Pub Date : 2014-06-08DOI: 10.1109/IVS.2014.6856402
Moritz Knorr, W. Niehsen, C. Stiller
Knowledge of motion with respect to the ground plane is required in many computer vision applications such as obstacle avoidance, egomotion estimation, and online calibration. The homography matrix comprises motion as well as ground plane information. Estimation of the homography matrix is challenging, as measurements are often not only corrupted by sparse gross outliers, but might also contain other structures, which are inconsistent with the ground plane such as curbstones and sidewalks. Several well studied algorithms regarding the identification of sparse gross outliers already exist. However, identifying structural outliers remains a challenging problem due the outliers' inner coherence. In homography and plane estimation structural outliers often cause plane fits that do not correspond to any physical plane in the scene. We make use of the large field of view of fisheye cameras by exploiting that outlier identification can be performed more robustly in the near field where motion parallax vectors are large. More sensitive data can then be tested subsequently based on the preceding results. The main contribution of this paper is twofold. First, we present a statistical analysis of parallax amplitudes that are to be expected due to the distance of a point from the ground plane and measurement noise. This leads to a statistical test for outliers with local adaptive thresholds. Second, we embed this concept into an extended Kalman filter for efficient processing. Furthermore, we emphasize the importance of warping captured images into a common frame previous to feature detection and matching to avoid distortion effects and to equalize search regions. We demonstrate the robustness of our approach and the effects of prewarping on the estimation using real data.
{"title":"Robust ground plane induced homography estimation for wide angle fisheye cameras","authors":"Moritz Knorr, W. Niehsen, C. Stiller","doi":"10.1109/IVS.2014.6856402","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856402","url":null,"abstract":"Knowledge of motion with respect to the ground plane is required in many computer vision applications such as obstacle avoidance, egomotion estimation, and online calibration. The homography matrix comprises motion as well as ground plane information. Estimation of the homography matrix is challenging, as measurements are often not only corrupted by sparse gross outliers, but might also contain other structures, which are inconsistent with the ground plane such as curbstones and sidewalks. Several well studied algorithms regarding the identification of sparse gross outliers already exist. However, identifying structural outliers remains a challenging problem due the outliers' inner coherence. In homography and plane estimation structural outliers often cause plane fits that do not correspond to any physical plane in the scene. We make use of the large field of view of fisheye cameras by exploiting that outlier identification can be performed more robustly in the near field where motion parallax vectors are large. More sensitive data can then be tested subsequently based on the preceding results. The main contribution of this paper is twofold. First, we present a statistical analysis of parallax amplitudes that are to be expected due to the distance of a point from the ground plane and measurement noise. This leads to a statistical test for outliers with local adaptive thresholds. Second, we embed this concept into an extended Kalman filter for efficient processing. Furthermore, we emphasize the importance of warping captured images into a common frame previous to feature detection and matching to avoid distortion effects and to equalize search regions. We demonstrate the robustness of our approach and the effects of prewarping on the estimation using real data.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117272188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-06-08DOI: 10.1109/IVS.2014.6856543
Yue Kang, Danilo Alves de Lima, A. Victorino
This paper presents the approach of an applicable safety driving methodology for human drivers based on Dynamic Window Approach (DWA), as an implementation of Advanced Driving Assist Systems (ADASs). The human driving behaviors are modelled for the design of controller, refined by referential paths using evasive trajectory model, and the linear and angular velocities are limited and corrected by DWA which performed as an obstacle avoidance strategy. Results of trajectory following and obstacle avoidance are compared with the Visual Servoing (VS) controller as a corresponding approach of autonomous control pattern.
{"title":"An approach of human driving behavior correction based on Dynamic Window Approach","authors":"Yue Kang, Danilo Alves de Lima, A. Victorino","doi":"10.1109/IVS.2014.6856543","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856543","url":null,"abstract":"This paper presents the approach of an applicable safety driving methodology for human drivers based on Dynamic Window Approach (DWA), as an implementation of Advanced Driving Assist Systems (ADASs). The human driving behaviors are modelled for the design of controller, refined by referential paths using evasive trajectory model, and the linear and angular velocities are limited and corrected by DWA which performed as an obstacle avoidance strategy. Results of trajectory following and obstacle avoidance are compared with the Visual Servoing (VS) controller as a corresponding approach of autonomous control pattern.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122097108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-06-08DOI: 10.1109/IVS.2014.6856447
Daniel Hultqvist, Jacob Roll, Fredrik Svensson, J. Dahlin, Thomas Bo Schön
We are concerned with the problem of detecting an overtaking vehicle using a single camera mounted behind the ego-vehicle windscreen. The proposed solution makes use of 1D optical flow evaluated along lines parallel to the motion of the overtaking vehicles. The 1D optical flow is computed by tracking features along these lines. Based on these features, the position of the overtaking vehicle can also be estimated. The proposed solution has been implemented and tested in real time with promising results. The video data was recorded during test drives in normal traffic conditions in Sweden and Germany.
{"title":"Detecting and positioning overtaking vehicles using 1D optical flow","authors":"Daniel Hultqvist, Jacob Roll, Fredrik Svensson, J. Dahlin, Thomas Bo Schön","doi":"10.1109/IVS.2014.6856447","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856447","url":null,"abstract":"We are concerned with the problem of detecting an overtaking vehicle using a single camera mounted behind the ego-vehicle windscreen. The proposed solution makes use of 1D optical flow evaluated along lines parallel to the motion of the overtaking vehicles. The 1D optical flow is computed by tracking features along these lines. Based on these features, the position of the overtaking vehicle can also be estimated. The proposed solution has been implemented and tested in real time with promising results. The video data was recorded during test drives in normal traffic conditions in Sweden and Germany.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116879791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-06-08DOI: 10.1109/IVS.2014.6856502
J. Hudeček, L. Eckstein
Short term motion planners for automated vehicles typically require a reference path as input to optimize the ride quality between distinct vehicle states. This paper presents a novel approach to simplify the generation of such reference paths. It is based on the idea of converting the driving route and all relevant objects from the current vehicle's environment into a road-aligned coordinate system eliminating road's curvature. Based on this, a suitable path using geometric primitives can be constructed, which is then converted back into the original coordinate system. When considered during generation, the resulting reference path guarantees to respect vehicle kinodynamics and is checked against collisions.
{"title":"Improving and simplifying the generation of reference trajectories by usage of road-aligned coordinate systems","authors":"J. Hudeček, L. Eckstein","doi":"10.1109/IVS.2014.6856502","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856502","url":null,"abstract":"Short term motion planners for automated vehicles typically require a reference path as input to optimize the ride quality between distinct vehicle states. This paper presents a novel approach to simplify the generation of such reference paths. It is based on the idea of converting the driving route and all relevant objects from the current vehicle's environment into a road-aligned coordinate system eliminating road's curvature. Based on this, a suitable path using geometric primitives can be constructed, which is then converted back into the original coordinate system. When considered during generation, the resulting reference path guarantees to respect vehicle kinodynamics and is checked against collisions.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"314 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123925862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-06-08DOI: 10.1109/IVS.2014.6856533
Rawia Mhiri, P. Vasseur, S. Mousset, R. Boutteau, A. Bensrhair
This paper presents a visual odometry with metric scale estimation of a multi-camera system in challenging un-synchronized setup. The intended application is in the field of intelligent vehicles. We propose a new algorithm named “triangle-based” method. The proposed algorithm employs the information from both extrinsic and intrinsic parameters of calibrated cameras. We assume that the trajectory between two consecutive frames of a camera is a linear segment (straight trajectory). The relative camera poses are estimated via classical Structure-from-Motion. Then, the scale factors are computed by imposing the known extrinsic parameters and the linearity assumption. We verify the validity of our method both in simulated and real conditions. For the real world, the motion trajectory estimated for image sequence of two cameras from KITTI dataset is compared against the GPS/INS ground truth.
{"title":"Visual odometry with unsynchronized multi-cameras setup for intelligent vehicle application","authors":"Rawia Mhiri, P. Vasseur, S. Mousset, R. Boutteau, A. Bensrhair","doi":"10.1109/IVS.2014.6856533","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856533","url":null,"abstract":"This paper presents a visual odometry with metric scale estimation of a multi-camera system in challenging un-synchronized setup. The intended application is in the field of intelligent vehicles. We propose a new algorithm named “triangle-based” method. The proposed algorithm employs the information from both extrinsic and intrinsic parameters of calibrated cameras. We assume that the trajectory between two consecutive frames of a camera is a linear segment (straight trajectory). The relative camera poses are estimated via classical Structure-from-Motion. Then, the scale factors are computed by imposing the known extrinsic parameters and the linearity assumption. We verify the validity of our method both in simulated and real conditions. For the real world, the motion trajectory estimated for image sequence of two cameras from KITTI dataset is compared against the GPS/INS ground truth.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126119489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-06-08DOI: 10.1109/IVS.2014.6856414
Nils Einecke, J. Eggert
In this paper, we present a new scheme for block-matching stereo. The main intention is to relax the inherent assumption that within a block the disparities are constant, because this assumption is often violated. Instead of using the matching cost of one disparity within a matching block, the best matching for several disparities are first selected for each pixel and then these best matches are combined to the final block-matching value. Results on the KITTI benchmark show that this scheme increases the performance of block-matching stereo especially for large matching windows, however, there is also a significant increase for smaller block sizes. Furthermore, we show that a straightforward combination with the appearance-aligned block-matching stereo leads to results that surpass the performance of both single techniques.
{"title":"Block-matching stereo with relaxed fronto-parallel assumption","authors":"Nils Einecke, J. Eggert","doi":"10.1109/IVS.2014.6856414","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856414","url":null,"abstract":"In this paper, we present a new scheme for block-matching stereo. The main intention is to relax the inherent assumption that within a block the disparities are constant, because this assumption is often violated. Instead of using the matching cost of one disparity within a matching block, the best matching for several disparities are first selected for each pixel and then these best matches are combined to the final block-matching value. Results on the KITTI benchmark show that this scheme increases the performance of block-matching stereo especially for large matching windows, however, there is also a significant increase for smaller block sizes. Furthermore, we show that a straightforward combination with the appearance-aligned block-matching stereo leads to results that surpass the performance of both single techniques.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125069155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-06-08DOI: 10.1109/IVS.2014.6856556
Simon Ulbrich, T. Nothdurft, M. Maurer, P. Hecker
The Stadtpilot project aims at fully automated driving on Braunschweig's inner city ring road. The TU Braunschweig's research vehicle Leonie is one of the first vehicles having the ability of fully automated driving in real urban traffic scenarios. In this paper, we present our approaches for context representation and environment modeling for automated driving. The demonstrated approach allows to provide a simple and yet universal information storage layer for the development of complex driving applications. Moreover, we present our approach for aggregating and fusing information between dynamic traffic objects detected by the sensor systems and a-priori map information.
{"title":"Graph-based context representation, environment modeling and information aggregation for automated driving","authors":"Simon Ulbrich, T. Nothdurft, M. Maurer, P. Hecker","doi":"10.1109/IVS.2014.6856556","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856556","url":null,"abstract":"The Stadtpilot project aims at fully automated driving on Braunschweig's inner city ring road. The TU Braunschweig's research vehicle Leonie is one of the first vehicles having the ability of fully automated driving in real urban traffic scenarios. In this paper, we present our approaches for context representation and environment modeling for automated driving. The demonstrated approach allows to provide a simple and yet universal information storage layer for the development of complex driving applications. Moreover, we present our approach for aggregating and fusing information between dynamic traffic objects detected by the sensor systems and a-priori map information.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129443067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-06-08DOI: 10.1109/IVS.2014.6856426
Yi Yang, Yan Guang, Hao Zhu, M. Fu, Meiling Wang
We proposed an unsupervised algorithm to extract profile features and detect moving object under dynamic background in 3D range Data. Moving object detection under dynamic background has become an increasingly popular research topic in mobile robotics. For the characteristics of dynamic background scene, we proposed an online unsupervised moving object detection algorithm, based on Gaussian Mixture Models and Motion Compensation. Furthermore, we did the work of clustering and identifying of the targets. In order to improve the robustness of the algorithm, we used a tracker to track the results of the detection. At last, experimental results on real laser data depicting urban and rural scenes under static and dynamic background are presented.
{"title":"Moving object detection under dynamic background in 3D range data","authors":"Yi Yang, Yan Guang, Hao Zhu, M. Fu, Meiling Wang","doi":"10.1109/IVS.2014.6856426","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856426","url":null,"abstract":"We proposed an unsupervised algorithm to extract profile features and detect moving object under dynamic background in 3D range Data. Moving object detection under dynamic background has become an increasingly popular research topic in mobile robotics. For the characteristics of dynamic background scene, we proposed an online unsupervised moving object detection algorithm, based on Gaussian Mixture Models and Motion Compensation. Furthermore, we did the work of clustering and identifying of the targets. In order to improve the robustness of the algorithm, we used a tracker to track the results of the detection. At last, experimental results on real laser data depicting urban and rural scenes under static and dynamic background are presented.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128631540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-06-08DOI: 10.1109/IVS.2014.6856429
Dongxiu Ou, Yuchen Yang, Lixia Xue, T. Shen, Lun Zhang
Vehicular technologies have been recently introduced to highway safety applications and caught much attention of governments and management authorities. The vehicular ad hoc network (VANET) uses cars as mobile nodes to form a mobile network. VANET offers potential and promising technical solutions to vehicular safety. Multi-hop broadcasting schemes are particularly preferred methods to transmit time-sensitive safety warning information to potentially influenced vehicles. However, broadcasting may lead to frequent contention and serious collision, thus causing broadcast storms. In order to alleviate broadcast storm and promptly disseminate safety warnings, the paper proposes an optimal forwarder selection scheme to minimize the number of rebroadcasting nodes and guarantee fast and efficient safety warning information dissemination. The proposed forwarder selection scheme is based on the analytic hierarchy process (AHP). The criteria include inter-vehicular lateral and longitudinal distances, vehicular communication ranges, and vehicles covered within the communication range of previous forwarder vehicles. The AHP-based forwarder selection model is established and model's validity is verified mathematically in this paper.
{"title":"The analytic hierarchy process-based optimal forwarder selection in multi-hop broadcasting scheme for vehicular safety","authors":"Dongxiu Ou, Yuchen Yang, Lixia Xue, T. Shen, Lun Zhang","doi":"10.1109/IVS.2014.6856429","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856429","url":null,"abstract":"Vehicular technologies have been recently introduced to highway safety applications and caught much attention of governments and management authorities. The vehicular ad hoc network (VANET) uses cars as mobile nodes to form a mobile network. VANET offers potential and promising technical solutions to vehicular safety. Multi-hop broadcasting schemes are particularly preferred methods to transmit time-sensitive safety warning information to potentially influenced vehicles. However, broadcasting may lead to frequent contention and serious collision, thus causing broadcast storms. In order to alleviate broadcast storm and promptly disseminate safety warnings, the paper proposes an optimal forwarder selection scheme to minimize the number of rebroadcasting nodes and guarantee fast and efficient safety warning information dissemination. The proposed forwarder selection scheme is based on the analytic hierarchy process (AHP). The criteria include inter-vehicular lateral and longitudinal distances, vehicular communication ranges, and vehicles covered within the communication range of previous forwarder vehicles. The AHP-based forwarder selection model is established and model's validity is verified mathematically in this paper.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129544730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-06-08DOI: 10.1109/IVS.2014.6856495
Hojun Kim, Kyoungah Choi, Impyeong Lee
Nowadays cars are equipped with various built-in sensors such as speedometers, odometers, accelerometers, and gyros for safety and maintenance. Also, front view images can be economically acquired by a low-cost camera available in smartphones or black boxes. The combination of the built-in sensory data and the images can be an effective complement to a GPS based navigation. Therefore, we propose a car navigation framework to determine car position and attitude using the built-in sensory data such as a speed, angular rate and the images from a front view camera. The method consists of three steps, 1) dead reckoning using the velocity and yaw rate provided in real-time, 2) image georeferencing based on a sequential bundle adjustment using the dead reckoning results and 3) final estimation using a Kalman filter with the georeferencing results. The experimental results show that the proposed method can provide the positions with a reasonable accuracy level, which can be meaningful to complement a traditional GPS based navigation with a low cost.
{"title":"High accurate affordable car navigation using built-in sensory data and images acquired from a front view camera","authors":"Hojun Kim, Kyoungah Choi, Impyeong Lee","doi":"10.1109/IVS.2014.6856495","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856495","url":null,"abstract":"Nowadays cars are equipped with various built-in sensors such as speedometers, odometers, accelerometers, and gyros for safety and maintenance. Also, front view images can be economically acquired by a low-cost camera available in smartphones or black boxes. The combination of the built-in sensory data and the images can be an effective complement to a GPS based navigation. Therefore, we propose a car navigation framework to determine car position and attitude using the built-in sensory data such as a speed, angular rate and the images from a front view camera. The method consists of three steps, 1) dead reckoning using the velocity and yaw rate provided in real-time, 2) image georeferencing based on a sequential bundle adjustment using the dead reckoning results and 3) final estimation using a Kalman filter with the georeferencing results. The experimental results show that the proposed method can provide the positions with a reasonable accuracy level, which can be meaningful to complement a traditional GPS based navigation with a low cost.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129126018","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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