluation. Comparing with the RBF kernel based SLDA method and existing LDA-based kernel methods such as KDDA and GDA, the proposed Haar wavelet convolution kernel based method gives superior results.","PeriodicalId":219884,"journal":{"name":"2010 International Conference on Wavelet Analysis and Pattern Recognition","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133626202","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: 3
Sparsity-regularized support vector machine with stationary mixing input sequence 具有平稳混合输入序列的稀疏正则化支持向量机
2010 International Conference on Wavelet Analysis and Pattern Recognition Pub Date : 2010-07-11 DOI: 10.1109/ICWAPR.2010.5576330
Yi Ding, Yi Tang
{"title":"Sparsity-regularized support vector machine with stationary mixing input sequence","authors":"Yi Ding, Yi Tang","doi":"10.1109/ICWAPR.2010.5576330","DOIUrl":"https://doi.org/10.1109/ICWAPR.2010.5576330","url":null,"abstract":"It has been shown that a sparse target can be well learned by the l1-regularized learning methods when samples are independent and identically distributed (i.i.d.). In this paper we go far beyond this classical framework by bounding the generalization errors and excess risks of l1-regularized support vector machine(l1-svm) for stationary β-mixing observations. Utilizing a technique introduced by [1] that constructs a sequence of independent blocks close in distribution to the original samples, such bounds are developed by Rademacher average technique. The results replied partly an open question in [2] of wether Rademacher average technique can be extended to deal with dependent status.","PeriodicalId":219884,"journal":{"name":"2010 International Conference on Wavelet Analysis and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130516662","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 recognition under varying illumination based on Dual-tree complex wavelet transform 基于双树复小波变换的变光照下人脸识别
2010 International Conference on Wavelet Analysis and Pattern Recognition Pub Date : 2010-07-11 DOI: 10.1109/ICWAPR.2010.5576374
Lin Jiang
{"title":"Face recognition under varying illumination based on Dual-tree complex wavelet transform","authors":"Lin Jiang","doi":"10.1109/ICWAPR.2010.5576374","DOIUrl":"https://doi.org/10.1109/ICWAPR.2010.5576374","url":null,"abstract":"As we all know, face recognition usually cares about the main features of a face, such as the shapes and relative positions of the main facial feature, and ignores the illumination changes on the face. Accordingly, A novel method to extract illumination invariant features using the Dual-tree complex wavelet transform for face recognition under varying lighting conditions, the proposed method estimates illumination by minimizing the difference between the normalized illumination and estimated original illumination in the logarithm domain. To evaluate effectiveness of our method, three illumination methods (MSR, SQI and LTV) were implemented using Yale B database. It shows that the performance of the proposed method is better than other methods.","PeriodicalId":219884,"journal":{"name":"2010 International Conference on Wavelet Analysis and Pattern Recognition","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116693669","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
SMT solder paste deposit inspection based on 3D PMP and 2D image features fusion 基于三维PMP和二维图像特征融合的SMT焊膏沉积检测
2010 International Conference on Wavelet Analysis and Pattern Recognition Pub Date : 2010-07-11 DOI: 10.1109/ICWAPR.2010.5576321
Bing Luo, Liyun Zhang
{"title":"SMT solder paste deposit inspection based on 3D PMP and 2D image features fusion","authors":"Bing Luo, Liyun Zhang","doi":"10.1109/ICWAPR.2010.5576321","DOIUrl":"https://doi.org/10.1109/ICWAPR.2010.5576321","url":null,"abstract":"Defects inspection for solder paste deposit in PCB SMT assembly can depress final fault rate and save rework cost. The solder paste deposit inspection needs both 2D analysis and 3D volume measurement. Conventional methods suffered slow speed and low reliability. This paper proposed inspecting by grating projection phase shifting profilometry 3D scale fusion with 2D image features, which help phase measuring and unwrapping as well as shadows processing. Experimental results show that the approach is fast, accurate and reliable.","PeriodicalId":219884,"journal":{"name":"2010 International Conference on Wavelet Analysis and Pattern Recognition","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132818815","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: 10
Characterizations of tight frame wavelets with special dilation matrices 具有特殊膨胀矩阵的紧框架小波的性质
2010 International Conference on Wavelet Analysis and Pattern Recognition Pub Date : 2010-07-11 DOI: 10.1109/ICWAPR.2010.5576366
Fengjuan Zhu, Yong-dong Huang
{"title":"Characterizations of tight frame wavelets with special dilation matrices","authors":"Fengjuan Zhu, Yong-dong Huang","doi":"10.1109/ICWAPR.2010.5576366","DOIUrl":"https://doi.org/10.1109/ICWAPR.2010.5576366","url":null,"abstract":"We study all generalized low-pass filters and tight frame wavelet with special dilation matrix M (M-TFW), where M satisfy Md = 2Id and generates the checkerboard lattice. Firstly, we study the pseudo-scaling function, generalized low-pass filters and multiresolution analysis tight frame wavelets with dilation matrix M(MRA M-TFW), and give some important characterization aboutthem. Then, we characterize all M-TFW by showing that they correspond precisely to those for which the dimension function is non-negative integer-valued","PeriodicalId":219884,"journal":{"name":"2010 International Conference on Wavelet Analysis and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128624060","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
An algorithm based on rough-set theory for color image segmentation 基于粗糙集理论的彩色图像分割算法
2010 International Conference on Wavelet Analysis and Pattern Recognition Pub Date : 2010-07-11 DOI: 10.1109/ICWAPR.2010.5576457
Ming-xin Zhang, Cai_Yun Zhao, Zhao-Wei Shang, Hua Li, Jinlong Zheng
{"title":"An algorithm based on rough-set theory for color image segmentation","authors":"Ming-xin Zhang, Cai_Yun Zhao, Zhao-Wei Shang, Hua Li, Jinlong Zheng","doi":"10.1109/ICWAPR.2010.5576457","DOIUrl":"https://doi.org/10.1109/ICWAPR.2010.5576457","url":null,"abstract":"In view of the over- and under-segmentation problems existed in the conventional image segmentation based on rough-set theory, an novel color image segmentation approach based on Rough-Set theory is presented in this paper. Firstly, the new distance has been defined by using the vector angle and Euclidean distance. And then according to the new distance, the space binary matrixes that represent the similar color sphere and the Histon of each color component are calculated. Finally, the color image segmentation is implemented by selection of threshold values and region merging through introducing a histogram based on roughness. The analysis of experimental results show that the proposed approach yields better segmentation which is more intuitive to human vision compare with the conventional image segmentation based on rough-set theory.","PeriodicalId":219884,"journal":{"name":"2010 International Conference on Wavelet Analysis and Pattern Recognition","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131887346","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
Necessary condition for the multiwavelet frames in two dimension 二维多小波帧的必要条件
2010 International Conference on Wavelet Analysis and Pattern Recognition Pub Date : 2010-07-11 DOI: 10.1109/ICWAPR.2010.5576382
G. Wu, Rui Zhang
{"title":"Necessary condition for the multiwavelet frames in two dimension","authors":"G. Wu, Rui Zhang","doi":"10.1109/ICWAPR.2010.5576382","DOIUrl":"https://doi.org/10.1109/ICWAPR.2010.5576382","url":null,"abstract":"In this note, a necessary condition of multiwavelet frame in L2(R2) is given. Our result generalizes the result of existing univariate and single generator to the cases of 2-dimension with an arbitrary expansive matrix dilation and several generators.","PeriodicalId":219884,"journal":{"name":"2010 International Conference on Wavelet Analysis and Pattern Recognition","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115543925","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
Random sampling LDA incorporating feature selection for face recognition 结合特征选择的随机抽样LDA人脸识别
2010 International Conference on Wavelet Analysis and Pattern Recognition Pub Date : 2010-07-11 DOI: 10.1109/ICWAPR.2010.5576317
Ming Yang, Jianwu Wan, Gen-Lin Ji
{"title":"Random sampling LDA incorporating feature selection for face recognition","authors":"Ming Yang, Jianwu Wan, Gen-Lin Ji","doi":"10.1109/ICWAPR.2010.5576317","DOIUrl":"https://doi.org/10.1109/ICWAPR.2010.5576317","url":null,"abstract":"Classical Linear Discriminant Analysis(LDA) is usually suffers from the small sample size(SSS) problem when dealing with the high dimensional face data. Many methods have been proposed for solving this problem such as Fisherface and Null Space LDA(N-LDA), but these methods are overfitted to the training set and inevitably lose some useful discriminative information in many cases. To effectively utilize nearly all useful discriminative information, a not completely random sampling framework for the integration of multiple features is developed. However, this method has the following main disadvantage: By directly employing feature extraction, the newly constructed variables may contain lots of information originated from those redundant features in the original space. So, in this paper, we introduce a new random sampling LDA by incorporating feature selection for face recognition, that is, some redundant features are removed using the given feature selection methods at first, and then PCA is employed, finally we use random sampling to generate multiple feature subsets. Along this, corresponding weak LDA classifiers are naturally generated and an integrated classifier is developed using a fusion rule. Experiments on 4 face datasets(AR, ORL, Yale, YaleB) show the effectiveness of our algorithm.","PeriodicalId":219884,"journal":{"name":"2010 International Conference on Wavelet Analysis and Pattern Recognition","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127336576","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: 10
Applying an improved neural network to impulse noise removal 应用改进的神经网络去除脉冲噪声
2010 International Conference on Wavelet Analysis and Pattern Recognition Pub Date : 2010-07-11 DOI: 10.1109/ICWAPR.2010.5576334
Chao Deng, Hong-Min Liu, Zhi-Heng Wang
{"title":"Applying an improved neural network to impulse noise removal","authors":"Chao Deng, Hong-Min Liu, Zhi-Heng Wang","doi":"10.1109/ICWAPR.2010.5576334","DOIUrl":"https://doi.org/10.1109/ICWAPR.2010.5576334","url":null,"abstract":"A new noise removal algorithm based on improved neural network, is applied to remove the impulse noise of the digital images. First of all, an improved neural network is used to detect the noise-pixels and distinguish it from noise-free pixels efficiently; Second, the noise-pixels are replaced further by the suitable pixel which has the most local similarity; Finally, the output is the combination of the noise-free pixels and the suitable pixel. The proposed algorithm is capable of removing the impulse noise effectively. At the same time it can keep more image details well. Experiential results show that the new algorithm is more improved than the conventional filters.","PeriodicalId":219884,"journal":{"name":"2010 International Conference on Wavelet Analysis and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128194801","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: 3
Construction of symmetric orthogonal multiwavelets with multiplicity 2 重数为2的对称正交多小波的构造
2010 International Conference on Wavelet Analysis and Pattern Recognition Pub Date : 2010-07-11 DOI: 10.1109/ICWAPR.2010.5576370
Chenglin Liu, Xiaoxia Feng, Zhongpeng Yang
{"title":"Construction of symmetric orthogonal multiwavelets with multiplicity 2","authors":"Chenglin Liu, Xiaoxia Feng, Zhongpeng Yang","doi":"10.1109/ICWAPR.2010.5576370","DOIUrl":"https://doi.org/10.1109/ICWAPR.2010.5576370","url":null,"abstract":"For given symmetric/antisymmetric orthogonal mul-tiscaling functions with miltiplicity 2 and support [0,2L], we obtain a general method to construct the corresponding multiwavelets with the similar properties by applying the paraunitary extension of matrix and parameterization of paraunitary matrixfrom, and the symmetric orthogonal multiwavelet with support [0, 2] derived by Jiang can be easily recovered by our method.","PeriodicalId":219884,"journal":{"name":"2010 International Conference on Wavelet Analysis and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125808032","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
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