Pub Date : 2012-11-01DOI: 10.1007/978-1-4614-7245-2_9
Sheng Chen, Kenji Suzuki
{"title":"Bone suppression in chest radiographs by means of anatomically specific multiple massive-training ANNs","authors":"Sheng Chen, Kenji Suzuki","doi":"10.1007/978-1-4614-7245-2_9","DOIUrl":"https://doi.org/10.1007/978-1-4614-7245-2_9","url":null,"abstract":"","PeriodicalId":331913,"journal":{"name":"Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134178649","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 : 2012-07-15DOI: 10.1109/ICMLC.2012.6359503
Ying Gu, Yanyun Qu, Tian-Zhu Fang, Cuihua Li, Hanzi Wang
In this paper, a novel approach to single image super-resolution based on the multikernel regression is presented. This approach aims to learn the map between the space of high-resolution image patches and the space of blurred high-resolution image patches, which are the interpolation results generated from the corresponding low-resolution images. Kernel regression based super-resolution approaches are promising, but kernel selection is a critical problem. In order to avoid selecting kernels via a large number of cross-verifications, the multikernel regression is applied to learn the map function. This approach is efficient and the experimental results show that it manifests a high-quality performance in comparison with other superresolution methods.
{"title":"Image super-resolution based on multikernel regression","authors":"Ying Gu, Yanyun Qu, Tian-Zhu Fang, Cuihua Li, Hanzi Wang","doi":"10.1109/ICMLC.2012.6359503","DOIUrl":"https://doi.org/10.1109/ICMLC.2012.6359503","url":null,"abstract":"In this paper, a novel approach to single image super-resolution based on the multikernel regression is presented. This approach aims to learn the map between the space of high-resolution image patches and the space of blurred high-resolution image patches, which are the interpolation results generated from the corresponding low-resolution images. Kernel regression based super-resolution approaches are promising, but kernel selection is a critical problem. In order to avoid selecting kernels via a large number of cross-verifications, the multikernel regression is applied to learn the map function. This approach is efficient and the experimental results show that it manifests a high-quality performance in comparison with other superresolution methods.","PeriodicalId":331913,"journal":{"name":"Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131040683","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}
Human hand is composed of structures called carpal bones, metacarpal bones and phalanges (which form the fingers). Typically, fingerprint matching is used for personal authentication, with images & features obtained from the “tip” of the fingers, ie. distal phalanges (sections, digits). In this study, we report fingerprint minutiae matching results, with images obtained from proximal and middle phalanges. Experiments conducted on a medium-size database, collected using a commercial low-cost optical (distal) fingerprint sensor without any modification, show that, in applications where distal phalanx images are not usable (e.g. due to missing digits, low quality finger surface due to manual labor), non-distal phalanges may provide an acceptable biometric verification source.
{"title":"Fingerprint matching utilizing non-distal phalanges","authors":"B. Topcu, M. Kayaoglu, M. Yildirim, U. Uludag","doi":"10.5072/ZENODO.20998","DOIUrl":"https://doi.org/10.5072/ZENODO.20998","url":null,"abstract":"Human hand is composed of structures called carpal bones, metacarpal bones and phalanges (which form the fingers). Typically, fingerprint matching is used for personal authentication, with images & features obtained from the “tip” of the fingers, ie. distal phalanges (sections, digits). In this study, we report fingerprint minutiae matching results, with images obtained from proximal and middle phalanges. Experiments conducted on a medium-size database, collected using a commercial low-cost optical (distal) fingerprint sensor without any modification, show that, in applications where distal phalanx images are not usable (e.g. due to missing digits, low quality finger surface due to manual labor), non-distal phalanges may provide an acceptable biometric verification source.","PeriodicalId":331913,"journal":{"name":"Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114499594","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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