Pub Date : 1999-06-23DOI: 10.1109/CVPR.1999.786938
R. Carceroni, Kiriakos N. Kutulakos
We introduce a framework for recovering the 3D shape and motion of unknown, arbitrarily-moving curves from two or more image sequences acquired simultaneously from distinct points in space. We use this framework to (1) identify ambiguities in the multi-view recovery of (rigid or nonrigid) 3D motion for arbitrary curves, and (2) identify a novel spatio-temporal constraint that couples the problems of 3D shape and 3D motion recovery in the multi-view case. We show that this constraint leads to a simple hypothesize-and-test algorithm for estimating 3D curve shape and motion simultaneously. Experiments performed with synthetic data suggest that, in addition to recovering 3D curve motion, our approach yields shape estimates of higher accuracy than those obtained when stereo analysis alone is applied to a multi-view sequence.
{"title":"Toward recovering shape and motion of 3D curves from multi-view image sequences","authors":"R. Carceroni, Kiriakos N. Kutulakos","doi":"10.1109/CVPR.1999.786938","DOIUrl":"https://doi.org/10.1109/CVPR.1999.786938","url":null,"abstract":"We introduce a framework for recovering the 3D shape and motion of unknown, arbitrarily-moving curves from two or more image sequences acquired simultaneously from distinct points in space. We use this framework to (1) identify ambiguities in the multi-view recovery of (rigid or nonrigid) 3D motion for arbitrary curves, and (2) identify a novel spatio-temporal constraint that couples the problems of 3D shape and 3D motion recovery in the multi-view case. We show that this constraint leads to a simple hypothesize-and-test algorithm for estimating 3D curve shape and motion simultaneously. Experiments performed with synthetic data suggest that, in addition to recovering 3D curve motion, our approach yields shape estimates of higher accuracy than those obtained when stereo analysis alone is applied to a multi-view sequence.","PeriodicalId":20644,"journal":{"name":"Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149)","volume":"45 1","pages":"192-197 Vol. 1"},"PeriodicalIF":0.0,"publicationDate":"1999-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85863472","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 : 1999-06-23DOI: 10.1109/CVPR.1999.784657
David Jelinek, C. J. Taylor
This paper deals with the problem of recovering the dimensions of an object and its pose from a single image acquired with a camera of unknown focal length. It is assumed that the object in question can be modeled as a polyhedron where the coordinates of the vertices can be expressed as a linear function of a dimension vector, /spl lambda/. The reconstruction program takes as input a set of correspondences between features in the model and features in the image. From this information the program determines an appropriate projection model for the camera (scaled orthographic or perspective), the dimensions of the object, its pose relative to the camera and, in the case of perspective projection, the focal length of the camera. We demonstrate that this reconstruction task can be framed as an unconstrained optimization problem involving a small number of variables, no more than four, regardless of the number of parameters in the dimension vector.
{"title":"Reconstruction of linearly parameterized models from single images with a camera of unknown focal length","authors":"David Jelinek, C. J. Taylor","doi":"10.1109/CVPR.1999.784657","DOIUrl":"https://doi.org/10.1109/CVPR.1999.784657","url":null,"abstract":"This paper deals with the problem of recovering the dimensions of an object and its pose from a single image acquired with a camera of unknown focal length. It is assumed that the object in question can be modeled as a polyhedron where the coordinates of the vertices can be expressed as a linear function of a dimension vector, /spl lambda/. The reconstruction program takes as input a set of correspondences between features in the model and features in the image. From this information the program determines an appropriate projection model for the camera (scaled orthographic or perspective), the dimensions of the object, its pose relative to the camera and, in the case of perspective projection, the focal length of the camera. We demonstrate that this reconstruction task can be framed as an unconstrained optimization problem involving a small number of variables, no more than four, regardless of the number of parameters in the dimension vector.","PeriodicalId":20644,"journal":{"name":"Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149)","volume":"34 1","pages":"346-352 Vol. 2"},"PeriodicalIF":0.0,"publicationDate":"1999-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89102486","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 : 1999-06-23DOI: 10.1109/CVPR.1999.786930
Yanhua Li, M. Brooks
A recursive method is presented for recovering 3D object shape and camera motion under orthography from an extended sequence of video images. This may be viewed as a natural extension of both the original and the sequential factorization methods. A critical aspect of these factorization approaches is the estimation of the so-called shape space, and they may in part be characterized by the manner in which this subspace is computed. If P points are tracked through F frames, the recursive least-squares method proposed in this paper updates the shape space with complexity O(P) per frame. In contrast, the sequential factorization method updates the shape space with complexity O(P/sup 2/) per frame. The original factorization method is intended to be used in batch mode using points tracked across all available frames. It effectively computes the shape space with complexity O(FP/sup 2/) after F frames. Unlike other methods, the recursive approach does not require the estimation or updating of a large measurement or covariance matrix. Experiments with real and synthetic image sequences confirm the recursive method's low computational complexity and good performance, and indicate that it is well suited to real-time applications.
{"title":"An efficient recursive factorization method for determining structure from motion","authors":"Yanhua Li, M. Brooks","doi":"10.1109/CVPR.1999.786930","DOIUrl":"https://doi.org/10.1109/CVPR.1999.786930","url":null,"abstract":"A recursive method is presented for recovering 3D object shape and camera motion under orthography from an extended sequence of video images. This may be viewed as a natural extension of both the original and the sequential factorization methods. A critical aspect of these factorization approaches is the estimation of the so-called shape space, and they may in part be characterized by the manner in which this subspace is computed. If P points are tracked through F frames, the recursive least-squares method proposed in this paper updates the shape space with complexity O(P) per frame. In contrast, the sequential factorization method updates the shape space with complexity O(P/sup 2/) per frame. The original factorization method is intended to be used in batch mode using points tracked across all available frames. It effectively computes the shape space with complexity O(FP/sup 2/) after F frames. Unlike other methods, the recursive approach does not require the estimation or updating of a large measurement or covariance matrix. Experiments with real and synthetic image sequences confirm the recursive method's low computational complexity and good performance, and indicate that it is well suited to real-time applications.","PeriodicalId":20644,"journal":{"name":"Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149)","volume":"7 1","pages":"138-143 Vol. 1"},"PeriodicalIF":0.0,"publicationDate":"1999-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76021097","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 : 1999-06-23DOI: 10.1109/CVPR.1999.786957
D. Alexander
In this paper, parametric statistical modelling of distributions of colour camera data is discussed. A review is provided with some analysis of the properties of some common models, which are generally based on an assumption of independence of the chromaticity and intensity components of colour data. Results of an empirical comparison of the performance of various models are also reviewed. These results indicate that such models are not appropriate for situations other than highly controlled environments. In particular, they perform poorly for daylight imagery. Here, a modification to existing statistical colour models is proposed and the resultant new models are assessed using the same methodology as for the previous results. This simple modification, which is based on the inclusion of an ambient term in the underlying physical model, is shown to have a major impact on the performance of the models in less constrained daylight environments.
{"title":"Advances in daylight statistical colour modelling","authors":"D. Alexander","doi":"10.1109/CVPR.1999.786957","DOIUrl":"https://doi.org/10.1109/CVPR.1999.786957","url":null,"abstract":"In this paper, parametric statistical modelling of distributions of colour camera data is discussed. A review is provided with some analysis of the properties of some common models, which are generally based on an assumption of independence of the chromaticity and intensity components of colour data. Results of an empirical comparison of the performance of various models are also reviewed. These results indicate that such models are not appropriate for situations other than highly controlled environments. In particular, they perform poorly for daylight imagery. Here, a modification to existing statistical colour models is proposed and the resultant new models are assessed using the same methodology as for the previous results. This simple modification, which is based on the inclusion of an ambient term in the underlying physical model, is shown to have a major impact on the performance of the models in less constrained daylight environments.","PeriodicalId":20644,"journal":{"name":"Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149)","volume":"320 1","pages":"313-318 Vol. 1"},"PeriodicalIF":0.0,"publicationDate":"1999-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77603104","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 : 1999-06-23DOI: 10.1109/CVPR.1999.784639
M. Fradkin, M. Roux, H. Maître, U. Leloglu
Accurate 3D surface models of dense urban areas are essential for a variety of applications, such as cartography, urban planning and monitoring mobile communications, etc. Since manual surface reconstruction is very costly and time-consuming, the development of automated algorithms is of great importance. While most of existing algorithms focus on surface reconstruction either in rural or sub-urban areas, we present an approach dealing with dense urban scenes. The approach utilizes different image-derived cues, like multiview stereo and color information, as well as the general scene knowledge, formulated in data-driven reasoning and geometric constraints. Another important feature of our approach is simultaneous processing of 2D and 3D data. Our approach begins with two independent tasks: stereo reconstruction using multiple views and region-based image segmentation, resulting in generation disparity and segmentation maps, respectively. Then, the information derived from the both maps is utilized for generation of a dense elevation map, through robust verification of planar surface approximations for the detected regions and imposition of geometric constraints. The approach has been successfully tested on complex residential and industrial scenes.
{"title":"Surface reconstruction from multiple aerial images in dense urban areas","authors":"M. Fradkin, M. Roux, H. Maître, U. Leloglu","doi":"10.1109/CVPR.1999.784639","DOIUrl":"https://doi.org/10.1109/CVPR.1999.784639","url":null,"abstract":"Accurate 3D surface models of dense urban areas are essential for a variety of applications, such as cartography, urban planning and monitoring mobile communications, etc. Since manual surface reconstruction is very costly and time-consuming, the development of automated algorithms is of great importance. While most of existing algorithms focus on surface reconstruction either in rural or sub-urban areas, we present an approach dealing with dense urban scenes. The approach utilizes different image-derived cues, like multiview stereo and color information, as well as the general scene knowledge, formulated in data-driven reasoning and geometric constraints. Another important feature of our approach is simultaneous processing of 2D and 3D data. Our approach begins with two independent tasks: stereo reconstruction using multiple views and region-based image segmentation, resulting in generation disparity and segmentation maps, respectively. Then, the information derived from the both maps is utilized for generation of a dense elevation map, through robust verification of planar surface approximations for the detected regions and imposition of geometric constraints. The approach has been successfully tested on complex residential and industrial scenes.","PeriodicalId":20644,"journal":{"name":"Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149)","volume":"8 1-2","pages":"262-267 Vol. 2"},"PeriodicalIF":0.0,"publicationDate":"1999-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91500286","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 : 1999-06-23DOI: 10.1109/CVPR.1999.784636
Tat-Jen Cham, James M. Rehg
This paper describes a probabilistic multiple-hypothesis framework for tracking highly articulated objects. In this framework, the probability density of the tracker state is represented as a set of modes with piecewise Gaussians characterizing the neighborhood around these modes. The temporal evolution of the probability density is achieved through sampling from the prior distribution, followed by local optimization of the sample positions to obtain updated modes. This method of generating hypotheses from state-space search does not require the use of discrete features unlike classical multiple-hypothesis tracking. The parametric form of the model is suited for high dimensional state-spaces which cannot be efficiently modeled using non-parametric approaches. Results are shown for tracking Fred Astaire in a movie dance sequence.
{"title":"A multiple hypothesis approach to figure tracking","authors":"Tat-Jen Cham, James M. Rehg","doi":"10.1109/CVPR.1999.784636","DOIUrl":"https://doi.org/10.1109/CVPR.1999.784636","url":null,"abstract":"This paper describes a probabilistic multiple-hypothesis framework for tracking highly articulated objects. In this framework, the probability density of the tracker state is represented as a set of modes with piecewise Gaussians characterizing the neighborhood around these modes. The temporal evolution of the probability density is achieved through sampling from the prior distribution, followed by local optimization of the sample positions to obtain updated modes. This method of generating hypotheses from state-space search does not require the use of discrete features unlike classical multiple-hypothesis tracking. The parametric form of the model is suited for high dimensional state-spaces which cannot be efficiently modeled using non-parametric approaches. Results are shown for tracking Fred Astaire in a movie dance sequence.","PeriodicalId":20644,"journal":{"name":"Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149)","volume":"46 1","pages":"239-244 Vol. 2"},"PeriodicalIF":0.0,"publicationDate":"1999-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91347194","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 : 1999-06-23DOI: 10.1109/CVPR.1999.786953
P. L. Worthington, E. Hancock
This paper makes two contributions to the problem of needle-map recovery using shape-from-shading. Firstly, we provide a geometric update procedure which allows the image irradiance equation to be satisfied as a hard-constraint. This improves the data-closeness of the recovered needle-map. Secondly, we consider how topographic constraints can be lured to impose local consistency on the recovered needle-map. We present several alternative curvature consistency models, and provide an experimental assessment of the new shape-from-shading framework on both real-world images and synthetic images with known ground-truth surface-normals. The main conclusion drawn from our analysis is that the new framework allows rapid development of more appropriate constraints on the SFS problem.
{"title":"Data-driven shape-from-shading using curvature consistency","authors":"P. L. Worthington, E. Hancock","doi":"10.1109/CVPR.1999.786953","DOIUrl":"https://doi.org/10.1109/CVPR.1999.786953","url":null,"abstract":"This paper makes two contributions to the problem of needle-map recovery using shape-from-shading. Firstly, we provide a geometric update procedure which allows the image irradiance equation to be satisfied as a hard-constraint. This improves the data-closeness of the recovered needle-map. Secondly, we consider how topographic constraints can be lured to impose local consistency on the recovered needle-map. We present several alternative curvature consistency models, and provide an experimental assessment of the new shape-from-shading framework on both real-world images and synthetic images with known ground-truth surface-normals. The main conclusion drawn from our analysis is that the new framework allows rapid development of more appropriate constraints on the SFS problem.","PeriodicalId":20644,"journal":{"name":"Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149)","volume":"5 1","pages":"287-293 Vol. 1"},"PeriodicalIF":0.0,"publicationDate":"1999-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87054479","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 : 1999-06-23DOI: 10.1109/CVPR.1999.784624
Mark A. Ruzon, Carlo Tomasi
The compass operator detects step edges without assuming that the regions on either side have constant color. Using distributions of pixel colors rather than the mean, the operator finds the orientation of a diameter that maximizes the difference between two halves of a circular window. Junctions can also be detected by exploiting their lack of bilateral symmetry. This approach is superior to a multi-dimensional gradient method in situations that often result in false negatives, and it localizes edges better as scale increases.
{"title":"Color edge detection with the compass operator","authors":"Mark A. Ruzon, Carlo Tomasi","doi":"10.1109/CVPR.1999.784624","DOIUrl":"https://doi.org/10.1109/CVPR.1999.784624","url":null,"abstract":"The compass operator detects step edges without assuming that the regions on either side have constant color. Using distributions of pixel colors rather than the mean, the operator finds the orientation of a diameter that maximizes the difference between two halves of a circular window. Junctions can also be detected by exploiting their lack of bilateral symmetry. This approach is superior to a multi-dimensional gradient method in situations that often result in false negatives, and it localizes edges better as scale increases.","PeriodicalId":20644,"journal":{"name":"Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149)","volume":"16 1","pages":"160-166 Vol. 2"},"PeriodicalIF":0.0,"publicationDate":"1999-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74706909","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 : 1999-06-23DOI: 10.1109/CVPR.1999.786947
M. N. Ahmed, S. Yamany, A. Farag, T. Moriarty
In this paper, we present a novel algorithm for adaptive fuzzy segmentation of MRI data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the RF coils or some problems associated with the acquisition sequences. The result is a slowly-varying shading artifact over the image that can produce errors with conventional intensity-based classification. Our algorithm is formulated by modifying the objective function of the standard fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel (voxel) to be influenced by the labels in its immediate neighborhood. The neighborhood effect acts as a regularizer and biases the solution towards piecewise-homogeneous labelings. Such a regularization is useful in segmenting scans corrupted by salt and pepper noise. Experimental results on both synthetic images and MR data are given to demonstrate the effectiveness and efficiency of the proposed algorithm.
{"title":"Bias field estimation and adaptive segmentation of MRI data using a modified fuzzy C-means algorithm","authors":"M. N. Ahmed, S. Yamany, A. Farag, T. Moriarty","doi":"10.1109/CVPR.1999.786947","DOIUrl":"https://doi.org/10.1109/CVPR.1999.786947","url":null,"abstract":"In this paper, we present a novel algorithm for adaptive fuzzy segmentation of MRI data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the RF coils or some problems associated with the acquisition sequences. The result is a slowly-varying shading artifact over the image that can produce errors with conventional intensity-based classification. Our algorithm is formulated by modifying the objective function of the standard fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel (voxel) to be influenced by the labels in its immediate neighborhood. The neighborhood effect acts as a regularizer and biases the solution towards piecewise-homogeneous labelings. Such a regularization is useful in segmenting scans corrupted by salt and pepper noise. Experimental results on both synthetic images and MR data are given to demonstrate the effectiveness and efficiency of the proposed algorithm.","PeriodicalId":20644,"journal":{"name":"Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149)","volume":"82 1","pages":"250-255 Vol. 1"},"PeriodicalIF":0.0,"publicationDate":"1999-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76507021","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 : 1999-06-23DOI: 10.1109/CVPR.1999.786912
J. Gluckman, S. Nayar
By using mirror reflections of a scene, stereo images can be captured with a single camera (catadioptric stereo). Single camera stereo provides both geometric and radiometric advantages over traditional two camera stereo. In this paper we discuss the geometry and calibration of catadioptric stereo with two planar mirrors and show how the relative orientation, the epipolar geometry and the estimation of the focal length are constrained by planar motion. In addition, we have implemented a real-time system which demonstrates the viability of stereo with mirrors as an alternative to traditional two camera stereo.
{"title":"Planar catadioptric stereo: geometry and calibration","authors":"J. Gluckman, S. Nayar","doi":"10.1109/CVPR.1999.786912","DOIUrl":"https://doi.org/10.1109/CVPR.1999.786912","url":null,"abstract":"By using mirror reflections of a scene, stereo images can be captured with a single camera (catadioptric stereo). Single camera stereo provides both geometric and radiometric advantages over traditional two camera stereo. In this paper we discuss the geometry and calibration of catadioptric stereo with two planar mirrors and show how the relative orientation, the epipolar geometry and the estimation of the focal length are constrained by planar motion. In addition, we have implemented a real-time system which demonstrates the viability of stereo with mirrors as an alternative to traditional two camera stereo.","PeriodicalId":20644,"journal":{"name":"Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149)","volume":"224 1","pages":"22-28 Vol. 1"},"PeriodicalIF":0.0,"publicationDate":"1999-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80063035","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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