Pub Date : 2008-06-23DOI: 10.1109/CVPRW.2008.4563131
Anastasios I. Mourikis, S. Roumeliotis
In this paper, we present a localization algorithm for estimating the 3D position and orientation (pose) of a moving vehicle based on visual and inertial measurements. The main advantage of the proposed method is that it provides precise pose estimates at low computational cost. This is achieved by introducing a two-layer estimation architecture that processes measurements based on their information content. Inertial measurements and feature tracks between consecutive images are processed locally in the first layer (multi-state-constraint Kalman filter) providing estimates for the motion of the vehicle at a high rate. The second layer comprises a bundle adjustment iterative estimator that operates intermittently so as to (i) reduce the effect of the linearization errors, and (ii) update the state estimates every time an area is re-visited and features are re-detected (loop closure). Through this process reliable state estimates are available continuously, while the estimation errors remain bounded during long-term operation. The performance of the developed system is demonstrated in large-scale experiments, involving a vehicle localizing within an urban area.
{"title":"A dual-layer estimator architecture for long-term localization","authors":"Anastasios I. Mourikis, S. Roumeliotis","doi":"10.1109/CVPRW.2008.4563131","DOIUrl":"https://doi.org/10.1109/CVPRW.2008.4563131","url":null,"abstract":"In this paper, we present a localization algorithm for estimating the 3D position and orientation (pose) of a moving vehicle based on visual and inertial measurements. The main advantage of the proposed method is that it provides precise pose estimates at low computational cost. This is achieved by introducing a two-layer estimation architecture that processes measurements based on their information content. Inertial measurements and feature tracks between consecutive images are processed locally in the first layer (multi-state-constraint Kalman filter) providing estimates for the motion of the vehicle at a high rate. The second layer comprises a bundle adjustment iterative estimator that operates intermittently so as to (i) reduce the effect of the linearization errors, and (ii) update the state estimates every time an area is re-visited and features are re-detected (loop closure). Through this process reliable state estimates are available continuously, while the estimation errors remain bounded during long-term operation. The performance of the developed system is demonstrated in large-scale experiments, involving a vehicle localizing within an urban area.","PeriodicalId":102206,"journal":{"name":"2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124425174","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 : 2008-06-23DOI: 10.1109/CVPRW.2008.4563089
C. Zach, D. Gallup, Jan-Michael Frahm
High-performance feature tracking from video input is a valuable tool in many computer vision techniques and mixed reality applications. This work presents a refined and substantially accelerated approach to KLT feature tracking performed on the GPU. Additionally, a global gain ratio between successive frames is estimated to compensate for changes in the camera exposure. The proposed approach achieves more than 200 frames per second on state-of-the art consumer GPUs for PAL (720 times 576) resolution data, and delivers real-time performance even on low-end mobile graphics processors.
{"title":"Fast gain-adaptive KLT tracking on the GPU","authors":"C. Zach, D. Gallup, Jan-Michael Frahm","doi":"10.1109/CVPRW.2008.4563089","DOIUrl":"https://doi.org/10.1109/CVPRW.2008.4563089","url":null,"abstract":"High-performance feature tracking from video input is a valuable tool in many computer vision techniques and mixed reality applications. This work presents a refined and substantially accelerated approach to KLT feature tracking performed on the GPU. Additionally, a global gain ratio between successive frames is estimated to compensate for changes in the camera exposure. The proposed approach achieves more than 200 frames per second on state-of-the art consumer GPUs for PAL (720 times 576) resolution data, and delivers real-time performance even on low-end mobile graphics processors.","PeriodicalId":102206,"journal":{"name":"2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127970508","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 : 2008-06-23DOI: 10.1109/CVPRW.2008.4563168
Jochen Radmer, Pol Moser Fuste, H. Schmidt, J. Krüger
For various applications, such as object recognition or tracking and especially when the object is partly occluded or articulated, 3D information is crucial for the robustness of the application. A recently developed sensor to acquire distance information is based on the Photo Mixer Device (PMD)for which a distance error based on different causes can be observed. This article presents an improved distance calibration approach for PMD-based distance sensoring which handles objects with different Lambertian reflectance properties. Within this scope the relation of the sources of distance errors were investigated. Where applicable they were isolated for relational studies with the actuating variables, i.e. integration time, amplitude and measured distance, as these are the only parameters available for the calibration. The calibration results of the proposed method excel the results of all other known methods. In particular with objects with unknown reflectance properties a significant reduction of the error is achieved.
{"title":"Incident light related distance error study and calibration of the PMD-range imaging camera","authors":"Jochen Radmer, Pol Moser Fuste, H. Schmidt, J. Krüger","doi":"10.1109/CVPRW.2008.4563168","DOIUrl":"https://doi.org/10.1109/CVPRW.2008.4563168","url":null,"abstract":"For various applications, such as object recognition or tracking and especially when the object is partly occluded or articulated, 3D information is crucial for the robustness of the application. A recently developed sensor to acquire distance information is based on the Photo Mixer Device (PMD)for which a distance error based on different causes can be observed. This article presents an improved distance calibration approach for PMD-based distance sensoring which handles objects with different Lambertian reflectance properties. Within this scope the relation of the sources of distance errors were investigated. Where applicable they were isolated for relational studies with the actuating variables, i.e. integration time, amplitude and measured distance, as these are the only parameters available for the calibration. The calibration results of the proposed method excel the results of all other known methods. In particular with objects with unknown reflectance properties a significant reduction of the error is achieved.","PeriodicalId":102206,"journal":{"name":"2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127983507","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 : 2008-06-23DOI: 10.1109/CVPRW.2008.4562964
S. Barbieri, M. Welk, J. Weickert
We present a variational framework for the registration of tensor-valued images. It is based on an energy functional with four terms: a data term based on a diffusion tensor constancy constraint, a compatibility term encoding the physical model linking domain deformations and tensor reorientation, and smoothness terms for deformation and tensor reorientation. Although the tensor deformation model employed here is designed with regard to diffusion tensor MRI data, the separation of data and compatibility term allows to adapt the model easily to different tensor deformation models. We minimise the energy functional with respect to both transformation fields by a multiscale gradient descent. Experiments demonstrate the viability and potential of this approach in the registration of tensor-valued images.
{"title":"Variational registration of tensor-valued images","authors":"S. Barbieri, M. Welk, J. Weickert","doi":"10.1109/CVPRW.2008.4562964","DOIUrl":"https://doi.org/10.1109/CVPRW.2008.4562964","url":null,"abstract":"We present a variational framework for the registration of tensor-valued images. It is based on an energy functional with four terms: a data term based on a diffusion tensor constancy constraint, a compatibility term encoding the physical model linking domain deformations and tensor reorientation, and smoothness terms for deformation and tensor reorientation. Although the tensor deformation model employed here is designed with regard to diffusion tensor MRI data, the separation of data and compatibility term allows to adapt the model easily to different tensor deformation models. We minimise the energy functional with respect to both transformation fields by a multiscale gradient descent. Experiments demonstrate the viability and potential of this approach in the registration of tensor-valued images.","PeriodicalId":102206,"journal":{"name":"2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133617911","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 : 2008-06-23DOI: 10.1109/CVPRW.2008.4563070
V. Balasubramanian, S. Panchanathan, Shayok Chakraborty
An important facet of learning in an online setting is the confidence associated with a prediction on a given test data point. In an online learning scenario, it would be expected that the system can increase its confidence of prediction as training data increases. We present a statistical approach in this work to associate a confidence value with a predicted class label in an online learning scenario. Our work is based on the existing work on transductive confidence machines (TCM) [1], which provided a methodology to define a heuristic confidence measure. We applied this approach to the problem of head pose classification from face images, and extended the framework to compute a confidence value when multiple cues are extracted from images to perform classification. Our approach is based on combining the results of multiple hypotheses and obtaining an integrated p-value to validate a single test hypothesis. From our experiments on the widely accepted FERET database, we obtained results which corroborated the significance of confidence measures - particularly, in online learning approaches. We could infer from our results with transductive learning that using confidence measures in online learning could yield significant boosts in the prediction accuracy, which would be very useful in critical pattern recognition applications.
{"title":"Multiple cue integration in transductive confidence machines for head pose classification","authors":"V. Balasubramanian, S. Panchanathan, Shayok Chakraborty","doi":"10.1109/CVPRW.2008.4563070","DOIUrl":"https://doi.org/10.1109/CVPRW.2008.4563070","url":null,"abstract":"An important facet of learning in an online setting is the confidence associated with a prediction on a given test data point. In an online learning scenario, it would be expected that the system can increase its confidence of prediction as training data increases. We present a statistical approach in this work to associate a confidence value with a predicted class label in an online learning scenario. Our work is based on the existing work on transductive confidence machines (TCM) [1], which provided a methodology to define a heuristic confidence measure. We applied this approach to the problem of head pose classification from face images, and extended the framework to compute a confidence value when multiple cues are extracted from images to perform classification. Our approach is based on combining the results of multiple hypotheses and obtaining an integrated p-value to validate a single test hypothesis. From our experiments on the widely accepted FERET database, we obtained results which corroborated the significance of confidence measures - particularly, in online learning approaches. We could infer from our results with transductive learning that using confidence measures in online learning could yield significant boosts in the prediction accuracy, which would be very useful in critical pattern recognition applications.","PeriodicalId":102206,"journal":{"name":"2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116951436","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 : 2008-06-23DOI: 10.1109/CVPRW.2008.4563094
Manoj Seshadrinathan, K. Dempski
In this paper, we propose a system for the complete implementation of the advanced encryption standard (AES) for encryption and decryption of images and text on a graphics processing unit. The GPU acts as a valuable co-processor that relieves the load off the CPU. In the decryption stage, we use a novel technique to display the decrypted images and text on the screen without bringing it onto CPU memory. We also present a system for encryption and decryption of hybrid map tiles generated from GIS data sets.
{"title":"Implementation of Advanced Encryption Standard for encryption and decryption of images and text on a GPU","authors":"Manoj Seshadrinathan, K. Dempski","doi":"10.1109/CVPRW.2008.4563094","DOIUrl":"https://doi.org/10.1109/CVPRW.2008.4563094","url":null,"abstract":"In this paper, we propose a system for the complete implementation of the advanced encryption standard (AES) for encryption and decryption of images and text on a graphics processing unit. The GPU acts as a valuable co-processor that relieves the load off the CPU. In the decryption stage, we use a novel technique to display the decrypted images and text on the screen without bringing it onto CPU memory. We also present a system for encryption and decryption of hybrid map tiles generated from GIS data sets.","PeriodicalId":102206,"journal":{"name":"2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134185389","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 : 2008-06-23DOI: 10.1109/CVPRW.2008.4563172
Marvin Lindner, A. Kolb, T. Ringbeck
Time-of-flight (ToF) sensors have become an alternative to conventional distance sensing techniques like laser scanners or image based stereo. ToF sensors provide full range distance information at high frame-rates and thus have a significant impact onto current research in areas like online object recognition, collision prevention or scene reconstruction. However, ToF cameras like the photonic mixer device (PMD) still exhibit a number of challenges regarding static and dynamic effects, e.g. systematic distance errors and motion artefacts, respectively. Sensor calibration techniques reducing static system errors have been proposed and show promising results. However, current calibration techniques in general need a large set of reference data in order to determine the corresponding parameters for the calibration model. This paper introduces a new calibration approach which combines different demodulation techniques for the ToF- camera 's reference signal. Examples show, that the resulting combined demodulation technique yields improved distance values based on only two required reference data sets.
{"title":"New insights into the calibration of ToF-sensors","authors":"Marvin Lindner, A. Kolb, T. Ringbeck","doi":"10.1109/CVPRW.2008.4563172","DOIUrl":"https://doi.org/10.1109/CVPRW.2008.4563172","url":null,"abstract":"Time-of-flight (ToF) sensors have become an alternative to conventional distance sensing techniques like laser scanners or image based stereo. ToF sensors provide full range distance information at high frame-rates and thus have a significant impact onto current research in areas like online object recognition, collision prevention or scene reconstruction. However, ToF cameras like the photonic mixer device (PMD) still exhibit a number of challenges regarding static and dynamic effects, e.g. systematic distance errors and motion artefacts, respectively. Sensor calibration techniques reducing static system errors have been proposed and show promising results. However, current calibration techniques in general need a large set of reference data in order to determine the corresponding parameters for the calibration model. This paper introduces a new calibration approach which combines different demodulation techniques for the ToF- camera 's reference signal. Examples show, that the resulting combined demodulation technique yields improved distance values based on only two required reference data sets.","PeriodicalId":102206,"journal":{"name":"2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":"29 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113933669","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 : 2008-06-23DOI: 10.1109/CVPRW.2008.4563033
Theodore C. Yapo, C. Stewart, R. Radke
We present a novel approach to 3D object detection in scenes scanned by LiDAR sensors, based on a probabilistic representation of free, occupied, and hidden space that extends the concept of occupancy grids from robot mapping algorithms. This scene representation naturally handles LiDAR sampling issues, can be used to fuse multiple LiDAR data sets, and captures the inherent uncertainty of the data due to occlusions and clutter. Using this model, we formulate a hypothesis testing methodology to determine the probability that given 3D objects are present in the scene. By propagating uncertainty in the original sample points, we are able to measure confidence in the detection results in a principled way. We demonstrate the approach in examples of detecting objects that are partially occluded by scene clutter such as camouflage netting.
{"title":"A probabilistic representation of LiDAR range data for efficient 3D object detection","authors":"Theodore C. Yapo, C. Stewart, R. Radke","doi":"10.1109/CVPRW.2008.4563033","DOIUrl":"https://doi.org/10.1109/CVPRW.2008.4563033","url":null,"abstract":"We present a novel approach to 3D object detection in scenes scanned by LiDAR sensors, based on a probabilistic representation of free, occupied, and hidden space that extends the concept of occupancy grids from robot mapping algorithms. This scene representation naturally handles LiDAR sampling issues, can be used to fuse multiple LiDAR data sets, and captures the inherent uncertainty of the data due to occlusions and clutter. Using this model, we formulate a hypothesis testing methodology to determine the probability that given 3D objects are present in the scene. By propagating uncertainty in the original sample points, we are able to measure confidence in the detection results in a principled way. We demonstrate the approach in examples of detecting objects that are partially occluded by scene clutter such as camouflage netting.","PeriodicalId":102206,"journal":{"name":"2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123875887","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 : 2008-06-23DOI: 10.1109/CVPRW.2008.4563115
K. Kollreider, H. Fronthaler, J. Bigün
Resisting spoofing attempts via photographs and video playbacks is a vital issue for the success of face biometrics. Yet, the ldquolivenessrdquo topic has only been partially studied in the past. In this paper we are suggesting a holistic liveness detection paradigm that collaborates with standard techniques in 2D face biometrics. The experiments show that many attacks are avertible via a combination of anti-spoofing measures. We have investigated the topic using real-time techniques and applied them to real-life spoofing scenarios in an indoor, yet uncontrolled environment.
{"title":"Verifying liveness by multiple experts in face biometrics","authors":"K. Kollreider, H. Fronthaler, J. Bigün","doi":"10.1109/CVPRW.2008.4563115","DOIUrl":"https://doi.org/10.1109/CVPRW.2008.4563115","url":null,"abstract":"Resisting spoofing attempts via photographs and video playbacks is a vital issue for the success of face biometrics. Yet, the ldquolivenessrdquo topic has only been partially studied in the past. In this paper we are suggesting a holistic liveness detection paradigm that collaborates with standard techniques in 2D face biometrics. The experiments show that many attacks are avertible via a combination of anti-spoofing measures. We have investigated the topic using real-time techniques and applied them to real-life spoofing scenarios in an indoor, yet uncontrolled environment.","PeriodicalId":102206,"journal":{"name":"2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127709336","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 : 2008-06-23DOI: 10.1109/CVPRW.2008.4562958
Tom Yeh, John J. Lee, Trevor Darrell
Object recognition systems designed for Internet applications typically need to adapt to userspsila needs in a flexible fashion and scale up to very large data sets. In this paper, we analyze the complexity of several multiclass SVM-based algorithms and highlight the computational bottleneck they suffer at test time: comparing the input image to every training image. We propose an algorithm that overcomes this bottleneck; it offers not only the efficiency of a simple nearest-neighbor classifier, by voting on class labels based on the k nearest neighbors quickly determined by a vocabulary tree, but also the recognition accuracy comparable to that of a complex SVM classifier, by incorporating SVM parameters into the voting scores incrementally accumulated from individual image features. Empirical results demonstrate that adjusting votes by relevant support vector weights can improve the recognition accuracy of a nearest-neighbor classifier without sacrificing speed. Compared to existing methods, our algorithm achieves a ten-fold speed increase while incurring an acceptable accuracy loss that can be easily offset by showing about two more labels in the result. The speed, scalability, and adaptability of our algorithm makes it suitable for Internet vision applications.
{"title":"Scalable classifiers for Internet vision tasks","authors":"Tom Yeh, John J. Lee, Trevor Darrell","doi":"10.1109/CVPRW.2008.4562958","DOIUrl":"https://doi.org/10.1109/CVPRW.2008.4562958","url":null,"abstract":"Object recognition systems designed for Internet applications typically need to adapt to userspsila needs in a flexible fashion and scale up to very large data sets. In this paper, we analyze the complexity of several multiclass SVM-based algorithms and highlight the computational bottleneck they suffer at test time: comparing the input image to every training image. We propose an algorithm that overcomes this bottleneck; it offers not only the efficiency of a simple nearest-neighbor classifier, by voting on class labels based on the k nearest neighbors quickly determined by a vocabulary tree, but also the recognition accuracy comparable to that of a complex SVM classifier, by incorporating SVM parameters into the voting scores incrementally accumulated from individual image features. Empirical results demonstrate that adjusting votes by relevant support vector weights can improve the recognition accuracy of a nearest-neighbor classifier without sacrificing speed. Compared to existing methods, our algorithm achieves a ten-fold speed increase while incurring an acceptable accuracy loss that can be easily offset by showing about two more labels in the result. The speed, scalability, and adaptability of our algorithm makes it suitable for Internet vision applications.","PeriodicalId":102206,"journal":{"name":"2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127797960","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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