Pub Date : 2007-06-17DOI: 10.1109/CVPR.2007.383165
Brian Amberg, S. Romdhani, T. Vetter
We show how to extend the ICP framework to nonrigid registration, while retaining the convergence properties of the original algorithm. The resulting optimal step nonrigid ICP framework allows the use of different regularisations, as long as they have an adjustable stiffness parameter. The registration loops over a series of decreasing stiffness weights, and incrementally deforms the template towards the target, recovering the whole range of global and local deformations. To find the optimal deformation for a given stiffness, optimal iterative closest point steps are used. Preliminary correspondences are estimated by a nearest-point search. Then the optimal deformation of the template for these fixed correspondences and the active stiffness is calculated. Afterwards the process continues with new correspondences found by searching from the displaced template vertices. We present an algorithm using a locally affine regularisation which assigns an affine transformation to each vertex and minimises the difference in the transformation of neighbouring vertices. It is shown that for this regularisation the optimal deformation for fixed correspondences and fixed stiffness can be determined exactly and efficiently. The method succeeds for a wide range of initial conditions, and handles missing data robustly. It is compared qualitatively and quantitatively to other algorithms using synthetic examples and real world data.
{"title":"Optimal Step Nonrigid ICP Algorithms for Surface Registration","authors":"Brian Amberg, S. Romdhani, T. Vetter","doi":"10.1109/CVPR.2007.383165","DOIUrl":"https://doi.org/10.1109/CVPR.2007.383165","url":null,"abstract":"We show how to extend the ICP framework to nonrigid registration, while retaining the convergence properties of the original algorithm. The resulting optimal step nonrigid ICP framework allows the use of different regularisations, as long as they have an adjustable stiffness parameter. The registration loops over a series of decreasing stiffness weights, and incrementally deforms the template towards the target, recovering the whole range of global and local deformations. To find the optimal deformation for a given stiffness, optimal iterative closest point steps are used. Preliminary correspondences are estimated by a nearest-point search. Then the optimal deformation of the template for these fixed correspondences and the active stiffness is calculated. Afterwards the process continues with new correspondences found by searching from the displaced template vertices. We present an algorithm using a locally affine regularisation which assigns an affine transformation to each vertex and minimises the difference in the transformation of neighbouring vertices. It is shown that for this regularisation the optimal deformation for fixed correspondences and fixed stiffness can be determined exactly and efficiently. The method succeeds for a wide range of initial conditions, and handles missing data robustly. It is compared qualitatively and quantitatively to other algorithms using synthetic examples and real world data.","PeriodicalId":351008,"journal":{"name":"2007 IEEE Conference on Computer Vision and Pattern Recognition","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126122679","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 : 2007-06-17DOI: 10.1109/CVPR.2007.383063
Z. Kukelova, T. Pajdla
Epipolar geometry and relative camera pose computation are examples of tasks which can be formulated as minimal problems and solved from a minimal number of image points. Finding the solution leads to solving systems of algebraic equations. Often, these systems are not trivial and therefore special algorithms have to be designed to achieve numerical robustness and computational efficiency. In this paper we provide a solution to the problem of estimating radial distortion and epipolar geometry from eight correspondences in two images. Unlike previous algorithms, which were able to solve the problem from nine correspondences only, we enforce the determinant of the fundamental matrix be zero. This leads to a system of eight quadratic and one cubic equation in nine variables. We simplify this system by eliminating six of these variables. Then, we solve the system by finding eigenvectors of an action matrix of a suitably chosen polynomial. We show how to construct the action matrix without computing complete Grobner basis, which provides an efficient and robust solver. The quality of the solver is demonstrated on synthetic and real data.
{"title":"A minimal solution to the autocalibration of radial distortion","authors":"Z. Kukelova, T. Pajdla","doi":"10.1109/CVPR.2007.383063","DOIUrl":"https://doi.org/10.1109/CVPR.2007.383063","url":null,"abstract":"Epipolar geometry and relative camera pose computation are examples of tasks which can be formulated as minimal problems and solved from a minimal number of image points. Finding the solution leads to solving systems of algebraic equations. Often, these systems are not trivial and therefore special algorithms have to be designed to achieve numerical robustness and computational efficiency. In this paper we provide a solution to the problem of estimating radial distortion and epipolar geometry from eight correspondences in two images. Unlike previous algorithms, which were able to solve the problem from nine correspondences only, we enforce the determinant of the fundamental matrix be zero. This leads to a system of eight quadratic and one cubic equation in nine variables. We simplify this system by eliminating six of these variables. Then, we solve the system by finding eigenvectors of an action matrix of a suitably chosen polynomial. We show how to construct the action matrix without computing complete Grobner basis, which provides an efficient and robust solver. The quality of the solver is demonstrated on synthetic and real data.","PeriodicalId":351008,"journal":{"name":"2007 IEEE Conference on Computer Vision and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129482145","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 : 2007-06-17DOI: 10.1109/CVPR.2007.383257
H. Dunlop, D. Thompson, David S. Wettergreen
Geologists and planetary scientists will benefit from methods for accurate segmentation of rocks in natural scenes. However, rocks are poorly suited for current visual segmentation techniques - they exhibit diverse morphologies and have no uniform property to distinguish them from background soil. We address this challenge with a novel detection and segmentation method incorporating features from multiple scales. These features include local attributes such as texture, object attributes such as shading and two-dimensional shape, and scene attributes such as the direction of illumination. Our method uses a superpixel segmentation followed by region-merging to search for the most probable groups of superpixels. A learned model of rock appearances identifies whole rocks by scoring candidate superpixel groupings. We evaluate our method's performance on representative images from the Mars Exploration Rover catalog.
{"title":"Multi-scale Features for Detection and Segmentation of Rocks in Mars Images","authors":"H. Dunlop, D. Thompson, David S. Wettergreen","doi":"10.1109/CVPR.2007.383257","DOIUrl":"https://doi.org/10.1109/CVPR.2007.383257","url":null,"abstract":"Geologists and planetary scientists will benefit from methods for accurate segmentation of rocks in natural scenes. However, rocks are poorly suited for current visual segmentation techniques - they exhibit diverse morphologies and have no uniform property to distinguish them from background soil. We address this challenge with a novel detection and segmentation method incorporating features from multiple scales. These features include local attributes such as texture, object attributes such as shading and two-dimensional shape, and scene attributes such as the direction of illumination. Our method uses a superpixel segmentation followed by region-merging to search for the most probable groups of superpixels. A learned model of rock appearances identifies whole rocks by scoring candidate superpixel groupings. We evaluate our method's performance on representative images from the Mars Exploration Rover catalog.","PeriodicalId":351008,"journal":{"name":"2007 IEEE Conference on Computer Vision and Pattern Recognition","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129704478","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 : 2007-06-17DOI: 10.1109/CVPR.2007.383473
H. Habe, Nobuo Saeki, T. Matsuyama
This paper proposes an effective method for compensating inter-reflection in immersive projection displays (IPDs). Because IPDs project images onto a screen, which surrounds a viewer, we have perform out both geometric and photometric corrections. Our method compensates inter-reflection on the screen. It requires no special device, and approximates both diffuse and specular reflections on the screen using block-based photometric calibration.
{"title":"Inter-Reflection Compensation for Immersive Projection Display","authors":"H. Habe, Nobuo Saeki, T. Matsuyama","doi":"10.1109/CVPR.2007.383473","DOIUrl":"https://doi.org/10.1109/CVPR.2007.383473","url":null,"abstract":"This paper proposes an effective method for compensating inter-reflection in immersive projection displays (IPDs). Because IPDs project images onto a screen, which surrounds a viewer, we have perform out both geometric and photometric corrections. Our method compensates inter-reflection on the screen. It requires no special device, and approximates both diffuse and specular reflections on the screen using block-based photometric calibration.","PeriodicalId":351008,"journal":{"name":"2007 IEEE Conference on Computer Vision and Pattern Recognition","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128306584","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 : 2007-06-17DOI: 10.1109/CVPR.2007.383081
E. Arbel, H. Hel-Or
Several approaches to shadow removal in color images have been introduced in recent years. Yet these methods fail in removing shadows that are cast on curved surfaces, as well as retaining the original texture of the image in shadow boundaries, known as penumbra regions. In this paper, we propose a novel approach which effectively removes shadows from curved surfaces while retaining the textural information in the penumbra, yielding high quality shadow-free images. Our approach aims at finding scale factors to cancel the effect of shadows, including penumbra regions where illumination changes gradually. Due to the fact that surface geometry is also taken into account when computing the scale factors, our method can handle a wider range of shadow images than current state-of-the-art methods, as demonstrated by several examples.
{"title":"Texture-Preserving Shadow Removal in Color Images Containing Curved Surfaces","authors":"E. Arbel, H. Hel-Or","doi":"10.1109/CVPR.2007.383081","DOIUrl":"https://doi.org/10.1109/CVPR.2007.383081","url":null,"abstract":"Several approaches to shadow removal in color images have been introduced in recent years. Yet these methods fail in removing shadows that are cast on curved surfaces, as well as retaining the original texture of the image in shadow boundaries, known as penumbra regions. In this paper, we propose a novel approach which effectively removes shadows from curved surfaces while retaining the textural information in the penumbra, yielding high quality shadow-free images. Our approach aims at finding scale factors to cancel the effect of shadows, including penumbra regions where illumination changes gradually. Due to the fact that surface geometry is also taken into account when computing the scale factors, our method can handle a wider range of shadow images than current state-of-the-art methods, as demonstrated by several examples.","PeriodicalId":351008,"journal":{"name":"2007 IEEE Conference on Computer Vision and Pattern Recognition","volume":"27 1 Suppl 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127296230","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 : 2007-06-17DOI: 10.1109/CVPR.2007.383452
Li Zhang, Bo Wu, R. Nevatia
Use of IR images is advantageous for many surveillance applications where the systems must operate around the clock and external illumination is not always available. We investigate the methods derived from visible spectrum analysis for the task of human detection. Two feature classes (edgelets and HOG features) and two classification models(AdaBoost and SVM cascade) are extended to IR images. We find out that it is possible to get detection performance in IR images that is comparable to state-of-the-art results for visible spectrum images. It is also shown that the two domains share many features, likely originating from the silhouettes, in spite of the starkly different appearances of the two modalities.
{"title":"Pedestrian Detection in Infrared Images based on Local Shape Features","authors":"Li Zhang, Bo Wu, R. Nevatia","doi":"10.1109/CVPR.2007.383452","DOIUrl":"https://doi.org/10.1109/CVPR.2007.383452","url":null,"abstract":"Use of IR images is advantageous for many surveillance applications where the systems must operate around the clock and external illumination is not always available. We investigate the methods derived from visible spectrum analysis for the task of human detection. Two feature classes (edgelets and HOG features) and two classification models(AdaBoost and SVM cascade) are extended to IR images. We find out that it is possible to get detection performance in IR images that is comparable to state-of-the-art results for visible spectrum images. It is also shown that the two domains share many features, likely originating from the silhouettes, in spite of the starkly different appearances of the two modalities.","PeriodicalId":351008,"journal":{"name":"2007 IEEE Conference on Computer Vision and Pattern Recognition","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132386221","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 : 2007-06-17DOI: 10.1109/CVPR.2007.383425
R. Shams, P. Sadeghi, R. Kennedy
Conventional mutual information (Ml)-based registration using pixel intensities is time-consuming and ignores spatial information, which can lead to misalignment. We propose a method to overcome these limitation by acquiring initial estimates of transformation parameters. We introduce the concept of 'gradient intensity' as a measure of spatial strength of an image in a given direction. We determine the rotation parameter by maximizing the MI between gradient intensity histograms. Calculation of the gradient intensity MI function is extremely efficient. Our method is designed to be invariant to scale and translation between the images. We then obtain estimates of scale and translation parameters using methods based on the centroids of gradient images. The estimated parameters are used to initialize an optimization algorithm which is designed to converge more quickly than the standard Powell algorithm in close proximity of the minimum. Experiments show that our method significantly improves the performance of the registration task and reduces the overall computational complexity by an order of magnitude.
{"title":"Gradient Intensity: A New Mutual Information-Based Registration Method","authors":"R. Shams, P. Sadeghi, R. Kennedy","doi":"10.1109/CVPR.2007.383425","DOIUrl":"https://doi.org/10.1109/CVPR.2007.383425","url":null,"abstract":"Conventional mutual information (Ml)-based registration using pixel intensities is time-consuming and ignores spatial information, which can lead to misalignment. We propose a method to overcome these limitation by acquiring initial estimates of transformation parameters. We introduce the concept of 'gradient intensity' as a measure of spatial strength of an image in a given direction. We determine the rotation parameter by maximizing the MI between gradient intensity histograms. Calculation of the gradient intensity MI function is extremely efficient. Our method is designed to be invariant to scale and translation between the images. We then obtain estimates of scale and translation parameters using methods based on the centroids of gradient images. The estimated parameters are used to initialize an optimization algorithm which is designed to converge more quickly than the standard Powell algorithm in close proximity of the minimum. Experiments show that our method significantly improves the performance of the registration task and reduces the overall computational complexity by an order of magnitude.","PeriodicalId":351008,"journal":{"name":"2007 IEEE Conference on Computer Vision and Pattern Recognition","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132969337","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 : 2007-06-17DOI: 10.1109/CVPR.2007.383499
Zhenfeng Zhu, Yao Zhao, Hanqing Lu
Based on multi-cue integration and hierarchical SVM, we present a sequential architecture for efficient car detection under complex outdoor scene in this paper. On the low level, two novel area templates based on edge and interest-point cues respectively are first constructed, which can be applied to forming the identities of visual perception to some extent and thus utilized to reject rapidly most of the negative non-car objects at the cost of missing few of the true ones. Moreover on the high level, both global structure and local texture cues are exploited to characterize the car objects precisely. To improve the computational efficiency of general SVM, a solution approximating based two-level hierarchical SVM is proposed. The experimental results show that the integration of global structure and local texture properties provides more powerful ability in discrimination of car objects from non-car ones. The final high detection performance also contributes to the utilizing of two novel low level visual cues and the hierarchical SVM.
{"title":"Sequential Architecture for Efficient Car Detection","authors":"Zhenfeng Zhu, Yao Zhao, Hanqing Lu","doi":"10.1109/CVPR.2007.383499","DOIUrl":"https://doi.org/10.1109/CVPR.2007.383499","url":null,"abstract":"Based on multi-cue integration and hierarchical SVM, we present a sequential architecture for efficient car detection under complex outdoor scene in this paper. On the low level, two novel area templates based on edge and interest-point cues respectively are first constructed, which can be applied to forming the identities of visual perception to some extent and thus utilized to reject rapidly most of the negative non-car objects at the cost of missing few of the true ones. Moreover on the high level, both global structure and local texture cues are exploited to characterize the car objects precisely. To improve the computational efficiency of general SVM, a solution approximating based two-level hierarchical SVM is proposed. The experimental results show that the integration of global structure and local texture properties provides more powerful ability in discrimination of car objects from non-car ones. The final high detection performance also contributes to the utilizing of two novel low level visual cues and the hierarchical SVM.","PeriodicalId":351008,"journal":{"name":"2007 IEEE Conference on Computer Vision and Pattern Recognition","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132058059","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 : 2007-06-17DOI: 10.1109/CVPR.2007.383333
A. Bissacco, Stefano Soatto
We propose a cord distance in the space of dynamical models that takes into account their dynamics, including transients, output maps and input distributions. In data analysis applications, as opposed to control, the input is often not known and is inferred as part of the (blind) identification. So it is an integral part of the model that should be considered when comparing different time series. Previous work on kernel distances between dynamical models assumed either identical or independent inputs. We extend it to arbitrary distributions, highlighting connections with system identification, independent component analysis, and optimal transport. The increased modeling power is demonstrated empirically on gait classification from simple visual features.
{"title":"On the Blind Classification of Time Series","authors":"A. Bissacco, Stefano Soatto","doi":"10.1109/CVPR.2007.383333","DOIUrl":"https://doi.org/10.1109/CVPR.2007.383333","url":null,"abstract":"We propose a cord distance in the space of dynamical models that takes into account their dynamics, including transients, output maps and input distributions. In data analysis applications, as opposed to control, the input is often not known and is inferred as part of the (blind) identification. So it is an integral part of the model that should be considered when comparing different time series. Previous work on kernel distances between dynamical models assumed either identical or independent inputs. We extend it to arbitrary distributions, highlighting connections with system identification, independent component analysis, and optimal transport. The increased modeling power is demonstrated empirically on gait classification from simple visual features.","PeriodicalId":351008,"journal":{"name":"2007 IEEE Conference on Computer Vision and Pattern Recognition","volume":"276 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132209602","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 : 2007-06-17DOI: 10.1109/CVPR.2007.383352
Björn Barrois, C. Wöhler
In this study we propose an integrated approach to the problem of 3D pose estimation. The main difference to the majority of known methods is the usage of complementary image information, including intensity and polarisation state of the light reflected from the object surface, edge information, and absolute depth values obtained based on a depth from defocus approach. Our method is based on the comparison of the input image to synthetic images generated by an OpenGL-based renderer using model information about the object provided by CAD data. This comparison provides an error term which is minimised by an iterative optimisation algorithm. Although all six degrees of freedom are estimated, our method requires only a monocular camera, circumventing disadvantages of multiocular camera systems such as the need for external camera calibration. Our framework is open for the inclusion of independently acquired depth data. We evaluate our method on a toy example as well as in two realistic scenarios in the domain of industrial quality inspection. Our experiments regarding complex real-world objects located at a distance of about 0.5 m to the camera show that the algorithm achieves typical accuracies of better than 1 degree for the rotation angles, 1-2 image pixels for the lateral translations, and several millimetres or about 1 percent for the object distance.
{"title":"3D Pose Estimation Based on Multiple Monocular Cues","authors":"Björn Barrois, C. Wöhler","doi":"10.1109/CVPR.2007.383352","DOIUrl":"https://doi.org/10.1109/CVPR.2007.383352","url":null,"abstract":"In this study we propose an integrated approach to the problem of 3D pose estimation. The main difference to the majority of known methods is the usage of complementary image information, including intensity and polarisation state of the light reflected from the object surface, edge information, and absolute depth values obtained based on a depth from defocus approach. Our method is based on the comparison of the input image to synthetic images generated by an OpenGL-based renderer using model information about the object provided by CAD data. This comparison provides an error term which is minimised by an iterative optimisation algorithm. Although all six degrees of freedom are estimated, our method requires only a monocular camera, circumventing disadvantages of multiocular camera systems such as the need for external camera calibration. Our framework is open for the inclusion of independently acquired depth data. We evaluate our method on a toy example as well as in two realistic scenarios in the domain of industrial quality inspection. Our experiments regarding complex real-world objects located at a distance of about 0.5 m to the camera show that the algorithm achieves typical accuracies of better than 1 degree for the rotation angles, 1-2 image pixels for the lateral translations, and several millimetres or about 1 percent for the object distance.","PeriodicalId":351008,"journal":{"name":"2007 IEEE Conference on Computer Vision and Pattern Recognition","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132321531","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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