This paper describes a method to align textured range images of deforming objects. The proposed procedure aligns deformable 3D models by matching both texture and shape features. First, the characteristics of each vertex of a 3D mesh model is defined by computing a color histogram for the texture feature and the average signed distance for the shape feature. Next, the key points, which are the distinctive vertices of a model, are extracted with respect to the texture and shape features. Subsequently, the corresponding points are located by matching the key points of the models before and after deformation. The deforming parameters are computed by minimizing the distance between the corresponding points. The proposed method iterates the correspondence search and deformation to align range images. Finally, the deformation for all vertices is computed by interpolating the parameters of the key points. In the experiments, we obtained textured range images by using a real-time range finder and a camera, and evaluated deformable registration for the range images.
{"title":"Deformable Registration of Textured Range Images by Using Texture and Shape Features","authors":"R. Sagawa, Nanaho Osawa, Y. Yagi","doi":"10.1109/3DIM.2007.18","DOIUrl":"https://doi.org/10.1109/3DIM.2007.18","url":null,"abstract":"This paper describes a method to align textured range images of deforming objects. The proposed procedure aligns deformable 3D models by matching both texture and shape features. First, the characteristics of each vertex of a 3D mesh model is defined by computing a color histogram for the texture feature and the average signed distance for the shape feature. Next, the key points, which are the distinctive vertices of a model, are extracted with respect to the texture and shape features. Subsequently, the corresponding points are located by matching the key points of the models before and after deformation. The deforming parameters are computed by minimizing the distance between the corresponding points. The proposed method iterates the correspondence search and deformation to align range images. Finally, the deformation for all vertices is computed by interpolating the parameters of the key points. In the experiments, we obtained textured range images by using a real-time range finder and a camera, and evaluated deformable registration for the range images.","PeriodicalId":442311,"journal":{"name":"Sixth International Conference on 3-D Digital Imaging and Modeling (3DIM 2007)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117198136","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}
An on-line 3D registration system is introduced to reconstruct a 3D model using a hand-held stereo camera. Multi-view range images, which are obtained from the hand-held stereo camera, are registered into a reference coordinate system simultaneously. For the coarse registration of a range image, we use the transformation of its previous frame and the centroid of foreground range images. After refining the coarse registration using a geometric feature matching technique, we use a modified KLT (Kanade-Lucas-Tomasi) tracker to match photometric features to enhance registration accuracy. We modify the KLT tracker to facilitate searching of correspondence from the results of the geometric registration. If a range image fails to register, we adjust the orientation of the camera while viewing a graphical user interaction system. After enough range images are registered, they are integrated into a 3D model in offline. Experimental results show that the proposed method can be used to reconstruct 3D models fast and accurately.
{"title":"Online Registration of Multi-view Range Images using Geometric and Photometric Feature Tracking","authors":"Soon-Yong Park, Jaewon Baek","doi":"10.1109/3DIM.2007.36","DOIUrl":"https://doi.org/10.1109/3DIM.2007.36","url":null,"abstract":"An on-line 3D registration system is introduced to reconstruct a 3D model using a hand-held stereo camera. Multi-view range images, which are obtained from the hand-held stereo camera, are registered into a reference coordinate system simultaneously. For the coarse registration of a range image, we use the transformation of its previous frame and the centroid of foreground range images. After refining the coarse registration using a geometric feature matching technique, we use a modified KLT (Kanade-Lucas-Tomasi) tracker to match photometric features to enhance registration accuracy. We modify the KLT tracker to facilitate searching of correspondence from the results of the geometric registration. If a range image fails to register, we adjust the orientation of the camera while viewing a graphical user interaction system. After enough range images are registered, they are integrated into a 3D model in offline. Experimental results show that the proposed method can be used to reconstruct 3D models fast and accurately.","PeriodicalId":442311,"journal":{"name":"Sixth International Conference on 3-D Digital Imaging and Modeling (3DIM 2007)","volume":"21 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120994463","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 present a novel approach for extracting silhouettes by using a particular pattern that we call the random pattern. The volume intersection method reconstructs the shapes of 3D objects from their silhouettes obtained with multiple cameras. With the method, if some parts of the silhouettes are missed, the corresponding parts of the reconstructed shapes are also missed. When colors of the objects and the backgrounds are similar, many parts of the silhouettes are missed. We adopt random pattern backgrounds to extract correct silhouettes. The random pattern has many small regions with randomly-selected colors. By using the random pattern backgrounds, we can keep the rate of missing parts below a specified percentage, even for objects of unknown color. To refine the silhouettes, we detect and fill in the missing parts by integrating multiple images. From the images captured by multiple cameras used to observe the object, the object's colors can be estimated. The missing parts can be detected by comparing the object's color with its corresponding background's color. In our experiments, we confirmed that this method effectively extracts silhouettes and reconstructs 3D shapes.
{"title":"Silhouette Extraction with Random Pattern Backgrounds for the Volume Intersection Method","authors":"M. Toyoura, M. Iiyama, K. Kakusho, M. Minoh","doi":"10.1109/3DIM.2007.48","DOIUrl":"https://doi.org/10.1109/3DIM.2007.48","url":null,"abstract":"In this paper, we present a novel approach for extracting silhouettes by using a particular pattern that we call the random pattern. The volume intersection method reconstructs the shapes of 3D objects from their silhouettes obtained with multiple cameras. With the method, if some parts of the silhouettes are missed, the corresponding parts of the reconstructed shapes are also missed. When colors of the objects and the backgrounds are similar, many parts of the silhouettes are missed. We adopt random pattern backgrounds to extract correct silhouettes. The random pattern has many small regions with randomly-selected colors. By using the random pattern backgrounds, we can keep the rate of missing parts below a specified percentage, even for objects of unknown color. To refine the silhouettes, we detect and fill in the missing parts by integrating multiple images. From the images captured by multiple cameras used to observe the object, the object's colors can be estimated. The missing parts can be detected by comparing the object's color with its corresponding background's color. In our experiments, we confirmed that this method effectively extracts silhouettes and reconstructs 3D shapes.","PeriodicalId":442311,"journal":{"name":"Sixth International Conference on 3-D Digital Imaging and Modeling (3DIM 2007)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122553762","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}
Geometric models created from range sensors are usually incomplete. Considerable effort has been made to fix this problem, ranging from manual repairing to geometric interpolation. We propose using multi-view stereo to complete such models. Our approach is practical and convenient because when scanning and object or environment one usually takes photographs to texture the resulting model. By using the incomplete scan data as a boundary condition, we use a variational multi-view approach to estimate the missing data.
{"title":"Image-based Model Completion","authors":"A. Brunton, S. Wuhrer, Chang Shu","doi":"10.1109/3DIM.2007.29","DOIUrl":"https://doi.org/10.1109/3DIM.2007.29","url":null,"abstract":"Geometric models created from range sensors are usually incomplete. Considerable effort has been made to fix this problem, ranging from manual repairing to geometric interpolation. We propose using multi-view stereo to complete such models. Our approach is practical and convenient because when scanning and object or environment one usually takes photographs to texture the resulting model. By using the incomplete scan data as a boundary condition, we use a variational multi-view approach to estimate the missing data.","PeriodicalId":442311,"journal":{"name":"Sixth International Conference on 3-D Digital Imaging and Modeling (3DIM 2007)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114602429","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 contribution we introduce the multiocular contracting curve density algorithm (MOCCD), a novel method for fitting a 3D parametric curve. The MOCCD is integrated into a tracking system and its suitability for tracking human body parts in 3D in front of cluttered background is examined. The developed system can be applied to a variety of body parts, as the object model is replaceable in a simple manner. Based on the example of tracking the human hand-forearm limb it is shown that the use of three MOCCD algorithms with three different kinematic models within the system leads to an accurate and temporally stable tracking. All necessary information is obtained from the images, only a coarse initialisation of the model parameters is required. The investigations are performed on 14 real-world test sequences. These contain movements of different hand-forearm configurations in front of a complex cluttered background. We find that the use of three cameras is essential for an accurate and temporally stable system performance since otherwise the pose estimation and tracking results are strongly affected by the aperture problem. Our best method achieves 95% recognition rate, compared to about 30% for the reference methods of 3D active contours and a curve model tracked by a particle filter. Hence only 5% of the estimated model points exceed a distance of 12 cm with respect to the ground truth, using the proposed method.
{"title":"Tracking of Human Body Parts using the Multiocular Contracting Curve Density Algorithm","authors":"Markus Hahn, Lars Krüger, C. Wöhler, H. Groß","doi":"10.1109/3DIM.2007.59","DOIUrl":"https://doi.org/10.1109/3DIM.2007.59","url":null,"abstract":"In this contribution we introduce the multiocular contracting curve density algorithm (MOCCD), a novel method for fitting a 3D parametric curve. The MOCCD is integrated into a tracking system and its suitability for tracking human body parts in 3D in front of cluttered background is examined. The developed system can be applied to a variety of body parts, as the object model is replaceable in a simple manner. Based on the example of tracking the human hand-forearm limb it is shown that the use of three MOCCD algorithms with three different kinematic models within the system leads to an accurate and temporally stable tracking. All necessary information is obtained from the images, only a coarse initialisation of the model parameters is required. The investigations are performed on 14 real-world test sequences. These contain movements of different hand-forearm configurations in front of a complex cluttered background. We find that the use of three cameras is essential for an accurate and temporally stable system performance since otherwise the pose estimation and tracking results are strongly affected by the aperture problem. Our best method achieves 95% recognition rate, compared to about 30% for the reference methods of 3D active contours and a curve model tracked by a particle filter. Hence only 5% of the estimated model points exceed a distance of 12 cm with respect to the ground truth, using the proposed method.","PeriodicalId":442311,"journal":{"name":"Sixth International Conference on 3-D Digital Imaging and Modeling (3DIM 2007)","volume":"263 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127836062","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}
We present a discrete approach for boundary extraction from 3D image data. The proposed technique is based on the duality between the Voronoi graph computed accross the digital boundary and the Delaunay triangulation. The originality of the approach is that algorithms perform only integer arithmetic and the method does not suffer from standard round problems and numerical instabilities in the case of floating point computations. This method has been applied both on segmented anatomical structures and on manufactured objects presenting corners and edges. The experimental results show that the method allows to produce a polygonal boundary representation which is guaranteed to be a 2-manifold. This representation is successfully transformed into a triangular quality mesh which meets all topological and geometrical requirements of applications such as augmented reality or simulation.
{"title":"Discrete Delaunay: Boundary extraction from voxel objects","authors":"Dobrina Boltcheva, D. Bechmann, S. Thery","doi":"10.1109/3DIM.2007.21","DOIUrl":"https://doi.org/10.1109/3DIM.2007.21","url":null,"abstract":"We present a discrete approach for boundary extraction from 3D image data. The proposed technique is based on the duality between the Voronoi graph computed accross the digital boundary and the Delaunay triangulation. The originality of the approach is that algorithms perform only integer arithmetic and the method does not suffer from standard round problems and numerical instabilities in the case of floating point computations. This method has been applied both on segmented anatomical structures and on manufactured objects presenting corners and edges. The experimental results show that the method allows to produce a polygonal boundary representation which is guaranteed to be a 2-manifold. This representation is successfully transformed into a triangular quality mesh which meets all topological and geometrical requirements of applications such as augmented reality or simulation.","PeriodicalId":442311,"journal":{"name":"Sixth International Conference on 3-D Digital Imaging and Modeling (3DIM 2007)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125482529","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}
We address the problem of reconstruction and registration of a deforming 3D surface observed by some 3D sensor giving a cloud of 3D points at each time instant. This problem is difficult since the basic data term does not provide enough constraints. We bring two main contributions. First, we examine a set of data and penalty terms that make the problem well-posed. The most important terms we introduce are the non- extensibility penalty and the attraction to boundary shape. Second, we show how the error function combining all these terms can be efficiently minimized with the Levenberg-Marquardt algorithm and sparse matrices. We report convincing results for challenging datasets coming from different kinds of 3D sensors. The algorithm is robust to missing and erroneous data points, and to spurious boundary detection.
{"title":"Joint Reconstruction and Registration of a Deformable Planar Surface Observed by a 3D Sensor","authors":"U. Castellani, V. Gay-Bellile, A. Bartoli","doi":"10.1109/3DIM.2007.31","DOIUrl":"https://doi.org/10.1109/3DIM.2007.31","url":null,"abstract":"We address the problem of reconstruction and registration of a deforming 3D surface observed by some 3D sensor giving a cloud of 3D points at each time instant. This problem is difficult since the basic data term does not provide enough constraints. We bring two main contributions. First, we examine a set of data and penalty terms that make the problem well-posed. The most important terms we introduce are the non- extensibility penalty and the attraction to boundary shape. Second, we show how the error function combining all these terms can be efficiently minimized with the Levenberg-Marquardt algorithm and sparse matrices. We report convincing results for challenging datasets coming from different kinds of 3D sensors. The algorithm is robust to missing and erroneous data points, and to spurious boundary detection.","PeriodicalId":442311,"journal":{"name":"Sixth International Conference on 3-D Digital Imaging and Modeling (3DIM 2007)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122676291","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}
Plants are characterised by a range of complex and variable attributes, and measuring these attributes accurately and reliably is a major challenge for the industry. In this paper, we investigate creating a surface model of plant from images taken by a stereo pair of cameras. The proposed modelling architecture comprises a fast stereo algorithm to estimate depths in the scene and a model of the scene based on visual appearance and 3D geometry measurements. Our stereo algorithm employs a coarse-fine strategy for disparity estimation. We develop a weighting method and use Kalman filter to refine estimations across scales. A self-organising map is applied to reconstruct a surface from these sample points created by the stereo algorithm. We compare and evaluate our stereo results against other popular stereo algorithms, and also demonstrate that the proposed surface model can be used to extract useful plant features that can be of importance in plant management and assessing quality for marketing.
{"title":"Surface Modelling of Plants from Stereo Images","authors":"Yu Song, R. Wilson, R. Edmondson, N. Parsons","doi":"10.1109/3DIM.2007.55","DOIUrl":"https://doi.org/10.1109/3DIM.2007.55","url":null,"abstract":"Plants are characterised by a range of complex and variable attributes, and measuring these attributes accurately and reliably is a major challenge for the industry. In this paper, we investigate creating a surface model of plant from images taken by a stereo pair of cameras. The proposed modelling architecture comprises a fast stereo algorithm to estimate depths in the scene and a model of the scene based on visual appearance and 3D geometry measurements. Our stereo algorithm employs a coarse-fine strategy for disparity estimation. We develop a weighting method and use Kalman filter to refine estimations across scales. A self-organising map is applied to reconstruct a surface from these sample points created by the stereo algorithm. We compare and evaluate our stereo results against other popular stereo algorithms, and also demonstrate that the proposed surface model can be used to extract useful plant features that can be of importance in plant management and assessing quality for marketing.","PeriodicalId":442311,"journal":{"name":"Sixth International Conference on 3-D Digital Imaging and Modeling (3DIM 2007)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134117360","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}
We use the AdaBoost algorithm to classify 3D aerial lidar scattered height data into four categories: road, grass, buildings, and trees. To do so we use five features: height, height variation, normal variation, lidar return intensity, and image intensity. We also use only lidar-derived features to organize the data into three classes (the road and grass classes are merged). We apply and test our results using ten regions taken from lidar data collected over an area of approximately eight square miles, obtaining higher than 92% accuracy. We also apply our classifier to our entire dataset, and present visual classification results both with and without uncertainty. We implement and experiment with several variations within the AdaBoost family of algorithms. We observe that our results are robust and stable over all the various tests and algorithmic variations. We also investigate features and values that are most critical in distinguishing between the classes. This insight is important in extending the results from one geographic region to another.
{"title":"Aerial Lidar Data Classification using AdaBoost","authors":"S. Lodha, D. Fitzpatrick, D. Helmbold","doi":"10.1109/3DIM.2007.10","DOIUrl":"https://doi.org/10.1109/3DIM.2007.10","url":null,"abstract":"We use the AdaBoost algorithm to classify 3D aerial lidar scattered height data into four categories: road, grass, buildings, and trees. To do so we use five features: height, height variation, normal variation, lidar return intensity, and image intensity. We also use only lidar-derived features to organize the data into three classes (the road and grass classes are merged). We apply and test our results using ten regions taken from lidar data collected over an area of approximately eight square miles, obtaining higher than 92% accuracy. We also apply our classifier to our entire dataset, and present visual classification results both with and without uncertainty. We implement and experiment with several variations within the AdaBoost family of algorithms. We observe that our results are robust and stable over all the various tests and algorithmic variations. We also investigate features and values that are most critical in distinguishing between the classes. This insight is important in extending the results from one geographic region to another.","PeriodicalId":442311,"journal":{"name":"Sixth International Conference on 3-D Digital Imaging and Modeling (3DIM 2007)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121269563","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}
While 3D photography research has enjoyed tremendous success, many everyday objects and materials are still difficult or impossible to capture in 3D. An important stumbling block is that typical algorithms do not consider the effects of light transport, i.e., the sequence of bounces, refractions and scattering events that may occur when light interacts with an object. This puts objects with transparent materials or highly-reflective surfaces (clear plastic, crystal, liquids, polished metal, etc.) outside the reach of current 3D scanning techniques. To overcome these limitations, we have been investigating algorithms that explicitly analyze the light transport process caused by such objects (N. Morris and K.N. Kutulakos, 2005), (K.N. Kutulakos and E. Steger, 2005). These algorithms rely on 2D photos taken from multiple views and reconstruct the individual 3D path(s) that light must have traced in order to reach each pixel. Despite the apparent intractability of this endeavor, our results suggest that reasoning about light transport can produce rich descriptions of surface geometry for objects with complex optical properties.
{"title":"Light Transport Analysis for 3D Photography","authors":"Kiriakos N. Kutulakos","doi":"10.1109/3DIM.2007.33","DOIUrl":"https://doi.org/10.1109/3DIM.2007.33","url":null,"abstract":"While 3D photography research has enjoyed tremendous success, many everyday objects and materials are still difficult or impossible to capture in 3D. An important stumbling block is that typical algorithms do not consider the effects of light transport, i.e., the sequence of bounces, refractions and scattering events that may occur when light interacts with an object. This puts objects with transparent materials or highly-reflective surfaces (clear plastic, crystal, liquids, polished metal, etc.) outside the reach of current 3D scanning techniques. To overcome these limitations, we have been investigating algorithms that explicitly analyze the light transport process caused by such objects (N. Morris and K.N. Kutulakos, 2005), (K.N. Kutulakos and E. Steger, 2005). These algorithms rely on 2D photos taken from multiple views and reconstruct the individual 3D path(s) that light must have traced in order to reach each pixel. Despite the apparent intractability of this endeavor, our results suggest that reasoning about light transport can produce rich descriptions of surface geometry for objects with complex optical properties.","PeriodicalId":442311,"journal":{"name":"Sixth International Conference on 3-D Digital Imaging and Modeling (3DIM 2007)","volume":"231 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123528179","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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