Pub Date : 2014-10-27DOI: 10.1109/ICIP.2014.7025278
Seong-Gyun Jeong, Y. Tarabalka, J. Zerubia
We propose a new model for wrinkle detection in human faces using a marked point process. In order to detect an arbitrary shape of wrinkles, we represent them as a set of line segments, where each segment is characterized by its length and orientation. We propose a probability density of wrinkle model which exploits local edge profile and geometric properties of wrinkles. To optimize the probability density of wrinkle model, we employ reversible jump Markov chain Monte Carlo sampler with delayed rejection. Experimental results demonstrate that the new algorithm detects facial wrinkles more accurately than a recent state-of-the-art method.
{"title":"Marked point process model for facial wrinkle detection","authors":"Seong-Gyun Jeong, Y. Tarabalka, J. Zerubia","doi":"10.1109/ICIP.2014.7025278","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025278","url":null,"abstract":"We propose a new model for wrinkle detection in human faces using a marked point process. In order to detect an arbitrary shape of wrinkles, we represent them as a set of line segments, where each segment is characterized by its length and orientation. We propose a probability density of wrinkle model which exploits local edge profile and geometric properties of wrinkles. To optimize the probability density of wrinkle model, we employ reversible jump Markov chain Monte Carlo sampler with delayed rejection. Experimental results demonstrate that the new algorithm detects facial wrinkles more accurately than a recent state-of-the-art method.","PeriodicalId":6856,"journal":{"name":"2014 IEEE International Conference on Image Processing (ICIP)","volume":"36 1","pages":"1391-1394"},"PeriodicalIF":0.0,"publicationDate":"2014-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83302716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-10-27DOI: 10.1109/ICIP.2014.7025350
B. Recur, H. D. Sarkissian, M. Servieres
In this paper, we develop a global iterative algorithm for tomographic reconstructions from Mojette projections. Since Spline-Mojette projections are obtained by convolving Dirac-Mojette values with a specific uniform projection kernel, we decorrelate iterative reconstructions from projection model and provide a global scheme available for all Mojette models. We refer iterative algorithms to their Radon based counterparts and propose a comparative study from several Mojette acquisitions.
{"title":"Global scheme for iterative mojette reconstructions","authors":"B. Recur, H. D. Sarkissian, M. Servieres","doi":"10.1109/ICIP.2014.7025350","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025350","url":null,"abstract":"In this paper, we develop a global iterative algorithm for tomographic reconstructions from Mojette projections. Since Spline-Mojette projections are obtained by convolving Dirac-Mojette values with a specific uniform projection kernel, we decorrelate iterative reconstructions from projection model and provide a global scheme available for all Mojette models. We refer iterative algorithms to their Radon based counterparts and propose a comparative study from several Mojette acquisitions.","PeriodicalId":6856,"journal":{"name":"2014 IEEE International Conference on Image Processing (ICIP)","volume":"115 1","pages":"1748-1752"},"PeriodicalIF":0.0,"publicationDate":"2014-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90743256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-10-01DOI: 10.1109/ICIP.2014.7025962
Michel Abboud, A. Benzinou, K. Nasreddine, M. Jazar
In this paper, we describe a statistical shape analysis founded on a robust elastic metric. The proposed metric is based on geodesics in the shape space. Using this distance, we formulate a variational setting to estimate intrinsic mean shape viewed as the perfect pattern to represent a set of given shapes. By applying a geodesic-based shape warping, we generate a principal component analysis (PCA) able to capture nonlinear shape variability. Indeed, the proposed approach better reflects the main modes of variability of the data. Therefore, characterizing dominant modes of individual shape variations is conducted well through the reconstruction process. We demonstrate the efficiency of our approach with an application on a GESTURES database.
{"title":"Geodesics-based statistical shape analysis","authors":"Michel Abboud, A. Benzinou, K. Nasreddine, M. Jazar","doi":"10.1109/ICIP.2014.7025962","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025962","url":null,"abstract":"In this paper, we describe a statistical shape analysis founded on a robust elastic metric. The proposed metric is based on geodesics in the shape space. Using this distance, we formulate a variational setting to estimate intrinsic mean shape viewed as the perfect pattern to represent a set of given shapes. By applying a geodesic-based shape warping, we generate a principal component analysis (PCA) able to capture nonlinear shape variability. Indeed, the proposed approach better reflects the main modes of variability of the data. Therefore, characterizing dominant modes of individual shape variations is conducted well through the reconstruction process. We demonstrate the efficiency of our approach with an application on a GESTURES database.","PeriodicalId":6856,"journal":{"name":"2014 IEEE International Conference on Image Processing (ICIP)","volume":"5 1","pages":"4747-4751"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73499332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-10-01DOI: 10.1109/ICIP.2014.7025431
Jin Chen, J. Núñez-Yáñez, A. Achim
In this paper, a joint video fusion and super-resolution algorithm is proposed. The method addresses the problem of generating a high-resolution (HR) image from infrared (IR) and visible (VI) low-resolution (LR) images, in a Bayesian framework. In order to preserve better the discontinuities, a Generalized Gaussian Markov Random Field (MRF) is used to formulate the prior. Experimental results demonstrate that information from both visible and infrared bands is recovered from the LR frames in an effective way.
{"title":"Joint video fusion and super resolution based on Markov random fields","authors":"Jin Chen, J. Núñez-Yáñez, A. Achim","doi":"10.1109/ICIP.2014.7025431","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025431","url":null,"abstract":"In this paper, a joint video fusion and super-resolution algorithm is proposed. The method addresses the problem of generating a high-resolution (HR) image from infrared (IR) and visible (VI) low-resolution (LR) images, in a Bayesian framework. In order to preserve better the discontinuities, a Generalized Gaussian Markov Random Field (MRF) is used to formulate the prior. Experimental results demonstrate that information from both visible and infrared bands is recovered from the LR frames in an effective way.","PeriodicalId":6856,"journal":{"name":"2014 IEEE International Conference on Image Processing (ICIP)","volume":"130 1","pages":"2150-2154"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73845687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-10-01DOI: 10.1109/ICIP.2014.7025705
Florian Angehrn, Oliver Wang, Yagiz Aksoy, M. Gross, A. Smolic
Free viewpoint video enables interactive viewpoint selection in real world scenes, which is attractive for many applications such as sports visualization. Multi-camera registration is one of the difficult tasks in such systems. We introduce the concept of a static high resolution master camera for improved long-term multiview alignment. All broadcast cameras are aligned to a common reference. Our approach builds on frame-to-frame alignment, extended into a recursive long-term estimation process, which is shown to be accurate, robust and stable over long sequences.
{"title":"MasterCam FVV: Robust registration of multiview sports video to a static high-resolution master camera for free viewpoint video","authors":"Florian Angehrn, Oliver Wang, Yagiz Aksoy, M. Gross, A. Smolic","doi":"10.1109/ICIP.2014.7025705","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025705","url":null,"abstract":"Free viewpoint video enables interactive viewpoint selection in real world scenes, which is attractive for many applications such as sports visualization. Multi-camera registration is one of the difficult tasks in such systems. We introduce the concept of a static high resolution master camera for improved long-term multiview alignment. All broadcast cameras are aligned to a common reference. Our approach builds on frame-to-frame alignment, extended into a recursive long-term estimation process, which is shown to be accurate, robust and stable over long sequences.","PeriodicalId":6856,"journal":{"name":"2014 IEEE International Conference on Image Processing (ICIP)","volume":"80 1","pages":"3474-3478"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73884376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-10-01DOI: 10.1109/ICIP.2014.7025757
Christian Feldmann, Fabian Jäger, M. Wien
In the current standardization process of the scalable extension to High Efficiency Video Coding (SHVC) a high level syntax multi-loop approach is close to completion. On the one hand this multi-loop approach offers a reasonable rate-distortion performance while only minimal modifications to the encoder and decoder in both layers are required. On the other hand this approach requires full reconstruction of all pictures of all layers at the decoder side which, in the case of quality scalability with two layers, doubles the decoder complexity. In this paper high layer modifications to the prediction structure similar to the scalable extension of H.264 - AVC are implemented in SHVC and studied. These modifications allow for an enhancement layer decoder implementation to skip a significant amount of motion compensation and deblocking operations in the base layer. It is shown that the decoder complexity can hereby be reduced up to 55% for the random access configuration and up to 64% for the low delay configuration compared to SHVC. An overall coding performance increase of 1.2% when decoding the enhancement layer is reported while when only decoding the base layer a drift can be observed between -0.16 dB for random access and -0.39 dB for low delay.
在当前高效视频编码(High Efficiency Video Coding, SHVC)可扩展的标准化进程中，一种高级语法多循环方法已接近完成。一方面，这种多环路方法提供了合理的率失真性能，同时只需要对两层的编码器和解码器进行最小的修改。另一方面，这种方法需要在解码器端完全重建所有层的所有图片，在两层的质量可扩展性的情况下，解码器的复杂性增加了一倍。本文在SHVC中实现了类似于H.264 - AVC可伸缩扩展的预测结构的高层修改，并对其进行了研究。这些修改允许增强层解码器实现在基础层中跳过大量的运动补偿和块化操作。结果表明，与SHVC相比，随机接入配置的解码器复杂度可降低55%，低延迟配置的解码器复杂度可降低64%。据报道，当解码增强层时，总体编码性能提高了1.2%，而当仅解码基础层时，可以观察到随机访问时的-0.16 dB和低延迟时的-0.39 dB之间的漂移。
{"title":"Decoder complexity reduction for the scalable extension of HEVC","authors":"Christian Feldmann, Fabian Jäger, M. Wien","doi":"10.1109/ICIP.2014.7025757","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025757","url":null,"abstract":"In the current standardization process of the scalable extension to High Efficiency Video Coding (SHVC) a high level syntax multi-loop approach is close to completion. On the one hand this multi-loop approach offers a reasonable rate-distortion performance while only minimal modifications to the encoder and decoder in both layers are required. On the other hand this approach requires full reconstruction of all pictures of all layers at the decoder side which, in the case of quality scalability with two layers, doubles the decoder complexity. In this paper high layer modifications to the prediction structure similar to the scalable extension of H.264 - AVC are implemented in SHVC and studied. These modifications allow for an enhancement layer decoder implementation to skip a significant amount of motion compensation and deblocking operations in the base layer. It is shown that the decoder complexity can hereby be reduced up to 55% for the random access configuration and up to 64% for the low delay configuration compared to SHVC. An overall coding performance increase of 1.2% when decoding the enhancement layer is reported while when only decoding the base layer a drift can be observed between -0.16 dB for random access and -0.39 dB for low delay.","PeriodicalId":6856,"journal":{"name":"2014 IEEE International Conference on Image Processing (ICIP)","volume":"364 1","pages":"3729-3733"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75439338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-10-01DOI: 10.1109/ICIP.2014.7025715
A. Rahimi, Jingjia Xu, Linwei Wang
Noninvasive cardiac electrophysiological imaging aims to mathematically reconstruct the spatio-temporal dynamics of cardiac current sources from body-surface electrocardiography data. This ill-posed problem is often regularized by imposing a certain constraining model on the solution. However, it enforces the source distribution to follow a pre-assumed spatial structure that does not always match the spatio-temporal changes of current sources. We propose a Bayesian approach for 3D current source estimation that consists of a continuous combination of multiple models, each reflecting a specific spatial property for current sources. Multiple models are incorporated into our Bayesian approach as an Lp-norm prior for current sources, where p is an unknown hyperparameter with prior probabilistic distribution defined over the range between 1 and 2. The current source estimation is then obtained as an optimally weighted combination of solutions across all models, the weight being determined from posterior distribution of p inferred from electrocardiography data. The performance of our proposed approach is assessed in a set of synthetic and real-data experiments on human heart-torso models. While the use of fixed models such as L1- and L2-norm only properly recovers sources with specific spatial structures, our method delivers consistent performance in reconstructing sources with different extents and structures.
{"title":"Multiple-model Bayesian approach to volumetric imaging of cardiac current sources","authors":"A. Rahimi, Jingjia Xu, Linwei Wang","doi":"10.1109/ICIP.2014.7025715","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025715","url":null,"abstract":"Noninvasive cardiac electrophysiological imaging aims to mathematically reconstruct the spatio-temporal dynamics of cardiac current sources from body-surface electrocardiography data. This ill-posed problem is often regularized by imposing a certain constraining model on the solution. However, it enforces the source distribution to follow a pre-assumed spatial structure that does not always match the spatio-temporal changes of current sources. We propose a Bayesian approach for 3D current source estimation that consists of a continuous combination of multiple models, each reflecting a specific spatial property for current sources. Multiple models are incorporated into our Bayesian approach as an Lp-norm prior for current sources, where p is an unknown hyperparameter with prior probabilistic distribution defined over the range between 1 and 2. The current source estimation is then obtained as an optimally weighted combination of solutions across all models, the weight being determined from posterior distribution of p inferred from electrocardiography data. The performance of our proposed approach is assessed in a set of synthetic and real-data experiments on human heart-torso models. While the use of fixed models such as L1- and L2-norm only properly recovers sources with specific spatial structures, our method delivers consistent performance in reconstructing sources with different extents and structures.","PeriodicalId":6856,"journal":{"name":"2014 IEEE International Conference on Image Processing (ICIP)","volume":"51 1","pages":"3522-3526"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75760980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-10-01DOI: 10.1109/ICIP.2014.7025000
Y. Duan, Huibin Chang, Weimin Huang, Jiayin Zhou
This paper presents a novel discrete Mumford-Shah model for the simultaneous bias correction and image segmentation(SBCIS) for images with intensity inhomogeneity. The model is based on the assumption that an image can be approximated by a product of true intensities and a bias field. Unlike the existing methods, where the true intensities are represented as a linear combination of characteristic functions of segmentation regions, we employ L0 gradient minimization to enforce a piecewise constant solution. We introduce a new neighbor term into the Mumford-Shah model to allow the true intensity of a pixel to be influenced by its immediate neighborhood. A two-stage segmentation method is applied to the proposed Mumford-Shah model. In the first stage, both the true intensities and bias field are obtained while the segmentation is done using the K-means clustering method in the second stage. Comparisons with the two-stage Mumford-Shah model show the advantages of our method in its ability in segmenting images with intensity inhomogeneity.
{"title":"Simultaneous bias correction and image segmentation via L0 regularized Mumford-Shah model","authors":"Y. Duan, Huibin Chang, Weimin Huang, Jiayin Zhou","doi":"10.1109/ICIP.2014.7025000","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025000","url":null,"abstract":"This paper presents a novel discrete Mumford-Shah model for the simultaneous bias correction and image segmentation(SBCIS) for images with intensity inhomogeneity. The model is based on the assumption that an image can be approximated by a product of true intensities and a bias field. Unlike the existing methods, where the true intensities are represented as a linear combination of characteristic functions of segmentation regions, we employ L0 gradient minimization to enforce a piecewise constant solution. We introduce a new neighbor term into the Mumford-Shah model to allow the true intensity of a pixel to be influenced by its immediate neighborhood. A two-stage segmentation method is applied to the proposed Mumford-Shah model. In the first stage, both the true intensities and bias field are obtained while the segmentation is done using the K-means clustering method in the second stage. Comparisons with the two-stage Mumford-Shah model show the advantages of our method in its ability in segmenting images with intensity inhomogeneity.","PeriodicalId":6856,"journal":{"name":"2014 IEEE International Conference on Image Processing (ICIP)","volume":"252 1","pages":"6-40"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75825772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-10-01DOI: 10.1109/ICIP.2014.7025050
Makarand Tapaswi, Cemal Cagn Corez, M. Bäuml, H. K. Ekenel, R. Stiefelhagen
Automatic person identification in TV series has gained popularity over the years. While most of the works rely on using face-based recognition, errors during tracking such as false positive face tracks are typically ignored. We propose a variety of methods to remove false positive face tracks and categorize the methods into confidence- and context-based. We evaluate our methods on a large TV series data set and show that up to 75% of the false positive face tracks are removed at the cost of 3.6% true positive tracks. We further show that the proposed method is general and applicable to other detectors or trackers.
{"title":"Cleaning up after a face tracker: False positive removal","authors":"Makarand Tapaswi, Cemal Cagn Corez, M. Bäuml, H. K. Ekenel, R. Stiefelhagen","doi":"10.1109/ICIP.2014.7025050","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025050","url":null,"abstract":"Automatic person identification in TV series has gained popularity over the years. While most of the works rely on using face-based recognition, errors during tracking such as false positive face tracks are typically ignored. We propose a variety of methods to remove false positive face tracks and categorize the methods into confidence- and context-based. We evaluate our methods on a large TV series data set and show that up to 75% of the false positive face tracks are removed at the cost of 3.6% true positive tracks. We further show that the proposed method is general and applicable to other detectors or trackers.","PeriodicalId":6856,"journal":{"name":"2014 IEEE International Conference on Image Processing (ICIP)","volume":"153 1","pages":"253-257"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74497586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-10-01DOI: 10.1109/ICIP.2014.7025964
José Lezama, R. G. V. Gioi, G. Randall, J. Morel
We will consider the problem of detecting configurations of points regularly spaced and lying on a smooth curve. This corresponds to the notion of good continuation introduced in the Gestalt theory. We present a robust algorithm for clustering points along such curves, whilst at the same time discarding noisy samples. Based on the a contrario methodology, the detector builds upon a simple, symmetric primitive for a triplet of points, and finds statistically meaningful chains of such triplets. An efficient implementation is proposed using the Floyd-Warshall algorithm. Experiments on synthetic and real data show that the method is able to identify the perceptually relevant configuration of points in good continuation.
{"title":"A contrario detection of good continuation of points","authors":"José Lezama, R. G. V. Gioi, G. Randall, J. Morel","doi":"10.1109/ICIP.2014.7025964","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025964","url":null,"abstract":"We will consider the problem of detecting configurations of points regularly spaced and lying on a smooth curve. This corresponds to the notion of good continuation introduced in the Gestalt theory. We present a robust algorithm for clustering points along such curves, whilst at the same time discarding noisy samples. Based on the a contrario methodology, the detector builds upon a simple, symmetric primitive for a triplet of points, and finds statistically meaningful chains of such triplets. An efficient implementation is proposed using the Floyd-Warshall algorithm. Experiments on synthetic and real data show that the method is able to identify the perceptually relevant configuration of points in good continuation.","PeriodicalId":6856,"journal":{"name":"2014 IEEE International Conference on Image Processing (ICIP)","volume":"31 1","pages":"4757-4761"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74520320","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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