Pub Date : 2009-07-12DOI: 10.1109/ICWAPR.2009.5207450
Zhong Zhang, Yasudake Aoki, H. Toda, T. Miyake, T. Imamura
It is well known that in real world source separation, the environment noise removal must be considered with complex reverberating sound, and various noises. In this study, in order to improve the voice recognition accuracy in real world source separation, a new method that uses Independent Component Analysis (ICA) in the time-frequency domain using the variable density complex discrete wavelet transform (VD-CDWT) and the subspace method has been proposed. Through comparison of the results according to signal noise ratio (SNR), the effectiveness of the proposed method is confirmed.
{"title":"Real world source separation by combining ICA and VD-CDWT in time-frequency domain","authors":"Zhong Zhang, Yasudake Aoki, H. Toda, T. Miyake, T. Imamura","doi":"10.1109/ICWAPR.2009.5207450","DOIUrl":"https://doi.org/10.1109/ICWAPR.2009.5207450","url":null,"abstract":"It is well known that in real world source separation, the environment noise removal must be considered with complex reverberating sound, and various noises. In this study, in order to improve the voice recognition accuracy in real world source separation, a new method that uses Independent Component Analysis (ICA) in the time-frequency domain using the variable density complex discrete wavelet transform (VD-CDWT) and the subspace method has been proposed. Through comparison of the results according to signal noise ratio (SNR), the effectiveness of the proposed method is confirmed.","PeriodicalId":424264,"journal":{"name":"2009 International Conference on Wavelet Analysis and Pattern Recognition","volume":"162 1 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":"116636164","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.2009.5207410
Xiao-Zhao Liu, Bin Fang, Zhaowei Shang
Hidden Markov tree (HMT) is a tree-structure statistical model, which is used to capture the statistical structure information of smooth and singular regions. It works by modeling the relationship between the wavelet coefficients interscales. For the discrete wavelet transform (DWT) has its own drawbacks inherently, such as shift variance, lack of directionality, etc. The traditional HMT model based on DWT often leads to an unideal segmentation result. Because of the near shift-variance and good directional-selectivity of complex wavelet transforms, here the authors proposed a complex wavelet domain HMT model (C-HMT) to improve the accuracy of multiscale classification results. To get an accurate final segmentation, labeling tree model was used to fuse the interscale classification results. In the experiment, the classification and segmentation results of the proposed method are found to be better than the traditional wavelet-based models.
{"title":"Texture image segmentation using complex wavelet transform and Hidden Markov models","authors":"Xiao-Zhao Liu, Bin Fang, Zhaowei Shang","doi":"10.1109/ICWAPR.2009.5207410","DOIUrl":"https://doi.org/10.1109/ICWAPR.2009.5207410","url":null,"abstract":"Hidden Markov tree (HMT) is a tree-structure statistical model, which is used to capture the statistical structure information of smooth and singular regions. It works by modeling the relationship between the wavelet coefficients interscales. For the discrete wavelet transform (DWT) has its own drawbacks inherently, such as shift variance, lack of directionality, etc. The traditional HMT model based on DWT often leads to an unideal segmentation result. Because of the near shift-variance and good directional-selectivity of complex wavelet transforms, here the authors proposed a complex wavelet domain HMT model (C-HMT) to improve the accuracy of multiscale classification results. To get an accurate final segmentation, labeling tree model was used to fuse the interscale classification results. In the experiment, the classification and segmentation results of the proposed method are found to be better than the traditional wavelet-based models.","PeriodicalId":424264,"journal":{"name":"2009 International Conference on Wavelet Analysis and Pattern Recognition","volume":"23 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":"117181931","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.2009.5207448
Zheng-Wei Shen, F. Liao
The fusing of high-spectral/low-spatial resolution multi-spectral and low-spectral/high-spatial resolution panchromatic satellite images is a very useful technique in various applications of remote sensing. HIS (Intensity-Hue-Saturation) transformation is one of the most commonly used method which fusing those two kinds of images, however, the traditional IHS transformation method faces a severe problem namely color distortion. In this paper, we first review several improved IHS transformation image fusion algorithm, and then proposes a new IHS fusion method based on region standard deviation, which fuses the low-spectral/high-spatial resolution images and the Intensity component of the high-spectral/low-spatial resolution multi-spectral image based on region standard deviation. Further, we improve this image fusion rule in wavelet field. The experiments show that this new proposed image fusion method can effectively provide richer information in the spatial and spectral domains simultaneously.
{"title":"Fusion of remote sensing images based on region standard deviation of wavelet","authors":"Zheng-Wei Shen, F. Liao","doi":"10.1109/ICWAPR.2009.5207448","DOIUrl":"https://doi.org/10.1109/ICWAPR.2009.5207448","url":null,"abstract":"The fusing of high-spectral/low-spatial resolution multi-spectral and low-spectral/high-spatial resolution panchromatic satellite images is a very useful technique in various applications of remote sensing. HIS (Intensity-Hue-Saturation) transformation is one of the most commonly used method which fusing those two kinds of images, however, the traditional IHS transformation method faces a severe problem namely color distortion. In this paper, we first review several improved IHS transformation image fusion algorithm, and then proposes a new IHS fusion method based on region standard deviation, which fuses the low-spectral/high-spatial resolution images and the Intensity component of the high-spectral/low-spatial resolution multi-spectral image based on region standard deviation. Further, we improve this image fusion rule in wavelet field. The experiments show that this new proposed image fusion method can effectively provide richer information in the spatial and spectral domains simultaneously.","PeriodicalId":424264,"journal":{"name":"2009 International Conference on Wavelet Analysis and Pattern Recognition","volume":"1 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":"125498980","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.2009.5207435
Jian Liang, Bao-chang Pan, Yong-hui Huang, Xiao-yan Fan, Jian-Hui Tan
As the dynamic range of the targets in X-ray images of feet is wide, and the overlapping interval of the background gray value and the target is large, this article comes up with a bone-image segmentation method in X-ray pictures of feet. First of all, composite enhancement will be applied, then the distribution features of target will be exploited to carry out a dynamic partition, finally density function will be incorporated to make the segmentation. The experiment has proved that this method can effectively separate the bone image of feet.
{"title":"Image segmentation of bone in X-ray pictures of feet","authors":"Jian Liang, Bao-chang Pan, Yong-hui Huang, Xiao-yan Fan, Jian-Hui Tan","doi":"10.1109/ICWAPR.2009.5207435","DOIUrl":"https://doi.org/10.1109/ICWAPR.2009.5207435","url":null,"abstract":"As the dynamic range of the targets in X-ray images of feet is wide, and the overlapping interval of the background gray value and the target is large, this article comes up with a bone-image segmentation method in X-ray pictures of feet. First of all, composite enhancement will be applied, then the distribution features of target will be exploited to carry out a dynamic partition, finally density function will be incorporated to make the segmentation. The experiment has proved that this method can effectively separate the bone image of feet.","PeriodicalId":424264,"journal":{"name":"2009 International Conference on Wavelet Analysis and Pattern Recognition","volume":"478 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":"122476696","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.2009.5207470
Junying Gan, Mengfei Liu
In this paper, we use edge information for face recognition. In order to get more details but less noise of an image, we decomposed the image with wavelet packets before the process of edge detection. After the edge detection, a logical data which only contains elements of 0 or 1 was introduced to the Hopfield neural network. The proposed algorithm is tested on ORL face database and the result is found to be perfect.
{"title":"Face recognition using wavelet packets decomposition and Hopfield neural network","authors":"Junying Gan, Mengfei Liu","doi":"10.1109/ICWAPR.2009.5207470","DOIUrl":"https://doi.org/10.1109/ICWAPR.2009.5207470","url":null,"abstract":"In this paper, we use edge information for face recognition. In order to get more details but less noise of an image, we decomposed the image with wavelet packets before the process of edge detection. After the edge detection, a logical data which only contains elements of 0 or 1 was introduced to the Hopfield neural network. The proposed algorithm is tested on ORL face database and the result is found to be perfect.","PeriodicalId":424264,"journal":{"name":"2009 International Conference on Wavelet Analysis and Pattern Recognition","volume":"15 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":"122659991","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.2009.5207409
Weibin Yang, Bin Fang, Yuanyan Tang, Zhaowei Shang, Donghui Li
A novel first-detect-then-identify approach with SIFT features and discrete wavelet transform for tracking object is proposed in real surveillance scenarios. For accurate and fast moving object detection, discrete wavelet transform is adopted to eliminate the noises of the frames which may cause detection errors, and then objects are detected by applying the inter-frame difference method on the low frequency parts of two consecutive frames, and then SIFT feature is used for object representation and identification due to its invariant properties. Experimental results demonstrate that the proposed strategy improves the tracking performance by comparing with the classical mean shift method, and it is also shown that the proposed algorithm can be also applied in multiple objects tracking in real scenarios.
{"title":"Sift features based object tracking with discrete wavelet transform","authors":"Weibin Yang, Bin Fang, Yuanyan Tang, Zhaowei Shang, Donghui Li","doi":"10.1109/ICWAPR.2009.5207409","DOIUrl":"https://doi.org/10.1109/ICWAPR.2009.5207409","url":null,"abstract":"A novel first-detect-then-identify approach with SIFT features and discrete wavelet transform for tracking object is proposed in real surveillance scenarios. For accurate and fast moving object detection, discrete wavelet transform is adopted to eliminate the noises of the frames which may cause detection errors, and then objects are detected by applying the inter-frame difference method on the low frequency parts of two consecutive frames, and then SIFT feature is used for object representation and identification due to its invariant properties. Experimental results demonstrate that the proposed strategy improves the tracking performance by comparing with the classical mean shift method, and it is also shown that the proposed algorithm can be also applied in multiple objects tracking in real scenarios.","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":"130314379","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.2009.5207422
Lan-yan Xue, Jianjia Pan
Aiming for the problem of discarding some important details of high-frequency sub-image when detecting the edge based on wavelet transform, and the effect of edge extracting is poor because of the noise influence. This paper proposed a new fusion algorithm based on wavelet transform and canny operator to detect image edges. In the wavelet domain, the low-frequency sub-image edges are detected by canny operator, while the high-frequency sub-image are detected by solving the maximum points of local wavelet coefficient model to restore edges after reducing the noise by wavelet. Then, both sub-images edges are fused according to certain rules. Experiment results show the proposed method can detect image edges not only remove the noise effectively but also enhance the edges and locate edges accurately.
{"title":"Edge detection combining wavelet transform and canny operator based on fusion rules","authors":"Lan-yan Xue, Jianjia Pan","doi":"10.1109/ICWAPR.2009.5207422","DOIUrl":"https://doi.org/10.1109/ICWAPR.2009.5207422","url":null,"abstract":"Aiming for the problem of discarding some important details of high-frequency sub-image when detecting the edge based on wavelet transform, and the effect of edge extracting is poor because of the noise influence. This paper proposed a new fusion algorithm based on wavelet transform and canny operator to detect image edges. In the wavelet domain, the low-frequency sub-image edges are detected by canny operator, while the high-frequency sub-image are detected by solving the maximum points of local wavelet coefficient model to restore edges after reducing the noise by wavelet. Then, both sub-images edges are fused according to certain rules. Experiment results show the proposed method can detect image edges not only remove the noise effectively but also enhance the edges and locate edges accurately.","PeriodicalId":424264,"journal":{"name":"2009 International Conference on Wavelet Analysis and Pattern Recognition","volume":"20 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":"124186002","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.2009.5207471
G. Wu, Zheng-xing Cheng
The 3-band cardinal orthogonal scaling function with compact support is of interest in several applications such as sampling theory, signal processing, computer graphics. In this paper, We generalize some results of the cardinal orthogonal scaling function from the 2-band case to the 3-band case. We give the characterization. Also,we give some examples to prove our theory.
{"title":"Classifying 3-band cardinal orthogonal scaling function","authors":"G. Wu, Zheng-xing Cheng","doi":"10.1109/ICWAPR.2009.5207471","DOIUrl":"https://doi.org/10.1109/ICWAPR.2009.5207471","url":null,"abstract":"The 3-band cardinal orthogonal scaling function with compact support is of interest in several applications such as sampling theory, signal processing, computer graphics. In this paper, We generalize some results of the cardinal orthogonal scaling function from the 2-band case to the 3-band case. We give the characterization. Also,we give some examples to prove our theory.","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":"131478380","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.2009.5207460
Jian-Xia Wang, Xiao-Jun Wang
This paper briefly introduces the recognition process on palm shape and mainly introduces feature parameter extraction process on palm shape. By statistic experiment eight feature parameters are extracted from many parameters of palm shape. They are the length of pinkie, the length of ring finger, the length of middle finger, the length of forefinger, the length of thumb, the width of ring finger, the width of middle finger, the width of palm. Experiment indicates that using these eight feature parameters to identify palm can reach high veracity and rapidity.
{"title":"The research on palm shape feature parameters extraction","authors":"Jian-Xia Wang, Xiao-Jun Wang","doi":"10.1109/ICWAPR.2009.5207460","DOIUrl":"https://doi.org/10.1109/ICWAPR.2009.5207460","url":null,"abstract":"This paper briefly introduces the recognition process on palm shape and mainly introduces feature parameter extraction process on palm shape. By statistic experiment eight feature parameters are extracted from many parameters of palm shape. They are the length of pinkie, the length of ring finger, the length of middle finger, the length of forefinger, the length of thumb, the width of ring finger, the width of middle finger, the width of palm. Experiment indicates that using these eight feature parameters to identify palm can reach high veracity and rapidity.","PeriodicalId":424264,"journal":{"name":"2009 International Conference on Wavelet Analysis and Pattern Recognition","volume":"42 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":"133614079","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.2009.5207428
Xiangfan He
A new image segmentation algorithm based on the Kring interpolation algorithm is proposed to segment the CT images of the rock into pore systems and the mineral grain systems. With the method, the CT image of the rock is segmented without isolated island by analyzing the correlation between the pixels of the Image. The 3D microstructure of the pore system and the mineral grain system in the rock sample are reconstructed basing on the segmented images with matching cube algorithm, in which the volume element is reconstructed with 3-dimensional interpolation method and the equipotential surface is analyzed by triangular facet method. The reconstructed microstructures are verified by slice images in two other orthometric directions and the results prove that both the distribution and the shape characteristic of the pores and mineral grains in the reconstructed microstructure are in coincidence with that in the actual CT image with statistical significance.
{"title":"Reconstruction of 3D microstructure of the rock sample basing on the CT images","authors":"Xiangfan He","doi":"10.1109/ICWAPR.2009.5207428","DOIUrl":"https://doi.org/10.1109/ICWAPR.2009.5207428","url":null,"abstract":"A new image segmentation algorithm based on the Kring interpolation algorithm is proposed to segment the CT images of the rock into pore systems and the mineral grain systems. With the method, the CT image of the rock is segmented without isolated island by analyzing the correlation between the pixels of the Image. The 3D microstructure of the pore system and the mineral grain system in the rock sample are reconstructed basing on the segmented images with matching cube algorithm, in which the volume element is reconstructed with 3-dimensional interpolation method and the equipotential surface is analyzed by triangular facet method. The reconstructed microstructures are verified by slice images in two other orthometric directions and the results prove that both the distribution and the shape characteristic of the pores and mineral grains in the reconstructed microstructure are in coincidence with that in the actual CT image with statistical significance.","PeriodicalId":424264,"journal":{"name":"2009 International Conference on Wavelet Analysis and Pattern Recognition","volume":"26 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":"133199735","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：