A number of problems in computer vision require the estimation of a set of matrices, each of which is defined only up to an individual scale factor and represents the parameters of a separate model, under the assumption that the models are intrinsically interconnected. One example of such a set is a family of fundamental matrices sharing an infinite homography. Here an approach is presented to estimating a general set of interdependent matrices defined to within separate scales. The input data is assumed to consist of individually estimated matrices for particular models, which when considered collectively may fail to satisfy the constraints representing the inter-model relationships. Two cost functions are proposed for upgrading, via optimisation, the data of this sort to a collection of matrices satisfying the inter- model constraints. One of these functions incorporates error covariances. Each function is invariant to any change of scale for the input estimates. The proposed approach is applied to the particular problem of estimating a set of fundamental matrices of the form of the example set above. Experimental results are given which demonstrate the effectiveness of the approach.
{"title":"Multi-projective Parameter Estimation for Sets of Homogeneous Matrices","authors":"W. Chojnacki, R. Hill, A. Hengel, M. Brooks","doi":"10.1109/DICTA.2009.27","DOIUrl":"https://doi.org/10.1109/DICTA.2009.27","url":null,"abstract":"A number of problems in computer vision require the estimation of a set of matrices, each of which is defined only up to an individual scale factor and represents the parameters of a separate model, under the assumption that the models are intrinsically interconnected. One example of such a set is a family of fundamental matrices sharing an infinite homography. Here an approach is presented to estimating a general set of interdependent matrices defined to within separate scales. The input data is assumed to consist of individually estimated matrices for particular models, which when considered collectively may fail to satisfy the constraints representing the inter-model relationships. Two cost functions are proposed for upgrading, via optimisation, the data of this sort to a collection of matrices satisfying the inter- model constraints. One of these functions incorporates error covariances. Each function is invariant to any change of scale for the input estimates. The proposed approach is applied to the particular problem of estimating a set of fundamental matrices of the form of the example set above. Experimental results are given which demonstrate the effectiveness of the approach.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121040376","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}
The success of visual tracking systems is highly dependent upon the effectiveness of the measurement function used to evaluate the likelihood of a hypothesized object state. Generative tracking algorithms attempt to find the global and other local maxima of these measurement functions. As such, designing measurement functions which have a small number of local maxima is highly desirable. Edge based measurements are an integral component of most measurement functions. Graph based methods are commonly used for image segmentation, and more recently have been applied to visual tracking problems. When self occlusions are present, it is necessary to find the shortest path across a graph when the weights of some graph vertices are unknown. In this paper, treatments are given for handling object self occlusions in graph based edge measurement methods. Experiments are performed to test the effect that each of these treatments has on the accuracy and number of modes in the observational likelihood.
{"title":"Self Occlusions and Graph Based Edge Measurement Schemes for Visual Tracking Applications","authors":"Andrew W. B. Smith, B. Lovell","doi":"10.1109/DICTA.2009.74","DOIUrl":"https://doi.org/10.1109/DICTA.2009.74","url":null,"abstract":"The success of visual tracking systems is highly dependent upon the effectiveness of the measurement function used to evaluate the likelihood of a hypothesized object state. Generative tracking algorithms attempt to find the global and other local maxima of these measurement functions. As such, designing measurement functions which have a small number of local maxima is highly desirable. Edge based measurements are an integral component of most measurement functions. Graph based methods are commonly used for image segmentation, and more recently have been applied to visual tracking problems. When self occlusions are present, it is necessary to find the shortest path across a graph when the weights of some graph vertices are unknown. In this paper, treatments are given for handling object self occlusions in graph based edge measurement methods. Experiments are performed to test the effect that each of these treatments has on the accuracy and number of modes in the observational likelihood.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127224513","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}
Local features are gaining popularity due to their robustness to occlusion and other variations such as minor deformation. However, using local features for recognition of biometric traits, which are generally highly similar, can produce large numbers of false matches. To increase recognition performance, we propose to eliminate some incorrect matches using a simple form geometric consistency, and some associated similarity measures. The performance of the approach is evaluated on different datasets and compared with some previous approaches. We obtain an improvement from 81.60% to 92.77% in rank-1 ear identification on the University of Notre Dame Biometric Database, the largest publicly available profile database from the University of Notre Dame with 415 subjects.
局部特征由于其对遮挡和其他变化(如轻微变形)的鲁棒性而越来越受欢迎。然而，使用局部特征来识别生物特征通常是高度相似的，可能会产生大量的错误匹配。为了提高识别性能，我们建议使用简单形式的几何一致性和一些相关的相似性度量来消除一些不正确的匹配。在不同的数据集上评估了该方法的性能，并与之前的一些方法进行了比较。我们在圣母大学生物特征数据库(University of Notre Dame Biometric Database)上获得了从81.60%提高到92.77%的秩1耳朵识别，该数据库是圣母大学最大的公开资料数据库，共有415名受试者。
{"title":"Refining Local 3D Feature Matching through Geometric Consistency for Robust Biometric Recognition","authors":"S. Islam, Rowan Davies","doi":"10.1109/DICTA.2009.87","DOIUrl":"https://doi.org/10.1109/DICTA.2009.87","url":null,"abstract":"Local features are gaining popularity due to their robustness to occlusion and other variations such as minor deformation. However, using local features for recognition of biometric traits, which are generally highly similar, can produce large numbers of false matches. To increase recognition performance, we propose to eliminate some incorrect matches using a simple form geometric consistency, and some associated similarity measures. The performance of the approach is evaluated on different datasets and compared with some previous approaches. We obtain an improvement from 81.60% to 92.77% in rank-1 ear identification on the University of Notre Dame Biometric Database, the largest publicly available profile database from the University of Notre Dame with 415 subjects.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116094712","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}
Md. Nazmul Haque, Moyuresh Biswas, M. Pickering, M. Frater
Now-a-days image alignment is one of the most widely used techniques in computer vision. Image alignment has many applications in fields as diverse as video surveillance, computer vision, medical imaging, and video coding. The estimation of an objects’ motion is a key step in image alignment. In this paper, we will present a low-complexity algorithm for estimation of motion parameters. Most of the motion parameters estimation algorithms are carried out with a precision of 8 bits per pixel; here we propose an algorithm using only 1 bit per pixel resulting in lower complexity. The proposed method includes a technique for calculating the gradient of the sum-of-squared difference (SSD) using XOR operations instead of multiplication. Experimental results show that the proposed method compares favorably with registration using the full precision of the input images.
{"title":"A Low Complexity Algorithm for Global Motion Parameter Estimation Targeting Hardware Implementation","authors":"Md. Nazmul Haque, Moyuresh Biswas, M. Pickering, M. Frater","doi":"10.1109/DICTA.2009.11","DOIUrl":"https://doi.org/10.1109/DICTA.2009.11","url":null,"abstract":"Now-a-days image alignment is one of the most widely used techniques in computer vision. Image alignment has many applications in fields as diverse as video surveillance, computer vision, medical imaging, and video coding. The estimation of an objects’ motion is a key step in image alignment. In this paper, we will present a low-complexity algorithm for estimation of motion parameters. Most of the motion parameters estimation algorithms are carried out with a precision of 8 bits per pixel; here we propose an algorithm using only 1 bit per pixel resulting in lower complexity. The proposed method includes a technique for calculating the gradient of the sum-of-squared difference (SSD) using XOR operations instead of multiplication. Experimental results show that the proposed method compares favorably with registration using the full precision of the input images.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129580826","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}
The transmission of remote sensed images across communication paths is becoming a very expensive process because of the recent advances towards the satellite technologies that enable to download of terabytes of data every day. Image compression is an option for reducing the number of bits in transmission and various compression techniques have been developed; including predictive coding, transform coding and vector quantization. However, most techniques perform data compression within a data set. In this paper, we assume that the user has already received previous data and needs to update that only. A combined time domain and spectral domain data compression scheme is proposed. Change detection between the two dates is first performed followed by separate modelling of changed and non changed data relationship for one band in order to transmit them more efficiently. The rest of bands are transmitted by the prediction from band to band, since they are highly correlated. The developed scheme is illustrated with a subset of Landsat ETM data recorded over Canberra, Australia, in 2000 and 2001.
{"title":"Combined Time Domain and Spectral Domain Data Compression for Fast Multispectral Imagery Updating","authors":"Md. Al Mamun, X. Jia, M. Ryan","doi":"10.1109/DICTA.2009.54","DOIUrl":"https://doi.org/10.1109/DICTA.2009.54","url":null,"abstract":"The transmission of remote sensed images across communication paths is becoming a very expensive process because of the recent advances towards the satellite technologies that enable to download of terabytes of data every day. Image compression is an option for reducing the number of bits in transmission and various compression techniques have been developed; including predictive coding, transform coding and vector quantization. However, most techniques perform data compression within a data set. In this paper, we assume that the user has already received previous data and needs to update that only. A combined time domain and spectral domain data compression scheme is proposed. Change detection between the two dates is first performed followed by separate modelling of changed and non changed data relationship for one band in order to transmit them more efficiently. The rest of bands are transmitted by the prediction from band to band, since they are highly correlated. The developed scheme is illustrated with a subset of Landsat ETM data recorded over Canberra, Australia, in 2000 and 2001.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"308 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116335223","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}
The inherent nature of image and video and its multi-dimension data space makes its processing and interpretation a very complex task, normally requiring considerable processing power. Moreover, understanding the meaning of video content and storing it in a fast searchable and readable form, requires taking advantage of image processing methods, which when running them on a video stream per query, would not be cost effective, and in some cases is quite impossible due to time restrictions. Hence, to speed up the search process, storing video and its extracted meta-data together is desired. The storage model itself is one of the challenges in this context, as based on the current CCTV technology; it is estimated to require a petabyte size data management system. This estimate however, is expected to grow rapidly as current advances in video recording devices are leading to higher resolution sensors, and larger frame size. On the other hand, the increasing demand for object tracking on video streams has invoked the research on Content-Based Image Retrieval (CBIR) and Content-Based Video Retrieval (CBVR). In this paper, we present the design and implementation of a framework and a data model for CCTV surveillance videos on RDBMS which provides the functions of a surveillance monitoring system, with a tagging structure for event detection. On account of some recent results, we believe this is a promising direction for surveillance video search in comparison to the existing solutions.
{"title":"Content-Based Video Retrieval (CBVR) System for CCTV Surveillance Videos","authors":"Yan Yang, B. Lovell, F. Dadgostar","doi":"10.1109/DICTA.2009.36","DOIUrl":"https://doi.org/10.1109/DICTA.2009.36","url":null,"abstract":"The inherent nature of image and video and its multi-dimension data space makes its processing and interpretation a very complex task, normally requiring considerable processing power. Moreover, understanding the meaning of video content and storing it in a fast searchable and readable form, requires taking advantage of image processing methods, which when running them on a video stream per query, would not be cost effective, and in some cases is quite impossible due to time restrictions. Hence, to speed up the search process, storing video and its extracted meta-data together is desired. The storage model itself is one of the challenges in this context, as based on the current CCTV technology; it is estimated to require a petabyte size data management system. This estimate however, is expected to grow rapidly as current advances in video recording devices are leading to higher resolution sensors, and larger frame size. On the other hand, the increasing demand for object tracking on video streams has invoked the research on Content-Based Image Retrieval (CBIR) and Content-Based Video Retrieval (CBVR). In this paper, we present the design and implementation of a framework and a data model for CCTV surveillance videos on RDBMS which provides the functions of a surveillance monitoring system, with a tagging structure for event detection. On account of some recent results, we believe this is a promising direction for surveillance video search in comparison to the existing solutions.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124047457","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}
In this paper, we examine the problem of authenticating paper media using the unique fiber structure of each piece of paper (the so-called "paper fingerprint"). In particular, we look at methods to authenticate paper media when text has been printed over the authentication zone. We show how alpha-masked correlation [Fitch05] can be applied to this problem and develop a modification to alpha-masked correlation that is more closely matched to the requirements of this problem and produces an improvement in performance. We also investigate two methods of pixel inpainting to remove printed text or marks from the authentication zone and allow ordinary correlation to be performed. We show that these methods can perform as well as alpha-masked correlation. Finally two methods of improving the robustness to forgery are investigated.
{"title":"Paper Fingerprinting Using alpha-Masked Image Matching","authors":"Tuan Q. Pham, S. Perry, P. Fletcher","doi":"10.1109/DICTA.2009.82","DOIUrl":"https://doi.org/10.1109/DICTA.2009.82","url":null,"abstract":"In this paper, we examine the problem of authenticating paper media using the unique fiber structure of each piece of paper (the so-called \"paper fingerprint\"). In particular, we look at methods to authenticate paper media when text has been printed over the authentication zone. We show how alpha-masked correlation [Fitch05] can be applied to this problem and develop a modification to alpha-masked correlation that is more closely matched to the requirements of this problem and produces an improvement in performance. We also investigate two methods of pixel inpainting to remove printed text or marks from the authentication zone and allow ordinary correlation to be performed. We show that these methods can perform as well as alpha-masked correlation. Finally two methods of improving the robustness to forgery are investigated.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127904883","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}
3D shape matching based on meshed surfaces can be formulated as an energy function minimisation problem under a Markov random field (MRF) framework. However, to solve such a global optimisation problem is NP-hard. So research mainly focuses on approximation algorithms. One of the best known is belief propagation (BP), which has shown success in early vision and many other practical applications. In this paper, we investigate the application of both sum and max product algorithms of belief propagation to 3D shape matching. We also apply the 3D shape matching results to a 3D registration problem.
{"title":"Applying Sum and Max Product Algorithms of Belief Propagation to 3D Shape Matching and Registration","authors":"Pengdong Xiao, N. Barnes, P. Lieby, T. Caetano","doi":"10.1109/DICTA.2009.70","DOIUrl":"https://doi.org/10.1109/DICTA.2009.70","url":null,"abstract":"3D shape matching based on meshed surfaces can be formulated as an energy function minimisation problem under a Markov random field (MRF) framework. However, to solve such a global optimisation problem is NP-hard. So research mainly focuses on approximation algorithms. One of the best known is belief propagation (BP), which has shown success in early vision and many other practical applications. In this paper, we investigate the application of both sum and max product algorithms of belief propagation to 3D shape matching. We also apply the 3D shape matching results to a 3D registration problem.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128811067","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}
In this paper we propose a new kernel PCA (KPCA) speed-up algorithm that aims to find a reduced KPCA to approximate the kernel mapping. The algorithm works by greedily choosing a subset of the training samples that minimizes the mean square error of the kernel mapping between the original KPCA and the reduced KPCA. Experimental results have shown that the proposed algorithm is more efficient in computation and effective with lower mapping errors than previous algorithms.
{"title":"Greedy Approximation of Kernel PCA by Minimizing the Mapping Error","authors":"Peng Cheng, W. Li, P. Ogunbona","doi":"10.1109/DICTA.2009.57","DOIUrl":"https://doi.org/10.1109/DICTA.2009.57","url":null,"abstract":"In this paper we propose a new kernel PCA (KPCA) speed-up algorithm that aims to find a reduced KPCA to approximate the kernel mapping. The algorithm works by greedily choosing a subset of the training samples that minimizes the mean square error of the kernel mapping between the original KPCA and the reduced KPCA. Experimental results have shown that the proposed algorithm is more efficient in computation and effective with lower mapping errors than previous algorithms.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125936594","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}
Hassan Masood, Mohammad Asim, Mustafa Mumtaz, A. Mansoor
Palmprint based personal verification is an accepted biometric modality due to its reliability, ease of acquisition and user acceptance. This paper presents a novel palmprint based identification approach which draw on the textural information available on the palmprint by utilizing a combination of Contourlet and Non Subsampled Contourlet Transforms. Center of the palm is computed using the Distance Transform whereas the parameters of best fitting ellipse help determine the alignment of the palmprint. ROI of 256X256 pixels is cropped around the center, and subsequently it is divided into fine slices, using iterated directional filterbanks. Next, directional energy components for each block of the decomposed subband outputs are computed using Contourlet and Non Subsampled Contourlet Transforms. The proposed algorithm captures global details in a palmprint as compact fixed length palm codes for Contourlet and NSCT respectively which are further concatenated at feature level for identification using normalized Euclidean distance classifier. The proposed algorithm is tested on a total of 500 palm images of GPDS Hand database, acquired from University of Las Palmas de Gran Canaria. The experimental results were compiled for individual transforms as well as for their optimized combination at feature level. CT based approach demonstrated the Decidability Index of 2.6212 and Equal Error Rate (EER) of 0.7082% while NSCT based approach depicted Decidability Index of 2.7278 and EER of 0.5082%. The feature level fusion achieved Decidability Index of 2.7956 and EER of 0.3112%.
{"title":"Combined Contourlet and Non-subsampled Contourlet Transforms Based Approach for Personal Identification Using Palmprint","authors":"Hassan Masood, Mohammad Asim, Mustafa Mumtaz, A. Mansoor","doi":"10.1109/DICTA.2009.73","DOIUrl":"https://doi.org/10.1109/DICTA.2009.73","url":null,"abstract":"Palmprint based personal verification is an accepted biometric modality due to its reliability, ease of acquisition and user acceptance. This paper presents a novel palmprint based identification approach which draw on the textural information available on the palmprint by utilizing a combination of Contourlet and Non Subsampled Contourlet Transforms. Center of the palm is computed using the Distance Transform whereas the parameters of best fitting ellipse help determine the alignment of the palmprint. ROI of 256X256 pixels is cropped around the center, and subsequently it is divided into fine slices, using iterated directional filterbanks. Next, directional energy components for each block of the decomposed subband outputs are computed using Contourlet and Non Subsampled Contourlet Transforms. The proposed algorithm captures global details in a palmprint as compact fixed length palm codes for Contourlet and NSCT respectively which are further concatenated at feature level for identification using normalized Euclidean distance classifier. The proposed algorithm is tested on a total of 500 palm images of GPDS Hand database, acquired from University of Las Palmas de Gran Canaria. The experimental results were compiled for individual transforms as well as for their optimized combination at feature level. CT based approach demonstrated the Decidability Index of 2.6212 and Equal Error Rate (EER) of 0.7082% while NSCT based approach depicted Decidability Index of 2.7278 and EER of 0.5082%. The feature level fusion achieved Decidability Index of 2.7956 and EER of 0.3112%.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"62 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128692800","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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