Pub Date : 2018-06-01DOI: 10.1109/ICIVC.2018.8492794
Huasheng Zhu, Jun Wang, Kaiyan Xie, Jun Ye
Existing detection algorithms of vehicle flow in video detect moving objects by per pixel, so they may be corrupted by noises and the computational costs are high. In this paper, we propose a robust moving vehicle detection algorithm with background dictionary learning. An improved vehicle flow detection algorithm based on virtual regions and virtual lines is presented. To do this, we firstly divide an image into multiple image patches that have the same sizes. Each patch is an object or a background. Then, a background dictionary is learnt for each patch. The similarity between a patch and the background dictionary is measured, upon which a patch is distinguished as an object or a background. Additionally, the virtual detection line is used and combined into the virtual regions to detect the vehicles. Experimental results demonstrate the real-time and accuracy of the proposed detection algorithm.
{"title":"Detection of Vehicle Flow in Video Surveillance","authors":"Huasheng Zhu, Jun Wang, Kaiyan Xie, Jun Ye","doi":"10.1109/ICIVC.2018.8492794","DOIUrl":"https://doi.org/10.1109/ICIVC.2018.8492794","url":null,"abstract":"Existing detection algorithms of vehicle flow in video detect moving objects by per pixel, so they may be corrupted by noises and the computational costs are high. In this paper, we propose a robust moving vehicle detection algorithm with background dictionary learning. An improved vehicle flow detection algorithm based on virtual regions and virtual lines is presented. To do this, we firstly divide an image into multiple image patches that have the same sizes. Each patch is an object or a background. Then, a background dictionary is learnt for each patch. The similarity between a patch and the background dictionary is measured, upon which a patch is distinguished as an object or a background. Additionally, the virtual detection line is used and combined into the virtual regions to detect the vehicles. Experimental results demonstrate the real-time and accuracy of the proposed detection algorithm.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130097183","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}
Most popular color constancy algorithms assume that the light source obeys a uniform distribution across the scene. However, in the real world, the illuminations can vary a lot according to their spatial distribution. To overcome this problem, in this paper, we adopt a method based on a full end-to-end deep neural model to directly learn a mapping from the original image to the corresponding well-colored image. With this formulation, the network is able to determine pixel-wise illumination and produce a final visually compelling image. The training and evaluation of the network were performed on a standard dataset of two-dominant-illuminants. In this dataset, this approach achieves state-of-the-art performance. Besides, the main architecture of the network simply consists of a stack of fully convolutional blocks which can take the input of arbitrary size and produce correspondingly-sized output with effective learning. The experimental result shows that our customized loss function can help to reach a better performance than simply using MSE.
{"title":"Multiple Illumination Estimation with End-to-End Network","authors":"Shen Yan, Feiyue Peng, Hanlin Tan, Shiming Lai, Maojun Zhang","doi":"10.1109/ICIVC.2018.8492879","DOIUrl":"https://doi.org/10.1109/ICIVC.2018.8492879","url":null,"abstract":"Most popular color constancy algorithms assume that the light source obeys a uniform distribution across the scene. However, in the real world, the illuminations can vary a lot according to their spatial distribution. To overcome this problem, in this paper, we adopt a method based on a full end-to-end deep neural model to directly learn a mapping from the original image to the corresponding well-colored image. With this formulation, the network is able to determine pixel-wise illumination and produce a final visually compelling image. The training and evaluation of the network were performed on a standard dataset of two-dominant-illuminants. In this dataset, this approach achieves state-of-the-art performance. Besides, the main architecture of the network simply consists of a stack of fully convolutional blocks which can take the input of arbitrary size and produce correspondingly-sized output with effective learning. The experimental result shows that our customized loss function can help to reach a better performance than simply using MSE.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126415824","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 : 2018-06-01DOI: 10.1109/ICIVC.2018.8492808
Su Jian, Zheng Mingyue, C. Yun
A set of FPGA software system for video signal processing circuit is designed in this paper. The system uses FPGA as the core logic control and uses high speed serial transceiver chip tlk2711 as the interface of data transmission. This paper describes the main components of the software and the realization method of the modular design of FPGA, and gives the simulation waveforms and debug results of the main modules. The test results show that the data transmission interfaces provided by the system have a data rate of 6.4Gbps, which can meet the testing requirements of the satellite camera video processing functions and greatly improve the data transmission rate and accuracy.
{"title":"Software Design of Video Signal Processing Circuit Based on FPGA","authors":"Su Jian, Zheng Mingyue, C. Yun","doi":"10.1109/ICIVC.2018.8492808","DOIUrl":"https://doi.org/10.1109/ICIVC.2018.8492808","url":null,"abstract":"A set of FPGA software system for video signal processing circuit is designed in this paper. The system uses FPGA as the core logic control and uses high speed serial transceiver chip tlk2711 as the interface of data transmission. This paper describes the main components of the software and the realization method of the modular design of FPGA, and gives the simulation waveforms and debug results of the main modules. The test results show that the data transmission interfaces provided by the system have a data rate of 6.4Gbps, which can meet the testing requirements of the satellite camera video processing functions and greatly improve the data transmission rate and accuracy.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"129-132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121035779","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 : 2018-06-01DOI: 10.1109/ICIVC.2018.8492880
Wenzhen Nie, Pengyu Liu, Ke-bin Jia, Huimin Liao, Xunping Huang
This article aims at Beijing Traffic Enforcement Corps off-site law enforcement issues. This paper proposes a novel detection method for taxi license plate block. First of all, selecting the taxi from the social vehicle; Secondly, Adaptive Boosting (Adaboost) algorithm will be used to train the license plate to locate the license plate of the taxi; eventually the adaptive threshold method will be used to judge the license plate blockage and take the evidence. The existing research on the license plate is mainly on the license plate recognition, but this article is based on the license plate recognition, and then to achieve the license plate block detection and evidence collection, for traffic law enforcement officers to punish the illegal taxi. The experimental results show that the proposed detection method of license plate block is effective.
{"title":"Taxi License Plate Block Detection Based on Complex Environment","authors":"Wenzhen Nie, Pengyu Liu, Ke-bin Jia, Huimin Liao, Xunping Huang","doi":"10.1109/ICIVC.2018.8492880","DOIUrl":"https://doi.org/10.1109/ICIVC.2018.8492880","url":null,"abstract":"This article aims at Beijing Traffic Enforcement Corps off-site law enforcement issues. This paper proposes a novel detection method for taxi license plate block. First of all, selecting the taxi from the social vehicle; Secondly, Adaptive Boosting (Adaboost) algorithm will be used to train the license plate to locate the license plate of the taxi; eventually the adaptive threshold method will be used to judge the license plate blockage and take the evidence. The existing research on the license plate is mainly on the license plate recognition, but this article is based on the license plate recognition, and then to achieve the license plate block detection and evidence collection, for traffic law enforcement officers to punish the illegal taxi. The experimental results show that the proposed detection method of license plate block is effective.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"202 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122447566","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 : 2018-06-01DOI: 10.1109/ICIVC.2018.8492860
Xu Zhang, W. Pan, P. Xiao
Skin capacitive imaging is a novel technique which has been developed for skin hydration and skin solvent penetration measurements. This research is to assess the performance of deep learning in in-vivo skin capacitive image analysis using AlexNet model. The image classifier has been trained by using pretrained model to implement the specific feature extraction, prediction and classification specifically for the skin characteristics such as hydration level, skin damage level etc. There are over 1000 skin capacitive images used in this study. The objectives of the research are: feature extraction implementation using the pretrained model AlexNet; accuracy assessment of the model; and further improve the system for multiple features classification. The image classification programme shows a good result which has accuracy over 0.98, and the test images were classified correctly comparing with the experimental results of skin hydration, skin damaged level and the gender of the volunteers.
{"title":"In-Vivo Skin Capacitive Image Classification Using AlexNet Convolution Neural Network","authors":"Xu Zhang, W. Pan, P. Xiao","doi":"10.1109/ICIVC.2018.8492860","DOIUrl":"https://doi.org/10.1109/ICIVC.2018.8492860","url":null,"abstract":"Skin capacitive imaging is a novel technique which has been developed for skin hydration and skin solvent penetration measurements. This research is to assess the performance of deep learning in in-vivo skin capacitive image analysis using AlexNet model. The image classifier has been trained by using pretrained model to implement the specific feature extraction, prediction and classification specifically for the skin characteristics such as hydration level, skin damage level etc. There are over 1000 skin capacitive images used in this study. The objectives of the research are: feature extraction implementation using the pretrained model AlexNet; accuracy assessment of the model; and further improve the system for multiple features classification. The image classification programme shows a good result which has accuracy over 0.98, and the test images were classified correctly comparing with the experimental results of skin hydration, skin damaged level and the gender of the volunteers.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124229396","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 : 2018-06-01DOI: 10.1109/ICIVC.2018.8492861
J. Sun, Yu Ding, Zedong Huang, Ning Wang, Xinglong Zhu, J. Xi
Laplacian surface editing is an intuitive mesh deformation tool that preserves surface detail features when editing mesh models. However, the Laplace editing algorithm needs to solve a linear matrix system proportional to the number of vertices of the three-dimension (3D) mesh model, which is highly time-consuming for 3D model deformation. Therefore, the purpose of this paper is to present an improved deformation algorithm of Laplacian based on the mesh model simplification. The simplified method of vertex deletion is used to simplify the original 3D mesh model and the Laplacian deformation technique is adopted in the simplified mesh module. The deformation efficiency of the model is improved while the rotation invariant feature is guaranteed. The result of the experiment also verifies the effectiveness of this algorithm.
{"title":"Laplacian Deformation Algorithm Based on Mesh Model Simplification","authors":"J. Sun, Yu Ding, Zedong Huang, Ning Wang, Xinglong Zhu, J. Xi","doi":"10.1109/ICIVC.2018.8492861","DOIUrl":"https://doi.org/10.1109/ICIVC.2018.8492861","url":null,"abstract":"Laplacian surface editing is an intuitive mesh deformation tool that preserves surface detail features when editing mesh models. However, the Laplace editing algorithm needs to solve a linear matrix system proportional to the number of vertices of the three-dimension (3D) mesh model, which is highly time-consuming for 3D model deformation. Therefore, the purpose of this paper is to present an improved deformation algorithm of Laplacian based on the mesh model simplification. The simplified method of vertex deletion is used to simplify the original 3D mesh model and the Laplacian deformation technique is adopted in the simplified mesh module. The deformation efficiency of the model is improved while the rotation invariant feature is guaranteed. The result of the experiment also verifies the effectiveness of this algorithm.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115818407","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 : 2018-06-01DOI: 10.1109/ICIVC.2018.8492850
Xuefeng Lv, Yongfeng Liao, Lan Deng
In order to more rapidly and effectively obtain the geospatial location and quantitative distribution of the trapped people who are under the ruins or besieged by special serious natural disasters, such as earthquake, flood and landslide, a kind of natural disaster emergency rescue system based on the mobile phone's high-precision positioning of the trapped people is put forward. Being integrated with the mobile phone positioning, mobile cellular communication, BeiDou satellite positioning and short message communication and 3D geographic scene technologies, this system can apply to the condition of the ground communication interruption and monitor the positions of trapped people and the rescue process in time. And it is by the three-level administrative application platforms that are respectively deployed on the ministry-level, the province-level and the disaster site that the on-site emergency rescue information collaboration is achieved so as to be helpful to improve the ability of the serious natural disaster emergency rescue and relief decision support.
{"title":"Natural Disaster Emergency Rescue System Based on the Mobile Phone's High-Precision Positioning","authors":"Xuefeng Lv, Yongfeng Liao, Lan Deng","doi":"10.1109/ICIVC.2018.8492850","DOIUrl":"https://doi.org/10.1109/ICIVC.2018.8492850","url":null,"abstract":"In order to more rapidly and effectively obtain the geospatial location and quantitative distribution of the trapped people who are under the ruins or besieged by special serious natural disasters, such as earthquake, flood and landslide, a kind of natural disaster emergency rescue system based on the mobile phone's high-precision positioning of the trapped people is put forward. Being integrated with the mobile phone positioning, mobile cellular communication, BeiDou satellite positioning and short message communication and 3D geographic scene technologies, this system can apply to the condition of the ground communication interruption and monitor the positions of trapped people and the rescue process in time. And it is by the three-level administrative application platforms that are respectively deployed on the ministry-level, the province-level and the disaster site that the on-site emergency rescue information collaboration is achieved so as to be helpful to improve the ability of the serious natural disaster emergency rescue and relief decision support.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132328330","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 : 2018-06-01DOI: 10.1109/ICIVC.2018.8492727
J. Sun, Yuzhong Ma, Han Yang, Ning Wang, Yu Ding, Aiping Song, Yongwei Zhu, J. Xi
In this paper, a method of preprocessing to extract numbers from a low-contrast image of numerical instruments and recognition based on skeleton structural features is proposed. The approach is based on the grayscale dilation operation which can enhance the difference of targets with the background of the image and eliminate the adhesion between the target and the environment. After that, a skeleton algorithm is used to highlight the shape and topology of the target which makes the target number easier to be identified. Using HALCON to test, the result shows that the identification has a high accuracy rate.
{"title":"Character Recognition Method for Low-Contrast Images of Numerical Instruments","authors":"J. Sun, Yuzhong Ma, Han Yang, Ning Wang, Yu Ding, Aiping Song, Yongwei Zhu, J. Xi","doi":"10.1109/ICIVC.2018.8492727","DOIUrl":"https://doi.org/10.1109/ICIVC.2018.8492727","url":null,"abstract":"In this paper, a method of preprocessing to extract numbers from a low-contrast image of numerical instruments and recognition based on skeleton structural features is proposed. The approach is based on the grayscale dilation operation which can enhance the difference of targets with the background of the image and eliminate the adhesion between the target and the environment. After that, a skeleton algorithm is used to highlight the shape and topology of the target which makes the target number easier to be identified. Using HALCON to test, the result shows that the identification has a high accuracy rate.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130867318","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 : 2018-06-01DOI: 10.1109/ICIVC.2018.8492739
Hao-Liang Yang, Xiuqin Su, Chunwu Ju, Shaobo Wu
Natural images suffer from degradations in imaging system, and image blur is a major source of them. Most existing approaches aim to estimate a blur kernel via an alternating optimization method in multiscale space. However, in our practical project application, we need to deal with motion blurs come from moving conveyor belts. In this case, the degradation model and its orientation are known to us. In this paper, we propose a self-adaptive image deblurring method to deal with it. The model parameters are optimized by a heuristic algorithm, and the latent images are deblurred by a deconvolution technique based on f 1 -norm constraint. Simulation results show that our method not only acts on motion blur model, but also can deal with atmosphere turbulence model and defocus model, and the comparison results indicate that it outperforms others’. Furthermore, it is able to deal with motion blur in real scenes with high efficiency.
{"title":"Efficient Self-Adaptive Image Deblurring Based on Model Parameter Optimization","authors":"Hao-Liang Yang, Xiuqin Su, Chunwu Ju, Shaobo Wu","doi":"10.1109/ICIVC.2018.8492739","DOIUrl":"https://doi.org/10.1109/ICIVC.2018.8492739","url":null,"abstract":"Natural images suffer from degradations in imaging system, and image blur is a major source of them. Most existing approaches aim to estimate a blur kernel via an alternating optimization method in multiscale space. However, in our practical project application, we need to deal with motion blurs come from moving conveyor belts. In this case, the degradation model and its orientation are known to us. In this paper, we propose a self-adaptive image deblurring method to deal with it. The model parameters are optimized by a heuristic algorithm, and the latent images are deblurred by a deconvolution technique based on f 1 -norm constraint. Simulation results show that our method not only acts on motion blur model, but also can deal with atmosphere turbulence model and defocus model, and the comparison results indicate that it outperforms others’. Furthermore, it is able to deal with motion blur in real scenes with high efficiency.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127514256","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 : 2018-06-01DOI: 10.1109/ICIVC.2018.8492719
Shan Luo, Qiu Xn, Tong Wu, S. Du
In this paper, a time-domain signal reconstruction method based on the Lv distribution (LVD) is introduced for multi-component linear frequency modulated (LFM) signals. Comparing to the LVD based signal reconstruction (LSR) which had been reported to recover signals based on the auto-terms, our approach can reduce recovery errors by subtracting cross-terms mixed in the auto-terms. Therefore it is an improved method of LSR, referring to as the LSR with suppressed cross-terms (LSRSC). Examples are simulated to show that the LSRSC is able to reconstruct multi-component LFM signals effectively. Finally, the proposed method is employed on re-sampling to arbitrary sampling rate and achieves better performance than the LSR and fractional Fourier transform.
{"title":"An Improved LFM Signal Reconstruction Method and its Application","authors":"Shan Luo, Qiu Xn, Tong Wu, S. Du","doi":"10.1109/ICIVC.2018.8492719","DOIUrl":"https://doi.org/10.1109/ICIVC.2018.8492719","url":null,"abstract":"In this paper, a time-domain signal reconstruction method based on the Lv distribution (LVD) is introduced for multi-component linear frequency modulated (LFM) signals. Comparing to the LVD based signal reconstruction (LSR) which had been reported to recover signals based on the auto-terms, our approach can reduce recovery errors by subtracting cross-terms mixed in the auto-terms. Therefore it is an improved method of LSR, referring to as the LSR with suppressed cross-terms (LSRSC). Examples are simulated to show that the LSRSC is able to reconstruct multi-component LFM signals effectively. Finally, the proposed method is employed on re-sampling to arbitrary sampling rate and achieves better performance than the LSR and fractional Fourier transform.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125615575","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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