Pub Date : 2016-09-01DOI: 10.1109/ICIP.2016.7532809
Michael M. Abdel-Sayed, Ahmed K. F. Khattab, Mohamed Fathy Abu Elyazeed
Compressed sensing enables the acquisition of sparse signals at a rate that is much lower than the Nyquist rate. Various greedy recovery algorithms have been proposed to achieve a lower computational complexity compared to the optimal ℓ1 minimization, while maintaining a good reconstruction accuracy. We propose a new greedy recovery algorithm for compressed sensing, called the Adaptive Reduced-set Matching Pursuit (ARMP). Our algorithm achieves higher reconstruction accuracy at a significantly low computational complexity compared to existing greedy recovery algorithms. It is even superior to ℓ1 minimization in terms of the normalized time-error product, a metric that we introduced to measure the trade-off between the reconstruction time and error.
{"title":"Adaptive reduced-set matching pursuit for compressed sensing recovery","authors":"Michael M. Abdel-Sayed, Ahmed K. F. Khattab, Mohamed Fathy Abu Elyazeed","doi":"10.1109/ICIP.2016.7532809","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7532809","url":null,"abstract":"Compressed sensing enables the acquisition of sparse signals at a rate that is much lower than the Nyquist rate. Various greedy recovery algorithms have been proposed to achieve a lower computational complexity compared to the optimal ℓ1 minimization, while maintaining a good reconstruction accuracy. We propose a new greedy recovery algorithm for compressed sensing, called the Adaptive Reduced-set Matching Pursuit (ARMP). Our algorithm achieves higher reconstruction accuracy at a significantly low computational complexity compared to existing greedy recovery algorithms. It is even superior to ℓ1 minimization in terms of the normalized time-error product, a metric that we introduced to measure the trade-off between the reconstruction time and error.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"4 1","pages":"2499-2503"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86935242","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.2016.7532550
Lu Wang, Lisheng Xu, Ming-Hsuan Yang
Despite significant progress in pedestrian detection has been made in recent years, detecting pedestrians in crowded scenes remains a challenging problem. In this paper, we propose to use visual contexts based on scale and occlusion cues from detections at proximity to better detect pedestrians for surveillance applications. Specifically, we first apply detectors based on full body and parts to generate initial detections. Scale prior at each image location is estimated using the cues provided by neighboring detections, and the confidence score of each detection is refined according to its consistency with the estimated scale prior. Local occlusion analysis is exploited in refining detection confidence scores which facilitates the final detection cluster based Non-Maximum Suppression. Experimental results on benchmark data sets show that the proposed algorithm performs favorably against the state-of-the-art methods.
{"title":"Pedestrian detection in crowded scenes via scale and occlusion analysis","authors":"Lu Wang, Lisheng Xu, Ming-Hsuan Yang","doi":"10.1109/ICIP.2016.7532550","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7532550","url":null,"abstract":"Despite significant progress in pedestrian detection has been made in recent years, detecting pedestrians in crowded scenes remains a challenging problem. In this paper, we propose to use visual contexts based on scale and occlusion cues from detections at proximity to better detect pedestrians for surveillance applications. Specifically, we first apply detectors based on full body and parts to generate initial detections. Scale prior at each image location is estimated using the cues provided by neighboring detections, and the confidence score of each detection is refined according to its consistency with the estimated scale prior. Local occlusion analysis is exploited in refining detection confidence scores which facilitates the final detection cluster based Non-Maximum Suppression. Experimental results on benchmark data sets show that the proposed algorithm performs favorably against the state-of-the-art methods.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"45 1","pages":"1210-1214"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90738825","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.2016.7532884
Tao Liu, Wen-gang Zhou, Houqiang Li
Sign Language Recognition (SLR) aims at translating the Sign Language (SL) into speech or text, so as to facilitate the communication between hearing-impaired people and the normal people. This problem has broad social impact, however it is challenging due to the variation for different people and the complexity in sign words. Traditional methods for SLR generally use handcrafted feature and Hidden Markov Models (HMMs) modeling temporal information. But reliable handcrafted features are difficult to design and not able to adapt to the large variations of sign words. To approach this problem, considering that Long Short-Term memory (LSTM) can model the contextual information of temporal sequence well, we propose an end-to-end method for SLR based on LSTM. Our system takes the moving trajectories of 4 skeleton joints as inputs without any prior knowledge and is free of explicit feature design. To evaluate our proposed model, we built a large isolated Chinese sign language vocabulary with Kinect 2.0. Experimental results demonstrate the effectiveness of our approach compared with traditional HMM based methods.
手语识别(Sign Language Recognition, SLR)旨在将手语(Sign Language, SL)翻译成语音或文字，以方便听障人士与正常人之间的交流。这个问题具有广泛的社会影响，但由于不同人的差异和手语的复杂性，它具有挑战性。传统的单反方法一般使用手工特征和隐马尔可夫模型(hmm)来建模时间信息。但是，可靠的手工特征很难设计，并且不能适应大量变化的标志文字。为了解决这一问题，考虑到长短期记忆(LSTM)可以很好地模拟时间序列的上下文信息，我们提出了一种基于LSTM的端到端SLR方法。我们的系统以4个骨骼关节的运动轨迹作为输入，没有任何先验知识，也没有明确的特征设计。为了评估我们提出的模型，我们用Kinect 2.0建立了一个大型孤立的中文手语词汇表。实验结果表明，与传统的HMM方法相比，该方法是有效的。
{"title":"Sign language recognition with long short-term memory","authors":"Tao Liu, Wen-gang Zhou, Houqiang Li","doi":"10.1109/ICIP.2016.7532884","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7532884","url":null,"abstract":"Sign Language Recognition (SLR) aims at translating the Sign Language (SL) into speech or text, so as to facilitate the communication between hearing-impaired people and the normal people. This problem has broad social impact, however it is challenging due to the variation for different people and the complexity in sign words. Traditional methods for SLR generally use handcrafted feature and Hidden Markov Models (HMMs) modeling temporal information. But reliable handcrafted features are difficult to design and not able to adapt to the large variations of sign words. To approach this problem, considering that Long Short-Term memory (LSTM) can model the contextual information of temporal sequence well, we propose an end-to-end method for SLR based on LSTM. Our system takes the moving trajectories of 4 skeleton joints as inputs without any prior knowledge and is free of explicit feature design. To evaluate our proposed model, we built a large isolated Chinese sign language vocabulary with Kinect 2.0. Experimental results demonstrate the effectiveness of our approach compared with traditional HMM based methods.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"85 1","pages":"2871-2875"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90989885","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.2016.7533066
Michele A. Saad, David G. Nicholas, Patrick McKnight, Jake Quartuccio, Ramesh Jaladi, P. Corriveau
Subjective test methodologies are morphing to enable researchers to answer questions relevant to rapidly evolving technologies in an efficient and reliable manner. This paper is an exploration of how subjective testing that employs crowdsourcing can be refined to drive stability and reliability in subjective results. We investigate how various design decisions can lead to disparate subjective responses; motivated by the need for efficient acquisition of large volumes of data, and the need to understand and mitigate pitfalls in online tests, when the stimuli are complex and subtle as is the case in popular consumer scenarios.
{"title":"Subtle consumer-photo quality evaluation","authors":"Michele A. Saad, David G. Nicholas, Patrick McKnight, Jake Quartuccio, Ramesh Jaladi, P. Corriveau","doi":"10.1109/ICIP.2016.7533066","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7533066","url":null,"abstract":"Subjective test methodologies are morphing to enable researchers to answer questions relevant to rapidly evolving technologies in an efficient and reliable manner. This paper is an exploration of how subjective testing that employs crowdsourcing can be refined to drive stability and reliability in subjective results. We investigate how various design decisions can lead to disparate subjective responses; motivated by the need for efficient acquisition of large volumes of data, and the need to understand and mitigate pitfalls in online tests, when the stimuli are complex and subtle as is the case in popular consumer scenarios.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"67 1","pages":"3778-3782"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91264093","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.2016.7532825
Lazhar Khelifi, M. Mignotte
Fusion of image segmentations using consensus clustering and based on the optimization of a single criterion (commonly called the median partition based approach) may bias and limit the performance of an image segmentation model. To address this issue, we propose, in this paper, a new fusion model of image segmentation based on multi-objective optimization which aims to avoid the bias caused by a single criterion and to achieve a final improved segmentation. The proposed fusion model combines two conflicting and complementary segmentation criteria, namely; the region-based variation of information (VoI) criterion and the contour-based F-Measure (precision-recall) criterion with an entropy-based confidence weighting factor. To optimize our energy-based model we use an optimization procedure derived from the iterative conditional modes (ICM) algorithm. The experimental results on the Berkeley database with manual ground truth segmentations clearly show the effectiveness and the robustness of our multi-objective median partition based approach.
{"title":"A new multi-criteria fusion model for color textured image segmentation","authors":"Lazhar Khelifi, M. Mignotte","doi":"10.1109/ICIP.2016.7532825","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7532825","url":null,"abstract":"Fusion of image segmentations using consensus clustering and based on the optimization of a single criterion (commonly called the median partition based approach) may bias and limit the performance of an image segmentation model. To address this issue, we propose, in this paper, a new fusion model of image segmentation based on multi-objective optimization which aims to avoid the bias caused by a single criterion and to achieve a final improved segmentation. The proposed fusion model combines two conflicting and complementary segmentation criteria, namely; the region-based variation of information (VoI) criterion and the contour-based F-Measure (precision-recall) criterion with an entropy-based confidence weighting factor. To optimize our energy-based model we use an optimization procedure derived from the iterative conditional modes (ICM) algorithm. The experimental results on the Berkeley database with manual ground truth segmentations clearly show the effectiveness and the robustness of our multi-objective median partition based approach.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"345 1","pages":"2579-2583"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76573999","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.2016.7532719
S. Bosse, Qiaobo Chen, Mischa Siekmann, W. Samek, T. Wiegand
This paper proposes a reduced reference image quality assessment method using only a low number of features. It involves a shearlet decomposition, directional pooling of the obtained coefficient and extracts the scalewise statistical location parameter as a feature. The proposed method is tested and compared to similar approaches on the LIVE image database. On this database it outperforms the compared methods on five of seven distortion types and on the full testset with a linear correlation of = 0.89.
{"title":"Shearlet-based reduced reference image quality assessment","authors":"S. Bosse, Qiaobo Chen, Mischa Siekmann, W. Samek, T. Wiegand","doi":"10.1109/ICIP.2016.7532719","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7532719","url":null,"abstract":"This paper proposes a reduced reference image quality assessment method using only a low number of features. It involves a shearlet decomposition, directional pooling of the obtained coefficient and extracts the scalewise statistical location parameter as a feature. The proposed method is tested and compared to similar approaches on the LIVE image database. On this database it outperforms the compared methods on five of seven distortion types and on the full testset with a linear correlation of = 0.89.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"27 1","pages":"2052-2056"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78514716","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.2016.7532674
Yuelong Li, V. Monga
Image alignment and stitching continue to be the topics of great interest. Image mosaicking is a key application that involves both alignment and stitching of multiple images. Despite significant previous effort, existing methods have limited robustness in dealing with occlusions and local object motion in different captures. To address this issue, we investigate the potential of applying sparsity-based methods to the task of image alignment and stitching. We formulate the alignment problem as a low-rank and sparse matrix decomposition problem under incomplete observations (multiple parts of a scene), and the stitching problem as a multiple labeling problem which utilizes the sparse components. Additionally we develop efficient algorithms for solving them. Unlike typical pairwise alignment manners in classical image alignment algorithms, our algorithm is capable of simultaneously aligning multiple images, making full use of inter-frame relationships among all images. Experimental results demonstrate that the proposed algorithm is capable of generating artifact-free stitched image mosaics that are robust against occlusions and object motion.
{"title":"SIASM: Sparsity-based image alignment and stitching method for robust image mosaicking","authors":"Yuelong Li, V. Monga","doi":"10.1109/ICIP.2016.7532674","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7532674","url":null,"abstract":"Image alignment and stitching continue to be the topics of great interest. Image mosaicking is a key application that involves both alignment and stitching of multiple images. Despite significant previous effort, existing methods have limited robustness in dealing with occlusions and local object motion in different captures. To address this issue, we investigate the potential of applying sparsity-based methods to the task of image alignment and stitching. We formulate the alignment problem as a low-rank and sparse matrix decomposition problem under incomplete observations (multiple parts of a scene), and the stitching problem as a multiple labeling problem which utilizes the sparse components. Additionally we develop efficient algorithms for solving them. Unlike typical pairwise alignment manners in classical image alignment algorithms, our algorithm is capable of simultaneously aligning multiple images, making full use of inter-frame relationships among all images. Experimental results demonstrate that the proposed algorithm is capable of generating artifact-free stitched image mosaics that are robust against occlusions and object motion.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"99 1","pages":"1828-1832"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78440811","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.2016.7532438
Yida Wang, Weihong Deng
CNN has shown excellent performance on object recognition based on huge amount of real images. For training with synthetic data rendered from 3D models alone to reduce the workload of collecting real images, we propose a concatenated self-restraint learning structure lead by a triplet and softmax jointed loss function for object recognition. Locally connected auto encoder trained from rendered images with and without background used for object reconstruction against environment variables produces an additional channel automatically concatenated to RGB channels as input of classification network. This structure makes it possible training a softmax classifier directly from CNN based on synthetic data with our rendering strategy. Our structure halves the gap between training based on real photos and 3D model in both PASCAL and ImageNet database compared to GoogleNet.
{"title":"Self-restraint object recognition by model based CNN learning","authors":"Yida Wang, Weihong Deng","doi":"10.1109/ICIP.2016.7532438","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7532438","url":null,"abstract":"CNN has shown excellent performance on object recognition based on huge amount of real images. For training with synthetic data rendered from 3D models alone to reduce the workload of collecting real images, we propose a concatenated self-restraint learning structure lead by a triplet and softmax jointed loss function for object recognition. Locally connected auto encoder trained from rendered images with and without background used for object reconstruction against environment variables produces an additional channel automatically concatenated to RGB channels as input of classification network. This structure makes it possible training a softmax classifier directly from CNN based on synthetic data with our rendering strategy. Our structure halves the gap between training based on real photos and 3D model in both PASCAL and ImageNet database compared to GoogleNet.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"14 1","pages":"654-658"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73425893","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.2016.7532723
Yibing Zhan, Rong Zhang
Structural information is critical in image quality assessment (IQA). Although existing objective IQA methods have achieved high consistency with subjective perception, detecting structural variation remains a difficult task. In this paper, we propose a novel structural variation detection strategy that is based on binary logic and inspired by the bag-of-words model. The proposed strategy detects structural variation by comparing the occurrences of structural features within the original and distorted images. In order to show the effectiveness of this strategy, this paper also proposes a novel and simple IQA method based on this strategy. The proposed method evaluates the image quality from two aspects: the structure distortion and the luminance distortion. The experimental results from four public databases show that the proposed method is highly congruous with subjective evaluation. The results also prove that the detection strategy is useful.
{"title":"A novel structural variation detection strategy for image quality assessment","authors":"Yibing Zhan, Rong Zhang","doi":"10.1109/ICIP.2016.7532723","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7532723","url":null,"abstract":"Structural information is critical in image quality assessment (IQA). Although existing objective IQA methods have achieved high consistency with subjective perception, detecting structural variation remains a difficult task. In this paper, we propose a novel structural variation detection strategy that is based on binary logic and inspired by the bag-of-words model. The proposed strategy detects structural variation by comparing the occurrences of structural features within the original and distorted images. In order to show the effectiveness of this strategy, this paper also proposes a novel and simple IQA method based on this strategy. The proposed method evaluates the image quality from two aspects: the structure distortion and the luminance distortion. The experimental results from four public databases show that the proposed method is highly congruous with subjective evaluation. The results also prove that the detection strategy is useful.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"147 1","pages":"2072-2076"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73722827","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.2016.7532848
Ali Karaali, C. Jung
This paper presents a new image retargeting method that explores blur information. Given the input image, we compute the blur map and estimate in-focus regions. For retargeting, we first try to crop image boundaries as much as possible (preserving in-focus regions). If cropping is not enough, we use seam carving exploring a novel blur-aware energy function that concentrates the seams in blurred regions of the image. Experimental results show that the proposed blur-aware retargeting scheme works better at preserving in-focus objects than other competitive retargeting algorithms.
{"title":"Image retargeting based on spatially varying defocus blur map","authors":"Ali Karaali, C. Jung","doi":"10.1109/ICIP.2016.7532848","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7532848","url":null,"abstract":"This paper presents a new image retargeting method that explores blur information. Given the input image, we compute the blur map and estimate in-focus regions. For retargeting, we first try to crop image boundaries as much as possible (preserving in-focus regions). If cropping is not enough, we use seam carving exploring a novel blur-aware energy function that concentrates the seams in blurred regions of the image. Experimental results show that the proposed blur-aware retargeting scheme works better at preserving in-focus objects than other competitive retargeting algorithms.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"84 1","pages":"2693-2697"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79339218","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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