Pub Date : 2016-09-01DOI: 10.1109/ICIP.2016.7532587
J. Mir, Dumidu S. Talagala, H. K. Arachchi, W. Fernando
In the absence of a commercial High Dynamic Range (HDR) distribution pipeline, two-layer backward-compatible HDR video coding is a viable solution for the imminent transition from Low Dynamic Range (LDR) to HDR content transmission. However, the performance of a two-layer coding solution is governed by the extension layer coding performance. In this paper, we propose an improved two-layer backward-compatible HDR video coding solution based on an adaptive residual mapping for the extension layer, keeping in view the performance of High Efficiency Video Coding (HEVC) being used to code this information. The proposed solution outperforms the reference method achieving averaged PU-PSNR improvements of up to 5.05 dB. The proposed method also shows potential of achieving the same HDR quality as the single layer coding solution with a minimum bitrate overhead and acceptable LDR quality in the base layer.
{"title":"Adaptive residual mapping for an efficient extension layer coding in two-layer HDR video coding","authors":"J. Mir, Dumidu S. Talagala, H. K. Arachchi, W. Fernando","doi":"10.1109/ICIP.2016.7532587","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7532587","url":null,"abstract":"In the absence of a commercial High Dynamic Range (HDR) distribution pipeline, two-layer backward-compatible HDR video coding is a viable solution for the imminent transition from Low Dynamic Range (LDR) to HDR content transmission. However, the performance of a two-layer coding solution is governed by the extension layer coding performance. In this paper, we propose an improved two-layer backward-compatible HDR video coding solution based on an adaptive residual mapping for the extension layer, keeping in view the performance of High Efficiency Video Coding (HEVC) being used to code this information. The proposed solution outperforms the reference method achieving averaged PU-PSNR improvements of up to 5.05 dB. The proposed method also shows potential of achieving the same HDR quality as the single layer coding solution with a minimum bitrate overhead and acceptable LDR quality in the base layer.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"138 1","pages":"1394-1398"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77435113","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 : 2016-09-01DOI: 10.1109/ICIP.2016.7532722
Yilin Wang, S. Kum, Chao Chen, A. Kokaram
Banding is a common video artifact caused by compressing low texture regions with coarse quantization. Relatively few previous attempts exist to address banding and none incorporate subjective testing for calibrating the measurement. In this paper, we propose a novel metric that incorporates both edge length and contrast across the edge to measure video banding. We further introduce both reference and non-reference metrics. Our results demonstrate that the new metrics have a very high correlation with subjective assessment and certainly outperforms PSNR, SSIM, and VQM.
{"title":"A perceptual visibility metric for banding artifacts","authors":"Yilin Wang, S. Kum, Chao Chen, A. Kokaram","doi":"10.1109/ICIP.2016.7532722","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7532722","url":null,"abstract":"Banding is a common video artifact caused by compressing low texture regions with coarse quantization. Relatively few previous attempts exist to address banding and none incorporate subjective testing for calibrating the measurement. In this paper, we propose a novel metric that incorporates both edge length and contrast across the edge to measure video banding. We further introduce both reference and non-reference metrics. Our results demonstrate that the new metrics have a very high correlation with subjective assessment and certainly outperforms PSNR, SSIM, and VQM.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"31 1","pages":"2067-2071"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77183986","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 : 2016-09-01DOI: 10.1109/ICIP.2016.7533097
Bo Peng, Wei Wang, Jing Dong, T. Tan
Existing 3D lighting consistency based forensic methods have some practical problems. They usually require additional images and human labor to reconstruct the 3D face model for lighting estimation, and furthermore, they cannot deal with expressional faces effectively. These drawbacks make them unusable in many practical cases. In this paper, we propose a more practical 3D lighting based forensic method by incorporating a facial landmark based 3D morphable model to efficiently fit the face shape. We also introduce a residual error based algorithm to automatically exclude outliers in lighting estimation. Our proposed method is fully automatic and very efficient compared to previous ones. Also, it does not depend on additional images and has better performance for expressional faces. Experiments on a realistic face dataset with variational lighting conditions indicate the efficacy and superiority of our method.
{"title":"Automatic detection of 3D lighting inconsistencies via a facial landmark based morphable model","authors":"Bo Peng, Wei Wang, Jing Dong, T. Tan","doi":"10.1109/ICIP.2016.7533097","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7533097","url":null,"abstract":"Existing 3D lighting consistency based forensic methods have some practical problems. They usually require additional images and human labor to reconstruct the 3D face model for lighting estimation, and furthermore, they cannot deal with expressional faces effectively. These drawbacks make them unusable in many practical cases. In this paper, we propose a more practical 3D lighting based forensic method by incorporating a facial landmark based 3D morphable model to efficiently fit the face shape. We also introduce a residual error based algorithm to automatically exclude outliers in lighting estimation. Our proposed method is fully automatic and very efficient compared to previous ones. Also, it does not depend on additional images and has better performance for expressional faces. Experiments on a realistic face dataset with variational lighting conditions indicate the efficacy and superiority of our method.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"7 1","pages":"3932-3936"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76265745","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 : 2016-09-01DOI: 10.1109/ICIP.2016.7532734
Médéric Blestel, M. Ropert, W. Hamidouche
In this paper, we address the problem of the statistical multiplexing of video streams. Dynamic bitrate allocation is used to improve the overall video quality of a pool of channels. The balance is obtained by providing more bits to complex channels, while deprivations are applied to non-complex ones. In this study, the error minimization optimization of several compressed video is considered along with different metrics in order to exhibit a repartition key for bitrate sharing among all the channels. The goal of this approach is to introduce a reactivity parameter able to manage the bit transfer between channels. The validity of the parametric model is verified on two particular values, and compared to a static repartition solution.
{"title":"Generic statistical multiplexer with a parametrized bitrate allocation criteria","authors":"Médéric Blestel, M. Ropert, W. Hamidouche","doi":"10.1109/ICIP.2016.7532734","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7532734","url":null,"abstract":"In this paper, we address the problem of the statistical multiplexing of video streams. Dynamic bitrate allocation is used to improve the overall video quality of a pool of channels. The balance is obtained by providing more bits to complex channels, while deprivations are applied to non-complex ones. In this study, the error minimization optimization of several compressed video is considered along with different metrics in order to exhibit a repartition key for bitrate sharing among all the channels. The goal of this approach is to introduce a reactivity parameter able to manage the bit transfer between channels. The validity of the parametric model is verified on two particular values, and compared to a static repartition solution.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"131 1","pages":"2127-2131"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76713237","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 : 2016-09-01DOI: 10.1109/ICIP.2016.7533169
Dan Wang, Canxiang Yan, S. Shan, Xilin Chen
The difficult acquisition of labeled data and the misalignment of local matching are major obstacles to apply person re-identification in real scenarios. To alleviate these problems, we propose an unsupervised method, called locality-constrained Earth Mover's Distance (LC-EMD), to learn the optimal measure between image pairs. Specifically, Gaussian mixture models (GMMs) are learned as signatures. By imposing locality constraints, LC-EMD can naturally achieve partial matching between Gaussian components. Moreover, LC-EMD has the analytical solution which can be efficiently computed. Experiments on two public datasets demonstrate LC-EMD is robust to misalignment and performs better than other unsupervised methods.
{"title":"Unsupervised person re-identification with locality-constrained Earth Mover's distance","authors":"Dan Wang, Canxiang Yan, S. Shan, Xilin Chen","doi":"10.1109/ICIP.2016.7533169","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7533169","url":null,"abstract":"The difficult acquisition of labeled data and the misalignment of local matching are major obstacles to apply person re-identification in real scenarios. To alleviate these problems, we propose an unsupervised method, called locality-constrained Earth Mover's Distance (LC-EMD), to learn the optimal measure between image pairs. Specifically, Gaussian mixture models (GMMs) are learned as signatures. By imposing locality constraints, LC-EMD can naturally achieve partial matching between Gaussian components. Moreover, LC-EMD has the analytical solution which can be efficiently computed. Experiments on two public datasets demonstrate LC-EMD is robust to misalignment and performs better than other unsupervised methods.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"115 1","pages":"4289-4293"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77926931","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 : 2016-09-01DOI: 10.1109/ICIP.2016.7533162
Yan Huang, Hao Sheng, Z. Xiong
This work proposes a novel person re-identification method based on Hierarchical Bipartite Graph Matching. Because human eyes observe person appearance roughly first and then goes further into the details gradually, our method abstracts person image from coarse to fine granularity, and finally into a three layer tree structure. Then, three bipartite graph matching methods are proposed for the matching of each layer between the trees. At the bottom layer Non-complete Bipartite Graph matching is proposed to collect matching pairs among small local regions. At the middle layer Semi-complete Bipartite Graph matching is used to deal with the problem of spatial misalignment between two person bodies. Complete Bipartite Graph matching is presented to refine the ranking result at the top layer. The effectiveness of our method is validated on the CAVIAR4REID and VIPeR datasets, and competitive results are achieved on both datasets.
{"title":"Person re-identification based on hierarchical bipartite graph matching","authors":"Yan Huang, Hao Sheng, Z. Xiong","doi":"10.1109/ICIP.2016.7533162","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7533162","url":null,"abstract":"This work proposes a novel person re-identification method based on Hierarchical Bipartite Graph Matching. Because human eyes observe person appearance roughly first and then goes further into the details gradually, our method abstracts person image from coarse to fine granularity, and finally into a three layer tree structure. Then, three bipartite graph matching methods are proposed for the matching of each layer between the trees. At the bottom layer Non-complete Bipartite Graph matching is proposed to collect matching pairs among small local regions. At the middle layer Semi-complete Bipartite Graph matching is used to deal with the problem of spatial misalignment between two person bodies. Complete Bipartite Graph matching is presented to refine the ranking result at the top layer. The effectiveness of our method is validated on the CAVIAR4REID and VIPeR datasets, and competitive results are achieved on both datasets.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"13 1","pages":"4255-4259"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78841906","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 : 2016-09-01DOI: 10.1109/ICIP.2016.7532618
Amin Zheng, Gene Cheung, D. Florêncio
The advent of depth sensing technologies has eased the detection of object contours in images. For efficient image compression, coded contours can enable edge-adaptive coding techniques such as graph Fourier transform (GFT) and arbitrarily shaped sub-block motion prediction. However, acquisition noise in captured depth images means that detected contours also suffer from errors. In this paper, we propose to jointly denoise and compress detected contours in an image. Specifically, we first propose a burst error model that models typical errors encountered in an observed string y of directional edges. We then formulate a rate-constrained maximum a posteriori (MAP) problem that trades off the posterior probability P(x|y) of an estimated string x given y with its code rate R(x). Given our burst error model, we show that the negative log of the likelihood P(y|x) can be written as a simple sum of burst error events, error symbols and burst lengths, while the geometric prior P(x) states intuitively that contours are more likely straight than curvy. We design a dynamic programming (DP) algorithm that solves the posed problem optimally. Experimental results show that our joint denoising / compression scheme outperformed a competing separate scheme in rate-distortion performance noticeably.
{"title":"Joint denoising / compression of image contours via geometric prior and variable-length context tree","authors":"Amin Zheng, Gene Cheung, D. Florêncio","doi":"10.1109/ICIP.2016.7532618","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7532618","url":null,"abstract":"The advent of depth sensing technologies has eased the detection of object contours in images. For efficient image compression, coded contours can enable edge-adaptive coding techniques such as graph Fourier transform (GFT) and arbitrarily shaped sub-block motion prediction. However, acquisition noise in captured depth images means that detected contours also suffer from errors. In this paper, we propose to jointly denoise and compress detected contours in an image. Specifically, we first propose a burst error model that models typical errors encountered in an observed string y of directional edges. We then formulate a rate-constrained maximum a posteriori (MAP) problem that trades off the posterior probability P(x|y) of an estimated string x given y with its code rate R(x). Given our burst error model, we show that the negative log of the likelihood P(y|x) can be written as a simple sum of burst error events, error symbols and burst lengths, while the geometric prior P(x) states intuitively that contours are more likely straight than curvy. We design a dynamic programming (DP) algorithm that solves the posed problem optimally. Experimental results show that our joint denoising / compression scheme outperformed a competing separate scheme in rate-distortion performance noticeably.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"1 1","pages":"1549-1553"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79798097","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 : 2016-09-01DOI: 10.1109/ICIP.2016.7532410
Tien-Dung Mai, T. Ngo, Duy-Dinh Le, D. Duong, Kiem Hoang, S. Satoh
Hierarchical classification is a computational efficient approach for large-scale image classification. The main challenging issue of this approach is to deal with error propagation. Irrelevant branching decision made at a parent node cannot be corrected at its child nodes in traversing the tree for classification. This paper presents a novel approach to reduce branching error at a node by taking its relative relationship into account. Given a node on the tree, we model each candidate branch by considering classification response of its child nodes, grandchild nodes and their differences with siblings. A maximum margin classifier is then applied to select the most discriminating candidate. Our proposed approach outperforms related approaches on Caltech-256, SUN-397 and ILSVRC2010-1K.
{"title":"Using node relationships for hierarchical classification","authors":"Tien-Dung Mai, T. Ngo, Duy-Dinh Le, D. Duong, Kiem Hoang, S. Satoh","doi":"10.1109/ICIP.2016.7532410","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7532410","url":null,"abstract":"Hierarchical classification is a computational efficient approach for large-scale image classification. The main challenging issue of this approach is to deal with error propagation. Irrelevant branching decision made at a parent node cannot be corrected at its child nodes in traversing the tree for classification. This paper presents a novel approach to reduce branching error at a node by taking its relative relationship into account. Given a node on the tree, we model each candidate branch by considering classification response of its child nodes, grandchild nodes and their differences with siblings. A maximum margin classifier is then applied to select the most discriminating candidate. Our proposed approach outperforms related approaches on Caltech-256, SUN-397 and ILSVRC2010-1K.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"4 1","pages":"514-518"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80957645","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 : 2016-09-01DOI: 10.1109/ICIP.2016.7532697
Utkarsh Gaur, M. Kourakis, E. Newman-Smith, William C. Smith, B. S. Manjunath
Segmentation is a key component of several bio-medical image processing systems. Recently, segmentation methods based on supervised learning such as deep convolutional networks have enjoyed immense success for natural image datasets and biological datasets alike. These methods require large volumes of data to avoid overfitting which limits their applicability. In this work, we present a transfer learning mechanism based on active learning which allows us to utilize pre-trained deep networks for segmenting new domains with limited labelled data. We introduce a novel optimization criterion to allow feedback on the most uncertain, yet abundant image patterns thus provisioning for an expert in the loop albeit with minimum amount of guidance. Our experiments demonstrate the effectiveness of the proposed method in improving segmentation performance with very limited labelled data.
{"title":"Membrane segmentation via active learning with deep networks","authors":"Utkarsh Gaur, M. Kourakis, E. Newman-Smith, William C. Smith, B. S. Manjunath","doi":"10.1109/ICIP.2016.7532697","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7532697","url":null,"abstract":"Segmentation is a key component of several bio-medical image processing systems. Recently, segmentation methods based on supervised learning such as deep convolutional networks have enjoyed immense success for natural image datasets and biological datasets alike. These methods require large volumes of data to avoid overfitting which limits their applicability. In this work, we present a transfer learning mechanism based on active learning which allows us to utilize pre-trained deep networks for segmenting new domains with limited labelled data. We introduce a novel optimization criterion to allow feedback on the most uncertain, yet abundant image patterns thus provisioning for an expert in the loop albeit with minimum amount of guidance. Our experiments demonstrate the effectiveness of the proposed method in improving segmentation performance with very limited labelled data.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"81 1","pages":"1943-1947"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83140227","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 : 2016-09-01DOI: 10.1109/ICIP.2016.7533128
Yinglong Wang, Chen Chen, Shuyuan Zhu, B. Zeng
In this paper, we propose an algorithm to remove rain streaks from single color image. Firstly, the guided filter, cooperated with rain pixels detection are used to separate a color image into low-frequency and high-frequency parts so that most rain components exist in the high-frequency part. Then, we focus on the high-frequency part to extract the non-rain details according to the characteristics of the rain in which a dictionary learning method is used. Meanwhile, to enhance the quality of the rain-removed image, the proposed principal direction of an image patch (PDIP) and the sensitivity of variance of color channels (SVCC) are employed in our work to help extract more non-rain details. Compared with the state-of-the-art works, our proposed method can remove the rain (especially heavy rain) from color images more efficiently.
{"title":"A framework of single-image deraining method based on analysis of rain characteristics","authors":"Yinglong Wang, Chen Chen, Shuyuan Zhu, B. Zeng","doi":"10.1109/ICIP.2016.7533128","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7533128","url":null,"abstract":"In this paper, we propose an algorithm to remove rain streaks from single color image. Firstly, the guided filter, cooperated with rain pixels detection are used to separate a color image into low-frequency and high-frequency parts so that most rain components exist in the high-frequency part. Then, we focus on the high-frequency part to extract the non-rain details according to the characteristics of the rain in which a dictionary learning method is used. Meanwhile, to enhance the quality of the rain-removed image, the proposed principal direction of an image patch (PDIP) and the sensitivity of variance of color channels (SVCC) are employed in our work to help extract more non-rain details. Compared with the state-of-the-art works, our proposed method can remove the rain (especially heavy rain) from color images more efficiently.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"4 1","pages":"4087-4091"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79626284","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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