Pub Date : 2018-11-01DOI: 10.1109/IPTA.2018.8608163
Francis Charette Migneault, Eric Granger, F. Mokhayeri
Still-to-video face recognition (FR) is an important function in many video surveillance applications, allowing to recognize target individuals of interest appearing over a distributed network of cameras. Systems for still-to-video FR match faces captured in videos under challenging conditions against facial models, often based on a single reference still per individual. To improve robustness to intra-class variations, an adaptive visual tracker is considered for learning of a diversified face trajectory model for each person appearing in the scene. These appearance models are updated along a trajectory, and matched against the reference gallery stills of each individual enrolled to the system. Matching scores per individual are thereby accumulated over successive frames for robust spatio-temporal recognition. In a specific implementation, face trajectory models learned with a STRUCK tracker are compared to reference stills using an ensemble of SVMs per individual that are trained a priori to discriminate target reference faces (in gallery stills) versus non-target faces (in videos from the operational domain). To represent common pose and illumination variations, domain-specific face synthesis is employed to augment the number of reference stills. Experimental results obtained with this implementation on the Chokepoint video dataset indicate that the proposed system can maintain a comparably high level of accuracy versus state-of-the-art systems, yet requires a lower complexity.
{"title":"Using Adaptive Trackers for Video Face Recognition from a Single Sample Per Person","authors":"Francis Charette Migneault, Eric Granger, F. Mokhayeri","doi":"10.1109/IPTA.2018.8608163","DOIUrl":"https://doi.org/10.1109/IPTA.2018.8608163","url":null,"abstract":"Still-to-video face recognition (FR) is an important function in many video surveillance applications, allowing to recognize target individuals of interest appearing over a distributed network of cameras. Systems for still-to-video FR match faces captured in videos under challenging conditions against facial models, often based on a single reference still per individual. To improve robustness to intra-class variations, an adaptive visual tracker is considered for learning of a diversified face trajectory model for each person appearing in the scene. These appearance models are updated along a trajectory, and matched against the reference gallery stills of each individual enrolled to the system. Matching scores per individual are thereby accumulated over successive frames for robust spatio-temporal recognition. In a specific implementation, face trajectory models learned with a STRUCK tracker are compared to reference stills using an ensemble of SVMs per individual that are trained a priori to discriminate target reference faces (in gallery stills) versus non-target faces (in videos from the operational domain). To represent common pose and illumination variations, domain-specific face synthesis is employed to augment the number of reference stills. Experimental results obtained with this implementation on the Chokepoint video dataset indicate that the proposed system can maintain a comparably high level of accuracy versus state-of-the-art systems, yet requires a lower complexity.","PeriodicalId":272294,"journal":{"name":"2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134297720","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 : 2018-11-01DOI: 10.1109/IPTA.2018.8608166
Oscar Karnalim, Setia Budi, Sulaeman Santoso, E. Handoyo, Hapnes Toba, Huyen Nguyen, Vishv M. Malhotra
The rapid development in face detection study has been greatly supported by the availability of large image datasets, which provide detailed annotations of faces on images. However, among a number of publicly accessible datasets, to our best knowledge, none of them are specifically created for academic applications. In this paper, we propose a systematic method in forming an image dataset tailored for classroom environment. We also made our dataset and its exploratory analyses publicly available. Studies in computer vision for academic application, such as an automated student attendance system, would benefit from our dataset.
{"title":"FACE - Face At Classroom Environment: Dataset and Exploration","authors":"Oscar Karnalim, Setia Budi, Sulaeman Santoso, E. Handoyo, Hapnes Toba, Huyen Nguyen, Vishv M. Malhotra","doi":"10.1109/IPTA.2018.8608166","DOIUrl":"https://doi.org/10.1109/IPTA.2018.8608166","url":null,"abstract":"The rapid development in face detection study has been greatly supported by the availability of large image datasets, which provide detailed annotations of faces on images. However, among a number of publicly accessible datasets, to our best knowledge, none of them are specifically created for academic applications. In this paper, we propose a systematic method in forming an image dataset tailored for classroom environment. We also made our dataset and its exploratory analyses publicly available. Studies in computer vision for academic application, such as an automated student attendance system, would benefit from our dataset.","PeriodicalId":272294,"journal":{"name":"2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129508980","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 : 2018-11-01DOI: 10.1109/IPTA.2018.8608147
Jingjing Xiao, M. Oussalah
Color-based particle filters have emerged as an appealing method for targets tracking. As the target may undergo rapid and significant appearance changes, the template (i.e. scale of the target, color distribution histogram) also needs to be updated. Traditional updates without learning contextual information may imply a high risk of distorting the model and losing the target. In this paper, a new algorithm utilizing the environmental information to update both the scale of the tracker and the reference appearance model for the purpose of object tracking in video sequences has been put forward. The proposal makes use of the well-established color-based particle filter tracking while differentiating the foreground and background particles according to their matching score. A roaming phenomenon that yields the estimation to shrink and diverge is investigated. The proposed solution is tested using publicly available benchmark datasets where a comparison with six state-of-the-art trackers has been carried out. The results demonstrate the feasibility of the proposal and lie down foundations for further research of complex tracking problems.
{"title":"On the use of contextual information for robust colour-based particle filter tracking","authors":"Jingjing Xiao, M. Oussalah","doi":"10.1109/IPTA.2018.8608147","DOIUrl":"https://doi.org/10.1109/IPTA.2018.8608147","url":null,"abstract":"Color-based particle filters have emerged as an appealing method for targets tracking. As the target may undergo rapid and significant appearance changes, the template (i.e. scale of the target, color distribution histogram) also needs to be updated. Traditional updates without learning contextual information may imply a high risk of distorting the model and losing the target. In this paper, a new algorithm utilizing the environmental information to update both the scale of the tracker and the reference appearance model for the purpose of object tracking in video sequences has been put forward. The proposal makes use of the well-established color-based particle filter tracking while differentiating the foreground and background particles according to their matching score. A roaming phenomenon that yields the estimation to shrink and diverge is investigated. The proposed solution is tested using publicly available benchmark datasets where a comparison with six state-of-the-art trackers has been carried out. The results demonstrate the feasibility of the proposal and lie down foundations for further research of complex tracking problems.","PeriodicalId":272294,"journal":{"name":"2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128359265","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 : 2018-11-01DOI: 10.1109/IPTA.2018.8608148
Hao Chen, Hong Zheng, Ying Deng
Rapid detection of small or non-salient attacking objects constitutes the dominant technical concern for prevention of airport bird strike. According to changes of the object observed from far to near, a novel detection proposal method based on shallow feature constraints (ShallowF) is thus proposed. Specifically, the object is located approximately by virtue of feature points, narrowing search spaces, reducing the number of sampling frames, and improving the efficiency of detection proposals. Then sampling rules are specified by connected domains and feature points, further narrowing search spaces and reducing the number of sampling frames. Finally, based on the difference between the target contour and the background, the structured edge group in the bounding boxes is extracted as the scoring basis for target detection before test and validation on the COCO Bird Dataset [1] and the VOC2007 Dataset [2]. Compared with the most advanced detection proposal methods, this method can improve the accuracy of candidate bounding boxes while reducing their quantity.
{"title":"Detection proposal method based on shallow feature constraints","authors":"Hao Chen, Hong Zheng, Ying Deng","doi":"10.1109/IPTA.2018.8608148","DOIUrl":"https://doi.org/10.1109/IPTA.2018.8608148","url":null,"abstract":"Rapid detection of small or non-salient attacking objects constitutes the dominant technical concern for prevention of airport bird strike. According to changes of the object observed from far to near, a novel detection proposal method based on shallow feature constraints (ShallowF) is thus proposed. Specifically, the object is located approximately by virtue of feature points, narrowing search spaces, reducing the number of sampling frames, and improving the efficiency of detection proposals. Then sampling rules are specified by connected domains and feature points, further narrowing search spaces and reducing the number of sampling frames. Finally, based on the difference between the target contour and the background, the structured edge group in the bounding boxes is extracted as the scoring basis for target detection before test and validation on the COCO Bird Dataset [1] and the VOC2007 Dataset [2]. Compared with the most advanced detection proposal methods, this method can improve the accuracy of candidate bounding boxes while reducing their quantity.","PeriodicalId":272294,"journal":{"name":"2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123341377","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}
Image aesthetic assessment aims at computationally evaluating the quality of images based on artistic perceptions. Although existing deep learning based approaches have obtained promising performance, they typically use the high-level features in the convolutional neural networks (CNNs) for aesthetic prediction. However, low-level and intermediate-level features are also highly correlated with image aesthetic. In this paper, we propose to use multi-level features from a CNN for learning effective image aesthetic assessment models. Specially, we extract features from multi-layers and then aggregate them for predicting a image aesthetic score. To evaluate its effectiveness, we build three multilayer aggregation networks (MLANs) based on different baseline networks, including MobileNet, VGG16, and Inception-v3, respectively. Experimental results show that aggregating multilayer features consistently and considerably achieved improved performance. Besides, MLANs show significant superiority over previous state-of-the-art in the aesthetic score prediction task.
{"title":"MLANs: Image Aesthetic Assessment via Multi-Layer Aggregation Networks","authors":"Xuantong Meng, Fei Gao, Shengjie Shi, Suguo Zhu, Jingjie Zhu","doi":"10.1109/IPTA.2018.8608132","DOIUrl":"https://doi.org/10.1109/IPTA.2018.8608132","url":null,"abstract":"Image aesthetic assessment aims at computationally evaluating the quality of images based on artistic perceptions. Although existing deep learning based approaches have obtained promising performance, they typically use the high-level features in the convolutional neural networks (CNNs) for aesthetic prediction. However, low-level and intermediate-level features are also highly correlated with image aesthetic. In this paper, we propose to use multi-level features from a CNN for learning effective image aesthetic assessment models. Specially, we extract features from multi-layers and then aggregate them for predicting a image aesthetic score. To evaluate its effectiveness, we build three multilayer aggregation networks (MLANs) based on different baseline networks, including MobileNet, VGG16, and Inception-v3, respectively. Experimental results show that aggregating multilayer features consistently and considerably achieved improved performance. Besides, MLANs show significant superiority over previous state-of-the-art in the aesthetic score prediction task.","PeriodicalId":272294,"journal":{"name":"2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132355768","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 : 2018-11-01DOI: 10.1109/IPTA.2018.8608139
Jinye Peng, Kai Yu, Jun Wang, Qunxi Zhang, Cheng Liu, L. Wang
This paper proposes a method that can effectively recover pattern information from painted pottery. The first step is to create an image of the pottery using hyperspectral imaging techniques. The Minimum Noise Fraction transform (MNF) is then used to reduce the dimensionality of the hyperspectral image to obtain the principal component image. Next, we propose a pattern extraction method based on deep learning, the topic of this paper, to further enhance the process resulting in more complete pattern information. Lastly, the pattern information image is fused with a true colour image using the improved sparse representation and detail injection fusion method to obtain an image that includes both the pattern and colour information of the painted pottery. The experimental results we observed confirm this process effectively extracts the pattern information from painted pottery.
{"title":"Extracting Painted Pottery Pattern Information Based on Deep Learning","authors":"Jinye Peng, Kai Yu, Jun Wang, Qunxi Zhang, Cheng Liu, L. Wang","doi":"10.1109/IPTA.2018.8608139","DOIUrl":"https://doi.org/10.1109/IPTA.2018.8608139","url":null,"abstract":"This paper proposes a method that can effectively recover pattern information from painted pottery. The first step is to create an image of the pottery using hyperspectral imaging techniques. The Minimum Noise Fraction transform (MNF) is then used to reduce the dimensionality of the hyperspectral image to obtain the principal component image. Next, we propose a pattern extraction method based on deep learning, the topic of this paper, to further enhance the process resulting in more complete pattern information. Lastly, the pattern information image is fused with a true colour image using the improved sparse representation and detail injection fusion method to obtain an image that includes both the pattern and colour information of the painted pottery. The experimental results we observed confirm this process effectively extracts the pattern information from painted pottery.","PeriodicalId":272294,"journal":{"name":"2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122558778","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 : 2018-11-01DOI: 10.1109/IPTA.2018.8608146
Q. Péntek, T. Allouis, O. Strauss, C. Fiorio
A key feature for multi-sensor fusion is the ability to associate, to each measured value, an estimate of its uncertainty. We aim at developing a point-to-pixel association based on UAV-borne LiDAR point cloud and conventional camera data to build digital elevation models where each 3D point is associated to a color. In this paper, we propose a convenient uncertainty prediction model dedicated to multi-beam LiDAR systems with a new consideration on laser diode stack emitted footprints. We supplement this proposition by a novel reference-free evaluation method of this model. This evaluation method aims at validating the LiDAR uncertainty prediction model and estimating its resolving power. It is based on two criteria: one for consistency, the other for specificity. We apply this method to the multi-beam Velodyne VLP-16 LiDAR. The sensor’s prediction model validates the consistency criterion but, as expected, not the specificity criterion. It returns coherently pessimistic prediction with a resolving power upper bounded by 2 cm at a distance of 5 m.
{"title":"DEVELOPING AND VALIDATING A PREDICTIVE MODEL OF MEASUREMENT UNCERTAINTY FOR MULTI-BEAM LIDARS: APPLICATION TO THE VELODYNE VLP-16","authors":"Q. Péntek, T. Allouis, O. Strauss, C. Fiorio","doi":"10.1109/IPTA.2018.8608146","DOIUrl":"https://doi.org/10.1109/IPTA.2018.8608146","url":null,"abstract":"A key feature for multi-sensor fusion is the ability to associate, to each measured value, an estimate of its uncertainty. We aim at developing a point-to-pixel association based on UAV-borne LiDAR point cloud and conventional camera data to build digital elevation models where each 3D point is associated to a color. In this paper, we propose a convenient uncertainty prediction model dedicated to multi-beam LiDAR systems with a new consideration on laser diode stack emitted footprints. We supplement this proposition by a novel reference-free evaluation method of this model. This evaluation method aims at validating the LiDAR uncertainty prediction model and estimating its resolving power. It is based on two criteria: one for consistency, the other for specificity. We apply this method to the multi-beam Velodyne VLP-16 LiDAR. The sensor’s prediction model validates the consistency criterion but, as expected, not the specificity criterion. It returns coherently pessimistic prediction with a resolving power upper bounded by 2 cm at a distance of 5 m.","PeriodicalId":272294,"journal":{"name":"2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130222385","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 : 2018-11-01DOI: 10.1109/IPTA.2018.8608134
G. Orrú, G. Marcialis, F. Roli
Nowadays, facial authentication systems are present in many daily life devices. Their performance is influenced by the appearance of the facial trait that changes according to many factors such as lighting, pose, variations over time and obstructions. Adaptive systems follow these variations by updating themselves through images acquired during system operations. Although the literature proposes many possible approaches, their evaluation is often left to data set not explicitly conceived to simulate a real application scenario. The substantial absence of an appropriate and objective evaluation set is probably the motivation of the lack of implementation of adaptive systems in real devices. This paper presents a facial dataset acquired by videos in the YouTube platform. The collected images are particularly suitable for evaluating adaptive systems as they contain many changes during the time-sequence. A set of experiments of the most representative self adaptive approaches recently appeared in the literature is also performed and discussed. They allow to give some initial insights about pros and cons of facial adaptive authentication systems by considering a medium-long term time window of the investigated systems performance.
{"title":"An experimental investigation on self adaptive facial recognition algorithms using a long time span data set","authors":"G. Orrú, G. Marcialis, F. Roli","doi":"10.1109/IPTA.2018.8608134","DOIUrl":"https://doi.org/10.1109/IPTA.2018.8608134","url":null,"abstract":"Nowadays, facial authentication systems are present in many daily life devices. Their performance is influenced by the appearance of the facial trait that changes according to many factors such as lighting, pose, variations over time and obstructions. Adaptive systems follow these variations by updating themselves through images acquired during system operations. Although the literature proposes many possible approaches, their evaluation is often left to data set not explicitly conceived to simulate a real application scenario. The substantial absence of an appropriate and objective evaluation set is probably the motivation of the lack of implementation of adaptive systems in real devices. This paper presents a facial dataset acquired by videos in the YouTube platform. The collected images are particularly suitable for evaluating adaptive systems as they contain many changes during the time-sequence. A set of experiments of the most representative self adaptive approaches recently appeared in the literature is also performed and discussed. They allow to give some initial insights about pros and cons of facial adaptive authentication systems by considering a medium-long term time window of the investigated systems performance.","PeriodicalId":272294,"journal":{"name":"2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123149541","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 : 2018-11-01DOI: 10.1109/IPTA.2018.8608127
Lei Xu, Honglei Zhang, Jenni Raitoharju, M. Gabbouj
In recent years, Generative Adversarial Networks (GANs) or various types of Auto-Encoders (AEs) have gained attention on facial image de-occlusion and/or in-painting tasks. In this paper, we propose a novel unsupervised technique to remove occlusion from facial images and complete the occluded parts simultaneously with optimized Deep Convolutional Generative Adversarial Networks (DCGANs) in an iterative way. Generally, GANs, as generative models, can estimate the distribution of images using a generator and a discriminator. DCGANs, as its variant, are proposed to conquer its instability during training. Existing facial image in-painting methods manually define a block of pixels as the missing part and the potential content of this block is semantically generated using generative models, such as GANs or AEs. In our method, a mask is inferred from an occluded facial image using a novel loss function, and then this mask is utilized to in-paint the occlusions automatically by pre-trained DCGANs. We evaluate the performance of our method on facial images with various occlusions, such as sunglasses and scarves. The experiments demonstrate that our method can effectively detect certain kinds of occlusions and complete the occluded parts in an unsupervised manner.
{"title":"Unsupervised Facial Image De-occlusion with Optimized Deep Generative Models","authors":"Lei Xu, Honglei Zhang, Jenni Raitoharju, M. Gabbouj","doi":"10.1109/IPTA.2018.8608127","DOIUrl":"https://doi.org/10.1109/IPTA.2018.8608127","url":null,"abstract":"In recent years, Generative Adversarial Networks (GANs) or various types of Auto-Encoders (AEs) have gained attention on facial image de-occlusion and/or in-painting tasks. In this paper, we propose a novel unsupervised technique to remove occlusion from facial images and complete the occluded parts simultaneously with optimized Deep Convolutional Generative Adversarial Networks (DCGANs) in an iterative way. Generally, GANs, as generative models, can estimate the distribution of images using a generator and a discriminator. DCGANs, as its variant, are proposed to conquer its instability during training. Existing facial image in-painting methods manually define a block of pixels as the missing part and the potential content of this block is semantically generated using generative models, such as GANs or AEs. In our method, a mask is inferred from an occluded facial image using a novel loss function, and then this mask is utilized to in-paint the occlusions automatically by pre-trained DCGANs. We evaluate the performance of our method on facial images with various occlusions, such as sunglasses and scarves. The experiments demonstrate that our method can effectively detect certain kinds of occlusions and complete the occluded parts in an unsupervised manner.","PeriodicalId":272294,"journal":{"name":"2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"661 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127589149","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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