Pub Date : 2009-07-12DOI: 10.1109/ICWAPR.2009.5207456
Chunyan Li, Bin Fang, Yi Wang, Guang-Zhou Lu, Ji-Ye Qian, Lin Chen
The appearance of cast cells in urine sediment is an essential sign of serious renal or urinary tract diseases. However, due to uneven illumination, low contrast against the background and complicated components of the microscopic urine sediment images, detection and recognition of cast cells in former study can not be considered sufficient. In this paper, an efficient approach for casts detecting and recognition in urine sediment images is proposed. It consists of three stages: Firstly, 4-direction variance mapping image is acquired from gray scale image. Secondly, we obtain binary image by applying an improved adaptive bi-threshold segmentation algorithm to the above mapping image. In the last stage, five texture and shape characteristics of casts are extracted from both gray scale image and binary image. Based on these characteristics, we develop an decision-tree classifier to distinguish casts from other particles in the image. Experimental results show that our method produces satisfactory segmentation, achieves an easy-implemented, time-saving classifier and has improved recognition performance.
{"title":"Automatic detecting and recognition of casts in urine sediment images","authors":"Chunyan Li, Bin Fang, Yi Wang, Guang-Zhou Lu, Ji-Ye Qian, Lin Chen","doi":"10.1109/ICWAPR.2009.5207456","DOIUrl":"https://doi.org/10.1109/ICWAPR.2009.5207456","url":null,"abstract":"The appearance of cast cells in urine sediment is an essential sign of serious renal or urinary tract diseases. However, due to uneven illumination, low contrast against the background and complicated components of the microscopic urine sediment images, detection and recognition of cast cells in former study can not be considered sufficient. In this paper, an efficient approach for casts detecting and recognition in urine sediment images is proposed. It consists of three stages: Firstly, 4-direction variance mapping image is acquired from gray scale image. Secondly, we obtain binary image by applying an improved adaptive bi-threshold segmentation algorithm to the above mapping image. In the last stage, five texture and shape characteristics of casts are extracted from both gray scale image and binary image. Based on these characteristics, we develop an decision-tree classifier to distinguish casts from other particles in the image. Experimental results show that our method produces satisfactory segmentation, achieves an easy-implemented, time-saving classifier and has improved recognition performance.","PeriodicalId":424264,"journal":{"name":"2009 International Conference on Wavelet Analysis and Pattern Recognition","volume":"87 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":"125427245","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.2009.5207474
Yijun Huang, Weijun Liu
The fundamental matrix is an effective tool to analyze epipolar geometry. An accurate solution for obtaining fundamental matrices is the basic requirement in many applications of computer vision. When noises and outliers exist in the set of initial match points, the estimation of the fundamental matrix becomes to a tough mission owing to the invalidation of normal linear and iterative methods. This paper proposes a novel robust technique for estimating the fundamental matrix by combining bucketing technique and the least trimmed squares(LTS) regression into one intelligent algorithm. The new algorithm solves the problem of even distribution of sample data. Also, it eliminates limitations on the proportion of outliers and the requirement a predefined threshold. Comparing with traditional robust methods, the proposed approach is proved to be accuracy and robust by simulation and real image experiments.
{"title":"Robust estimation for the fundamental matrix based on LTS and bucketing","authors":"Yijun Huang, Weijun Liu","doi":"10.1109/ICWAPR.2009.5207474","DOIUrl":"https://doi.org/10.1109/ICWAPR.2009.5207474","url":null,"abstract":"The fundamental matrix is an effective tool to analyze epipolar geometry. An accurate solution for obtaining fundamental matrices is the basic requirement in many applications of computer vision. When noises and outliers exist in the set of initial match points, the estimation of the fundamental matrix becomes to a tough mission owing to the invalidation of normal linear and iterative methods. This paper proposes a novel robust technique for estimating the fundamental matrix by combining bucketing technique and the least trimmed squares(LTS) regression into one intelligent algorithm. The new algorithm solves the problem of even distribution of sample data. Also, it eliminates limitations on the proportion of outliers and the requirement a predefined threshold. Comparing with traditional robust methods, the proposed approach is proved to be accuracy and robust by simulation and real image experiments.","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":"129722266","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.2009.5207484
Jiang-ping He, Yan Ma
A new algorithm based on windowed Hough Transform is proposed for triangle detection. A sliding window scans the image pixel by pixel and the Hough Transform is computed in the small region. Peaks of the Hough image which correspond to the line segments are then extracted. A triangle is detected when the three lines satisfy the certain conditions. Experimental results show that the arbitrary triangle can be detected and retrieved efficiently.
{"title":"Triangle detection based on windowed Hough Transform","authors":"Jiang-ping He, Yan Ma","doi":"10.1109/ICWAPR.2009.5207484","DOIUrl":"https://doi.org/10.1109/ICWAPR.2009.5207484","url":null,"abstract":"A new algorithm based on windowed Hough Transform is proposed for triangle detection. A sliding window scans the image pixel by pixel and the Hough Transform is computed in the small region. Peaks of the Hough image which correspond to the line segments are then extracted. A triangle is detected when the three lines satisfy the certain conditions. Experimental results show that the arbitrary triangle can be detected and retrieved efficiently.","PeriodicalId":424264,"journal":{"name":"2009 International Conference on Wavelet Analysis and Pattern Recognition","volume":"27 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":"121920879","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.2009.5207452
Yu Song, Wenhong Wang, Fengjuan Guo
Feature extraction and analysis are the foundation of audio classification. At first, audio features are analyzed deeply, including short-time energy, zero-crossing rate, bandwidth, low short-time energy ratio, high zero-crossing rate ratio, and noise rate. Secondly a new audio classification method for news video is proposed based on the decision tree method, and then divides audio information into four classes: silence, pure speech, music, non-pure speech. The experiment results show that the selected features are effective for audio classification in news video, and the classification accuracy is reasonable.
{"title":"Feature extraction and classification for audio information in news video","authors":"Yu Song, Wenhong Wang, Fengjuan Guo","doi":"10.1109/ICWAPR.2009.5207452","DOIUrl":"https://doi.org/10.1109/ICWAPR.2009.5207452","url":null,"abstract":"Feature extraction and analysis are the foundation of audio classification. At first, audio features are analyzed deeply, including short-time energy, zero-crossing rate, bandwidth, low short-time energy ratio, high zero-crossing rate ratio, and noise rate. Secondly a new audio classification method for news video is proposed based on the decision tree method, and then divides audio information into four classes: silence, pure speech, music, non-pure speech. The experiment results show that the selected features are effective for audio classification in news video, and the classification accuracy is reasonable.","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":"132951917","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.2009.5207477
L. Qiao, Wei Guo, WeiTao Yuan, KaiFu Niu, Li-Zhong Peng
In this paper, a renovate texture analysis method is proposed. The BEMD is a locally adaptive method and suitable for the analysis of nonlinear or nonstationary signals. The texture image is decomposed to several 2D-IMFs (two dimentional intrinsic mode functions) by BEMD (Bidimentional Empirical Mode Decomposition). Then quaternion is used to get the quaternionic analytic signals, which is compatible with the associated harmonic transform. Finally, each 2D-IMF's local properties are analyzed by using a new quaternionic representation. As an advanced method for describing the local properties of a 2D-signal, this algorithm has seven characters of each 2D-IMF including instantaneous frequency. The performance of this texture analysis method is demonstrated with both synthetic and natural images.
{"title":"Texture analysis based on Bidimensional Empirical Mode Decomposition and quaternions","authors":"L. Qiao, Wei Guo, WeiTao Yuan, KaiFu Niu, Li-Zhong Peng","doi":"10.1109/ICWAPR.2009.5207477","DOIUrl":"https://doi.org/10.1109/ICWAPR.2009.5207477","url":null,"abstract":"In this paper, a renovate texture analysis method is proposed. The BEMD is a locally adaptive method and suitable for the analysis of nonlinear or nonstationary signals. The texture image is decomposed to several 2D-IMFs (two dimentional intrinsic mode functions) by BEMD (Bidimentional Empirical Mode Decomposition). Then quaternion is used to get the quaternionic analytic signals, which is compatible with the associated harmonic transform. Finally, each 2D-IMF's local properties are analyzed by using a new quaternionic representation. As an advanced method for describing the local properties of a 2D-signal, this algorithm has seven characters of each 2D-IMF including instantaneous frequency. The performance of this texture analysis method is demonstrated with both synthetic and natural images.","PeriodicalId":424264,"journal":{"name":"2009 International Conference on Wavelet Analysis and Pattern Recognition","volume":"217 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":"134500959","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.2009.5207491
Wenping Liu, Gang Yang, Xin-yuan Huang
In order to find desired video clips efficiently, the research on content-based video retrieval techniques has become one of the most prominent research areas. A multiple semantic features based news stories segmentation approach is proposed in this paper. A prototype system with the capability of the news stories segmentation, and browsing & retrieval is developed for testing the proposed approach. In this approach, the video features, (i.e. anchor-person face) and the audio features (i.e. the silence gap and change of speaker) in the news video are detected and used to segment the news stories along with text information (i.e. extracted caption from the news video). The experimental results demonstrate that the proposed approach has higher segmentation precision than that of the caption-based method.
{"title":"Semantic features based news stories segmentation for news retrieval","authors":"Wenping Liu, Gang Yang, Xin-yuan Huang","doi":"10.1109/ICWAPR.2009.5207491","DOIUrl":"https://doi.org/10.1109/ICWAPR.2009.5207491","url":null,"abstract":"In order to find desired video clips efficiently, the research on content-based video retrieval techniques has become one of the most prominent research areas. A multiple semantic features based news stories segmentation approach is proposed in this paper. A prototype system with the capability of the news stories segmentation, and browsing & retrieval is developed for testing the proposed approach. In this approach, the video features, (i.e. anchor-person face) and the audio features (i.e. the silence gap and change of speaker) in the news video are detected and used to segment the news stories along with text information (i.e. extracted caption from the news video). The experimental results demonstrate that the proposed approach has higher segmentation precision than that of the caption-based method.","PeriodicalId":424264,"journal":{"name":"2009 International Conference on Wavelet Analysis and Pattern Recognition","volume":"29 2 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":"133623796","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.2009.5207424
Jun Feng, Xu Gao
As a kind of behavior-based personal identification techniques, automated Chinese handwriting identification becomes a hot topic in pattern recognition and machine learning research area. There are lots of key issues worthy researching. In this paper, the Chinese handwriting identification technology based on texture analysis is discussed. Firstly, a practical Chinese handwriting image samples library CHSL2007 is established for the comparison of exist algorithms and further research. Then the methods of feature extraction based on texture analysis are explored and the pairwise SVM classifier is utilized. The experiment results of texture analysis based on Gabor filter is compared with DB6 wavelet filter and demonstrate that the former is more suitable for handwriting identification on CHSL2007. Finally, the sheet recognition rate is defined and can be arrived at above 99.50% for CHSL2007.
{"title":"Study on Chinese handwriting identification based on texture analysis","authors":"Jun Feng, Xu Gao","doi":"10.1109/ICWAPR.2009.5207424","DOIUrl":"https://doi.org/10.1109/ICWAPR.2009.5207424","url":null,"abstract":"As a kind of behavior-based personal identification techniques, automated Chinese handwriting identification becomes a hot topic in pattern recognition and machine learning research area. There are lots of key issues worthy researching. In this paper, the Chinese handwriting identification technology based on texture analysis is discussed. Firstly, a practical Chinese handwriting image samples library CHSL2007 is established for the comparison of exist algorithms and further research. Then the methods of feature extraction based on texture analysis are explored and the pairwise SVM classifier is utilized. The experiment results of texture analysis based on Gabor filter is compared with DB6 wavelet filter and demonstrate that the former is more suitable for handwriting identification on CHSL2007. Finally, the sheet recognition rate is defined and can be arrived at above 99.50% for CHSL2007.","PeriodicalId":424264,"journal":{"name":"2009 International Conference on Wavelet Analysis and Pattern Recognition","volume":"53 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":"130105367","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.2009.5207454
R. Shalbaf, P. T. Hosseini, M. Analoui
Epilepsy is a disorder of the central nervous system characterized by the loss of consciousness and convulsions. If some early warning signal of an upcoming seizure (diagnosis of preictal period) could be detected, proper treatment could be applied to the patient to help prevent the seizure. In this articles, Detrended Fluctuation Analysis (DFA) has been introduced and used to extract the DFA feature from EEG signal. DFA is a scaling analysis method that provides a simple quantitative parameter to represent the correlation properties of a signal, we come to 100% separation of Normal, Preictal, and Ictal states of the brain.
{"title":"Epilepsy detection using Detrended Fluctuation Analysis","authors":"R. Shalbaf, P. T. Hosseini, M. Analoui","doi":"10.1109/ICWAPR.2009.5207454","DOIUrl":"https://doi.org/10.1109/ICWAPR.2009.5207454","url":null,"abstract":"Epilepsy is a disorder of the central nervous system characterized by the loss of consciousness and convulsions. If some early warning signal of an upcoming seizure (diagnosis of preictal period) could be detected, proper treatment could be applied to the patient to help prevent the seizure. In this articles, Detrended Fluctuation Analysis (DFA) has been introduced and used to extract the DFA feature from EEG signal. DFA is a scaling analysis method that provides a simple quantitative parameter to represent the correlation properties of a signal, we come to 100% separation of Normal, Preictal, and Ictal states of the brain.","PeriodicalId":424264,"journal":{"name":"2009 International Conference on Wavelet Analysis and Pattern Recognition","volume":"185 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":"126023850","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.2009.5207483
Baoqin Wang, Gang Wang, Y. Fu, J. Zhu
In the paper, the definition of the multi-resolution analysis on compact Lie Groups is introduced. And the mallat algorithm for a class of orthogonal wavelet on compact Lie Groups is discussed, the decomposition and reconstruction formula is offered.
{"title":"The Mallat algorithm for a class of orthogonal wavelet on compact Lie Groups","authors":"Baoqin Wang, Gang Wang, Y. Fu, J. Zhu","doi":"10.1109/ICWAPR.2009.5207483","DOIUrl":"https://doi.org/10.1109/ICWAPR.2009.5207483","url":null,"abstract":"In the paper, the definition of the multi-resolution analysis on compact Lie Groups is introduced. And the mallat algorithm for a class of orthogonal wavelet on compact Lie Groups is discussed, the decomposition and reconstruction formula is offered.","PeriodicalId":424264,"journal":{"name":"2009 International Conference on Wavelet Analysis and Pattern Recognition","volume":"71 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":"132648244","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.2009.5207482
Lei Wang, Lei Li, B. Zheng
In this paper, a new cooperative scheme for signal detection is proposed. Unlike previous works in the field, the new scheme does not require the knowledge of the noise statistics and is related to the behavior of the largest and smallest eigenvalue of channel matrices. Simulations show that the new algorithm is effective, outperforming classical energy detected techniques.
{"title":"A new cooperative algorithm for signal detection","authors":"Lei Wang, Lei Li, B. Zheng","doi":"10.1109/ICWAPR.2009.5207482","DOIUrl":"https://doi.org/10.1109/ICWAPR.2009.5207482","url":null,"abstract":"In this paper, a new cooperative scheme for signal detection is proposed. Unlike previous works in the field, the new scheme does not require the knowledge of the noise statistics and is related to the behavior of the largest and smallest eigenvalue of channel matrices. Simulations show that the new algorithm is effective, outperforming classical energy detected techniques.","PeriodicalId":424264,"journal":{"name":"2009 International Conference on Wavelet Analysis and Pattern Recognition","volume":"116 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":"114413271","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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