Pub Date : 2018-11-01DOI: 10.1109/IPTA.2018.8608141
Jian Pu, Yingbin Zheng, Hao Ye
Sparse representation and dictionary learning of image patches are well-known methods for single-image super-resolution. However, due to the regularization term of sparse-inducing penalties, the solution is usually biased. In this study, we present a de-biasing framework by adding a de-biasing step after sparse representation. Two de-biasing methods with sign consistency and feature consistency are further proposed under this framework. Using a unified proximal gradient method, we can solve the proposed de-biasing methods efficiently. Experiments on real super-resolution datasets validate the effectiveness and robustness of the proposed de-biasing methods.
{"title":"Single-image Super-resolution via De-biased Sparse Representation","authors":"Jian Pu, Yingbin Zheng, Hao Ye","doi":"10.1109/IPTA.2018.8608141","DOIUrl":"https://doi.org/10.1109/IPTA.2018.8608141","url":null,"abstract":"Sparse representation and dictionary learning of image patches are well-known methods for single-image super-resolution. However, due to the regularization term of sparse-inducing penalties, the solution is usually biased. In this study, we present a de-biasing framework by adding a de-biasing step after sparse representation. Two de-biasing methods with sign consistency and feature consistency are further proposed under this framework. Using a unified proximal gradient method, we can solve the proposed de-biasing methods efficiently. Experiments on real super-resolution datasets validate the effectiveness and robustness of the proposed de-biasing methods.","PeriodicalId":272294,"journal":{"name":"2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131000228","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 : 2018-11-01DOI: 10.1109/IPTA.2018.8608157
Y. A. L. Alsabahi, Lei Fan, Xiaoyi Feng
Until now many cancer cases have been discovered in their early stages based on Computer Aided Diagnosis (CAD) system. There are many methods in the medical image processing field have been proposed to address this issue, and the result of these methods was deficient. Further, the application of AI in DR images is not widespread in hospitals. The classification process in the DR image is more difficult than other types of images. In this paper, we use transfer learning which is based on Inception V3 model to classify the DR images. We used the weight of Inception V3 model which was trained in the ImageNet dataset, and fine-tuning in our own dataset. Comparing to other proposed methods, our result had a higher accuracy.
{"title":"Image Classification Method in DR Image Based on Transfer Learning","authors":"Y. A. L. Alsabahi, Lei Fan, Xiaoyi Feng","doi":"10.1109/IPTA.2018.8608157","DOIUrl":"https://doi.org/10.1109/IPTA.2018.8608157","url":null,"abstract":"Until now many cancer cases have been discovered in their early stages based on Computer Aided Diagnosis (CAD) system. There are many methods in the medical image processing field have been proposed to address this issue, and the result of these methods was deficient. Further, the application of AI in DR images is not widespread in hospitals. The classification process in the DR image is more difficult than other types of images. In this paper, we use transfer learning which is based on Inception V3 model to classify the DR images. We used the weight of Inception V3 model which was trained in the ImageNet dataset, and fine-tuning in our own dataset. Comparing to other proposed methods, our result had a higher accuracy.","PeriodicalId":272294,"journal":{"name":"2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"228 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114249207","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 : 2018-11-01DOI: 10.1109/IPTA.2018.8608143
Renato Baptista, Girum G. Demisse, Djamila Aouada, B. Ottersten
In this paper, we propose a system for abnormal motion detection using 3D skeleton information, where the abnormal motion is not known a priori. To that end, we present a curve-based representation of a sequence, based on few joints of a 3D skeleton, and a deformation-based distance function. We further introduce a time-variation model that is specifically designed for assessing the quality of a motion; we refer to a distance function that is based on such a model as motion quality distance. The overall advantages of the proposed approach are 1) lower dimensional yet representative sequence representation and 2) a distance function that emphasizes time variation, the motion quality distance, which is a particularly important property for quality assessment. We validate our approach using a publicly available dataset, SPHERE-StairCase2014 dataset. Qualitative and quantitative results show promising performance.
{"title":"Deformation-Based Abnormal Motion Detection using 3D Skeletons","authors":"Renato Baptista, Girum G. Demisse, Djamila Aouada, B. Ottersten","doi":"10.1109/IPTA.2018.8608143","DOIUrl":"https://doi.org/10.1109/IPTA.2018.8608143","url":null,"abstract":"In this paper, we propose a system for abnormal motion detection using 3D skeleton information, where the abnormal motion is not known a priori. To that end, we present a curve-based representation of a sequence, based on few joints of a 3D skeleton, and a deformation-based distance function. We further introduce a time-variation model that is specifically designed for assessing the quality of a motion; we refer to a distance function that is based on such a model as motion quality distance. The overall advantages of the proposed approach are 1) lower dimensional yet representative sequence representation and 2) a distance function that emphasizes time variation, the motion quality distance, which is a particularly important property for quality assessment. We validate our approach using a publicly available dataset, SPHERE-StairCase2014 dataset. Qualitative and quantitative results show promising performance.","PeriodicalId":272294,"journal":{"name":"2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128881450","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 : 2018-11-01DOI: 10.1109/IPTA.2018.8608150
Marco Micheletto, G. Orrú, Imad Rida, Luca Ghiani, G. Marcialis
The usual trend for the conventional palmvein recognition techniques is first to extract discriminative hand-crafted feature representations from the raw images, and then feed a classifier with them. Unfortunately, it is not yet clear how the effectiveness of such features may be held in case of a large user population or in environments where the variability among acquisitions of the same person may increase. In order to face with this problem, it may be considered that the use of multiple classifiers may increase the recognition performance with respect to that of the best individual classifier, and also may handle the problem of an effective feature extraction step. In this paper, we explore the ensemble classifier approach based on Random Subspace Method (RSM), where the basic feature space is derived after a preliminary feature reduction step on the source image, and compare results achieved with and without the use of hand-crafted features. Experimental results allow us concluding that this approach leads to better results under different environmental conditions.
{"title":"A multiple classifiers-based approach to palmvein identification","authors":"Marco Micheletto, G. Orrú, Imad Rida, Luca Ghiani, G. Marcialis","doi":"10.1109/IPTA.2018.8608150","DOIUrl":"https://doi.org/10.1109/IPTA.2018.8608150","url":null,"abstract":"The usual trend for the conventional palmvein recognition techniques is first to extract discriminative hand-crafted feature representations from the raw images, and then feed a classifier with them. Unfortunately, it is not yet clear how the effectiveness of such features may be held in case of a large user population or in environments where the variability among acquisitions of the same person may increase. In order to face with this problem, it may be considered that the use of multiple classifiers may increase the recognition performance with respect to that of the best individual classifier, and also may handle the problem of an effective feature extraction step. In this paper, we explore the ensemble classifier approach based on Random Subspace Method (RSM), where the basic feature space is derived after a preliminary feature reduction step on the source image, and compare results achieved with and without the use of hand-crafted features. Experimental results allow us concluding that this approach leads to better results under different environmental conditions.","PeriodicalId":272294,"journal":{"name":"2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132413536","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 : 2014-10-01DOI: 10.1109/ICOSP.2014.7015205
Yang Liu, Yangyu Fan, Zhe Guo
With the rapid development of virtual reality (VR) technology, the interactive roaming system becomes key technique in the VR industry, but it is restricted by many immature aspects. In order to improve this technique, we design the interactive bicycle virtual roaming system. Using the digital elevation model (DEM) terrain modeling technology and the shadow information rebuilding based architecture height acquisition method, the target virtual scene is established by utilizing three-dimensional modeling method. Based on the dynamics model of bicycle, the interactive motion control system is designed. Meanwhile, we integrate those cascade systems and many professional contents in the system. It can be widely applied in medical rehabilitation, physical training and entertainment.
{"title":"RESEARCH ON INTERACTIVE BICYCLE ROAMING SYSTEM","authors":"Yang Liu, Yangyu Fan, Zhe Guo","doi":"10.1109/ICOSP.2014.7015205","DOIUrl":"https://doi.org/10.1109/ICOSP.2014.7015205","url":null,"abstract":"With the rapid development of virtual reality (VR) technology, the interactive roaming system becomes key technique in the VR industry, but it is restricted by many immature aspects. In order to improve this technique, we design the interactive bicycle virtual roaming system. Using the digital elevation model (DEM) terrain modeling technology and the shadow information rebuilding based architecture height acquisition method, the target virtual scene is established by utilizing three-dimensional modeling method. Based on the dynamics model of bicycle, the interactive motion control system is designed. Meanwhile, we integrate those cascade systems and many professional contents in the system. It can be widely applied in medical rehabilitation, physical training and entertainment.","PeriodicalId":272294,"journal":{"name":"2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130233804","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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