Pub Date : 2004-01-01DOI: 10.1109/ICPR.2004.1334092
H. Kjellström
{"title":"Detecting Human Motion with Support Vector Machines","authors":"H. Kjellström","doi":"10.1109/ICPR.2004.1334092","DOIUrl":"https://doi.org/10.1109/ICPR.2004.1334092","url":null,"abstract":"","PeriodicalId":74516,"journal":{"name":"Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition","volume":"234 1","pages":"188-191"},"PeriodicalIF":0.0,"publicationDate":"2004-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85824202","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 : 2004-01-01DOI: 10.1109/ICPR.2004.1334351
N. Ezaki, M. Bulacu, Lambert Schomaker
{"title":"Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004","authors":"N. Ezaki, M. Bulacu, Lambert Schomaker","doi":"10.1109/ICPR.2004.1334351","DOIUrl":"https://doi.org/10.1109/ICPR.2004.1334351","url":null,"abstract":"","PeriodicalId":74516,"journal":{"name":"Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition","volume":"26 1","pages":"683-686"},"PeriodicalIF":0.0,"publicationDate":"2004-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84049599","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 : 2004-01-01DOI: 10.1109/ICPR.2004.1334231
Jian Cheng, Qingshan Liu, Hanqing Lu, Yenwei Chen
{"title":"Texture Classification Using Kernel Independent Component Analysi","authors":"Jian Cheng, Qingshan Liu, Hanqing Lu, Yenwei Chen","doi":"10.1109/ICPR.2004.1334231","DOIUrl":"https://doi.org/10.1109/ICPR.2004.1334231","url":null,"abstract":"","PeriodicalId":74516,"journal":{"name":"Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition","volume":"2 1","pages":"620-623"},"PeriodicalIF":0.0,"publicationDate":"2004-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87616429","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 : 2004-01-01DOI: 10.1109/ICPR.2004.1334130
Y. Zhou, C. Tan
{"title":"Coordinate Systems Reconstruction for Graphical Documents by Hough-feature Clustering and Geometric Analysis","authors":"Y. Zhou, C. Tan","doi":"10.1109/ICPR.2004.1334130","DOIUrl":"https://doi.org/10.1109/ICPR.2004.1334130","url":null,"abstract":"","PeriodicalId":74516,"journal":{"name":"Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition","volume":"49 1","pages":"376-379"},"PeriodicalIF":0.0,"publicationDate":"2004-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86074719","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 : 2004-01-01DOI: 10.1109/ICPR.2004.1334150
H. Eng, Junxian Wang, A. H. Kam, W. Yau
{"title":"A Bayesian Framework for Robust Human Detection and Occlusion Handling using Human Shape Model","authors":"H. Eng, Junxian Wang, A. H. Kam, W. Yau","doi":"10.1109/ICPR.2004.1334150","DOIUrl":"https://doi.org/10.1109/ICPR.2004.1334150","url":null,"abstract":"","PeriodicalId":74516,"journal":{"name":"Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition","volume":"53 1","pages":"257-260"},"PeriodicalIF":0.0,"publicationDate":"2004-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84585806","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 : 2004-01-01DOI: 10.1109/ICPR.2004.1334022
Pär Hammarstedt, Fredrik Kahl, A. Heyden
In this paper a method for obtaining affine structure from an image sequence taken by a translating camera with varying and unknown focal length is presented. A general geometric constraint, expressed using the camera matrices, is derived and this constraint is used in a least squares solution of the problem. The proposed algorithm extends the previous result of affine structure recovery from an image sequence with a translating camera with constant intrinsic parameters to the case with a camera with varying and unknown focal length and otherwise known intrinsic parameters. The noise sensitivity of the method is evaluated in simulated experiments.
{"title":"Affine Structure from Translational Motion with Varying and Unknown Focal Length","authors":"Pär Hammarstedt, Fredrik Kahl, A. Heyden","doi":"10.1109/ICPR.2004.1334022","DOIUrl":"https://doi.org/10.1109/ICPR.2004.1334022","url":null,"abstract":"In this paper a method for obtaining affine structure from an image sequence taken by a translating camera with varying and unknown focal length is presented. A general geometric constraint, expressed using the camera matrices, is derived and this constraint is used in a least squares solution of the problem. The proposed algorithm extends the previous result of affine structure recovery from an image sequence with a translating camera with constant intrinsic parameters to the case with a camera with varying and unknown focal length and otherwise known intrinsic parameters. The noise sensitivity of the method is evaluated in simulated experiments.","PeriodicalId":74516,"journal":{"name":"Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition","volume":"30 1","pages":"120-123"},"PeriodicalIF":0.0,"publicationDate":"2004-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90539944","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 : 2004-01-01DOI: 10.1109/ICPR.2004.1334613
K. Wong, Chi Lap Yip, Ping Wah Li, W. W. Tsang
A time-honored way of finding, or fixing, the center of a tropical cyclone (TC) is to overlay templates of spirals onto a printout of radar or satellite image. Modern methods, however, mostly focus on wind field analysis, or other motion vector techniques. These techniques cannot be applied effectively if the image is sampled infrequently, or when the TC moves fast. In this paper, we present a TC eye fix method that uses automatically generated templates to match spiral rainbands of TCs. Temporal information can be utilized optionally in the method to improve results. The method gives an average error of about 0.16 degrees in latitude/longitude on the Mercator projected map in our test radar image sequences. This is comparable to the relative error of about 0.3 degrees given by different TC warning centers.
{"title":"Automatic Template Matching Method for Tropical Cyclone Eye Fix","authors":"K. Wong, Chi Lap Yip, Ping Wah Li, W. W. Tsang","doi":"10.1109/ICPR.2004.1334613","DOIUrl":"https://doi.org/10.1109/ICPR.2004.1334613","url":null,"abstract":"A time-honored way of finding, or fixing, the center of a tropical cyclone (TC) is to overlay templates of spirals onto a printout of radar or satellite image. Modern methods, however, mostly focus on wind field analysis, or other motion vector techniques. These techniques cannot be applied effectively if the image is sampled infrequently, or when the TC moves fast. In this paper, we present a TC eye fix method that uses automatically generated templates to match spiral rainbands of TCs. Temporal information can be utilized optionally in the method to improve results. The method gives an average error of about 0.16 degrees in latitude/longitude on the Mercator projected map in our test radar image sequences. This is comparable to the relative error of about 0.3 degrees given by different TC warning centers.","PeriodicalId":74516,"journal":{"name":"Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition","volume":"40 1","pages":"650-653"},"PeriodicalIF":0.0,"publicationDate":"2004-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82432261","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 : 2004-01-01DOI: 10.1109/ICPR.2004.1334028
D. Vetrov, D. Kropotov
{"title":"Data Dependent Classifier Fusion for Construction of Stable Effective Algorithms","authors":"D. Vetrov, D. Kropotov","doi":"10.1109/ICPR.2004.1334028","DOIUrl":"https://doi.org/10.1109/ICPR.2004.1334028","url":null,"abstract":"","PeriodicalId":74516,"journal":{"name":"Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition","volume":"32 1","pages":"144-147"},"PeriodicalIF":0.0,"publicationDate":"2004-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75339763","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 : 2004-01-01DOI: 10.1109/ICPR.2004.1333796
Jun Li, W. Yau
This paper presents an algorithm to predict fingerprint orientation. The algorithm consists of two models: coarse prediction model and polynomial model. The coarse prediction model, which is based on first order phase portrait, is proposed to estimate the orientation in areas where there is no ridge information in the input image. Polynomial model is used to refine the fingerprint orientation predicted by the coarse model. Experimental results show the effectiveness of the proposed algorithm.
{"title":"Prediction of Fingerprint Orientation","authors":"Jun Li, W. Yau","doi":"10.1109/ICPR.2004.1333796","DOIUrl":"https://doi.org/10.1109/ICPR.2004.1333796","url":null,"abstract":"This paper presents an algorithm to predict fingerprint orientation. The algorithm consists of two models: coarse prediction model and polynomial model. The coarse prediction model, which is based on first order phase portrait, is proposed to estimate the orientation in areas where there is no ridge information in the input image. Polynomial model is used to refine the fingerprint orientation predicted by the coarse model. Experimental results show the effectiveness of the proposed algorithm.","PeriodicalId":74516,"journal":{"name":"Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition","volume":"57 1","pages":"436-439"},"PeriodicalIF":0.0,"publicationDate":"2004-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87182296","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 : 2002-12-10DOI: 10.1109/ICPR.2002.1048270
C. Stefano, A. D. Cioppa, A. Marcelli
We introduce a novel multiple classifier system that incorporates a global optimization technique based on a genetic algorithm for configuring the system. The system adopts the weighted majority vote approach to combine the decision of the experts, and obtains the weights by maximizing the performance of the whole set of experts, rather than that of each of them separately. The system has been tested on a handwritten digit recognition problem, and its performance compared with those exhibited by a system using the weights obtained during the training of each expert separately. The results of a set of experiments conducted on 30,000 digits extracted from the NIST database have shown that the proposed system exhibits better performance than those of the alternative one, and that such an improvement is due to a better estimate of the reliability of the participating classifiers.
{"title":"An Adaptive Weighted Majority Vote Rule for Combining Multiple Classifiers","authors":"C. Stefano, A. D. Cioppa, A. Marcelli","doi":"10.1109/ICPR.2002.1048270","DOIUrl":"https://doi.org/10.1109/ICPR.2002.1048270","url":null,"abstract":"We introduce a novel multiple classifier system that incorporates a global optimization technique based on a genetic algorithm for configuring the system. The system adopts the weighted majority vote approach to combine the decision of the experts, and obtains the weights by maximizing the performance of the whole set of experts, rather than that of each of them separately. The system has been tested on a handwritten digit recognition problem, and its performance compared with those exhibited by a system using the weights obtained during the training of each expert separately. The results of a set of experiments conducted on 30,000 digits extracted from the NIST database have shown that the proposed system exhibits better performance than those of the alternative one, and that such an improvement is due to a better estimate of the reliability of the participating classifiers.","PeriodicalId":74516,"journal":{"name":"Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition","volume":"10 1","pages":"192-195"},"PeriodicalIF":0.0,"publicationDate":"2002-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87554847","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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