Pub Date : 2017-11-01DOI: 10.1109/IPTA.2017.8310110
Viktor Kessler, Patrick Thiam, Mohammadreza Amirian, F. Schwenker
In the last years a lot of effort was made in predicting the heart rate of a participant with remote Photo-plethysmography (rPPG) from the video channel but only few authors used it as a biosignal for classification of e.g. stress. In this work, we present the rPPG signal as a new modality for pain classification and evaluate the benefit of the three color channels (red, green, blue) of the rPPG signal. In short the rPPG signal is filtered in multiple frequency ranges to extract the heart rate and the respiration rate as biophysiological signals. Then the pain is classified with a Support Vector Machine (SVM) and Random Forest classifier. The performance is compared to the electrocardiogram (ECG) and the respiration from the biosignal amplifier and facial landmark features from the video. The results show that the rPPG signal can be used for pain classification, especially its low frequencies.
{"title":"Pain recognition with camera photoplethysmography","authors":"Viktor Kessler, Patrick Thiam, Mohammadreza Amirian, F. Schwenker","doi":"10.1109/IPTA.2017.8310110","DOIUrl":"https://doi.org/10.1109/IPTA.2017.8310110","url":null,"abstract":"In the last years a lot of effort was made in predicting the heart rate of a participant with remote Photo-plethysmography (rPPG) from the video channel but only few authors used it as a biosignal for classification of e.g. stress. In this work, we present the rPPG signal as a new modality for pain classification and evaluate the benefit of the three color channels (red, green, blue) of the rPPG signal. In short the rPPG signal is filtered in multiple frequency ranges to extract the heart rate and the respiration rate as biophysiological signals. Then the pain is classified with a Support Vector Machine (SVM) and Random Forest classifier. The performance is compared to the electrocardiogram (ECG) and the respiration from the biosignal amplifier and facial landmark features from the video. The results show that the rPPG signal can be used for pain classification, especially its low frequencies.","PeriodicalId":316356,"journal":{"name":"2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123615011","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 : 2017-11-01DOI: 10.1109/IPTA.2017.8310099
Rachida Es-salhi, I. Daoudi, H. Ouardi
Image co-segmentation addresses the problem of simultaneously extracting the common targets from a set of related images. However, designing a robust and efficient co-segmentation algorithm is a challenging work because of the variety and complexity of the object and the background. In this paper, we propose a new semi-supervised method to extract foreground object from an image collection. The proposed method is composed of three tasks: 1) object proposal generation, 2) object prior propagation and 3) foreground extraction. The main idea of this paper is to transfer the segmentation from a subset of training images to test images. The comparison experiments conducted on public datasets iCoseg and MSRC demonstrate the performance of the proposed method.
{"title":"A new semi-supervised method for image co-segmentation","authors":"Rachida Es-salhi, I. Daoudi, H. Ouardi","doi":"10.1109/IPTA.2017.8310099","DOIUrl":"https://doi.org/10.1109/IPTA.2017.8310099","url":null,"abstract":"Image co-segmentation addresses the problem of simultaneously extracting the common targets from a set of related images. However, designing a robust and efficient co-segmentation algorithm is a challenging work because of the variety and complexity of the object and the background. In this paper, we propose a new semi-supervised method to extract foreground object from an image collection. The proposed method is composed of three tasks: 1) object proposal generation, 2) object prior propagation and 3) foreground extraction. The main idea of this paper is to transfer the segmentation from a subset of training images to test images. The comparison experiments conducted on public datasets iCoseg and MSRC demonstrate the performance of the proposed method.","PeriodicalId":316356,"journal":{"name":"2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128723888","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 : 2017-11-01DOI: 10.1109/IPTA.2017.8310096
D. Manger, D. Willersinn
In this work, we propose an extension of established image retrieval models which are based on the bag-of-words representation, i.e. on models which quantize local features such as SIFT to leverage an inverted file indexing scheme for speedup. Since the quantization of local features impairs their discriminability, the ability to retrieve those database images which show the same object or scene to a given query image is decreasing with the growing number of images in the database. We address this issue by extending a quantized local feature with information from its local spatial neighborhood incorporating a representation based on pooling features from deep convolutional neural network layer outputs. Using four public datasets, we evaluate both the discriminability of the representation and its overall performance in a large-scale image retrieval setup.
{"title":"Enlarging the discriminability of bag-of-words representations with deep convolutional features","authors":"D. Manger, D. Willersinn","doi":"10.1109/IPTA.2017.8310096","DOIUrl":"https://doi.org/10.1109/IPTA.2017.8310096","url":null,"abstract":"In this work, we propose an extension of established image retrieval models which are based on the bag-of-words representation, i.e. on models which quantize local features such as SIFT to leverage an inverted file indexing scheme for speedup. Since the quantization of local features impairs their discriminability, the ability to retrieve those database images which show the same object or scene to a given query image is decreasing with the growing number of images in the database. We address this issue by extending a quantized local feature with information from its local spatial neighborhood incorporating a representation based on pooling features from deep convolutional neural network layer outputs. Using four public datasets, we evaluate both the discriminability of the representation and its overall performance in a large-scale image retrieval setup.","PeriodicalId":316356,"journal":{"name":"2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129945159","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 : 2017-11-01DOI: 10.1109/IPTA.2017.8310077
M. A. Ferrer-Ballester, Moisés Díaz, C. Carmona-Duarte
Estimating the salient points in 8-connected curves from handwritten signature is a difficult task due to their relation to the writer neuromotor system. This paper faces up this topic proposing a two-steps perceptual important points estimation method: the first step estimates the sharper salient points by a curvature analysis at multiple scales, whereas the second step estimates the smoother salient points relying on circular shapes between estimated salient points in step one. In this approach, both sharper and smoother salient points represent the set of perceptual important points in an eight connected signature trajectory. Our validations, conducted on 2112 signatures from 132 users of the BiosecurID database, are focused on i) evaluating the number of estimated perceptual important points; ii) evaluating their locations in the trajectory and iii) evaluating the accuracy of the estimated duration of the signatures from the number of perceptual important points. The obtained results are encouraging for new developments in handwriting analysis based on this procedure.
{"title":"Two-steps perceptual important points estimator in 8-connected curves from handwritten signature","authors":"M. A. Ferrer-Ballester, Moisés Díaz, C. Carmona-Duarte","doi":"10.1109/IPTA.2017.8310077","DOIUrl":"https://doi.org/10.1109/IPTA.2017.8310077","url":null,"abstract":"Estimating the salient points in 8-connected curves from handwritten signature is a difficult task due to their relation to the writer neuromotor system. This paper faces up this topic proposing a two-steps perceptual important points estimation method: the first step estimates the sharper salient points by a curvature analysis at multiple scales, whereas the second step estimates the smoother salient points relying on circular shapes between estimated salient points in step one. In this approach, both sharper and smoother salient points represent the set of perceptual important points in an eight connected signature trajectory. Our validations, conducted on 2112 signatures from 132 users of the BiosecurID database, are focused on i) evaluating the number of estimated perceptual important points; ii) evaluating their locations in the trajectory and iii) evaluating the accuracy of the estimated duration of the signatures from the number of perceptual important points. The obtained results are encouraging for new developments in handwriting analysis based on this procedure.","PeriodicalId":316356,"journal":{"name":"2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127890399","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 : 2017-11-01DOI: 10.1109/IPTA.2017.8310106
Koffi Eddy Ihou, N. Bouguila
As a response to the limitations of the LDA in topic modeling and large scale applications, several extensions using flexible priors have been introduced to expose the problem of topic correlation. Models such as CTM, PAM, GD-LDA, and LGDA have been able to explore and capture semantic relationships between topics. However, many of these models suffer from incomplete generative processes which affect inferences efficiency. In addition, knowing these traditional inference techniques carry major limitations, the new approach in this paper, the CVB-LGDA is an extension to the state-of-the-art. It reconciles a complete generative process to a robust inference technique in a topic correlation framework. Its performance in image classification shows its robustness.
{"title":"A new latent generalized dirichlet allocation model for image classification","authors":"Koffi Eddy Ihou, N. Bouguila","doi":"10.1109/IPTA.2017.8310106","DOIUrl":"https://doi.org/10.1109/IPTA.2017.8310106","url":null,"abstract":"As a response to the limitations of the LDA in topic modeling and large scale applications, several extensions using flexible priors have been introduced to expose the problem of topic correlation. Models such as CTM, PAM, GD-LDA, and LGDA have been able to explore and capture semantic relationships between topics. However, many of these models suffer from incomplete generative processes which affect inferences efficiency. In addition, knowing these traditional inference techniques carry major limitations, the new approach in this paper, the CVB-LGDA is an extension to the state-of-the-art. It reconciles a complete generative process to a robust inference technique in a topic correlation framework. Its performance in image classification shows its robustness.","PeriodicalId":316356,"journal":{"name":"2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129157221","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 : 2017-10-11DOI: 10.1109/IPTA.2017.8310144
Morteza Babaie, H. Tizhoosh, Seyed Amin Khatami, M. Shiri
Radon transform and its inverse operation are important techniques in medical imaging tasks. Recently, there has been renewed interest in Radon transform for applications such as content-based medical image retrieval. However, all studies so far have used Radon transform as a global or quasi-global image descriptor by extracting projections of the whole image or large sub-images. This paper attempts to show that the dense sampling to generate the histogram of local Radon projections has a much higher discrimination capability than the global one. In this paper, we introduce Local Radon Descriptor (LRD) and apply it to the IRMA dataset, which contains 14,410 x-ray images as well as to the INRIA Holidays dataset with 1,990 images. Our results show significant improvement in retrieval performance by using LRD versus its global version. We also demonstrate that LRD can deliver results comparable to well-established descriptors like LBP and HOG.
{"title":"Local radon descriptors for image search","authors":"Morteza Babaie, H. Tizhoosh, Seyed Amin Khatami, M. Shiri","doi":"10.1109/IPTA.2017.8310144","DOIUrl":"https://doi.org/10.1109/IPTA.2017.8310144","url":null,"abstract":"Radon transform and its inverse operation are important techniques in medical imaging tasks. Recently, there has been renewed interest in Radon transform for applications such as content-based medical image retrieval. However, all studies so far have used Radon transform as a global or quasi-global image descriptor by extracting projections of the whole image or large sub-images. This paper attempts to show that the dense sampling to generate the histogram of local Radon projections has a much higher discrimination capability than the global one. In this paper, we introduce Local Radon Descriptor (LRD) and apply it to the IRMA dataset, which contains 14,410 x-ray images as well as to the INRIA Holidays dataset with 1,990 images. Our results show significant improvement in retrieval performance by using LRD versus its global version. We also demonstrate that LRD can deliver results comparable to well-established descriptors like LBP and HOG.","PeriodicalId":316356,"journal":{"name":"2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114519697","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 : 2017-10-11DOI: 10.1109/IPTA.2017.8310149
Brady Kieffer, Morteza Babaie, S. Kalra, H. Tizhoosh
We explore the problem of classification within a medical image data-set based on a feature vector extracted from the deepest layer of pre-trained Convolution Neural Networks. We have used feature vectors from several pre-trained structures, including networks with/without transfer learning to evaluate the performance of pre-trained deep features versus CNNs which have been trained by that specific dataset as well as the impact of transfer learning with a small number of samples. All experiments are done on Kimia Path24 dataset which consists of 27,055 histopathology training patches in 24 tissue texture classes along with 1,325 test patches for evaluation. The result shows that pre-trained networks are quite competitive against training from scratch. As well, fine-tuning does not seem to add any tangible improvement for VGG16 to justify additional training while we observed considerable improvement in retrieval and classification accuracy when we fine-tuned the Inception structure.
{"title":"Convolutional neural networks for histopathology image classification: Training vs. Using pre-trained networks","authors":"Brady Kieffer, Morteza Babaie, S. Kalra, H. Tizhoosh","doi":"10.1109/IPTA.2017.8310149","DOIUrl":"https://doi.org/10.1109/IPTA.2017.8310149","url":null,"abstract":"We explore the problem of classification within a medical image data-set based on a feature vector extracted from the deepest layer of pre-trained Convolution Neural Networks. We have used feature vectors from several pre-trained structures, including networks with/without transfer learning to evaluate the performance of pre-trained deep features versus CNNs which have been trained by that specific dataset as well as the impact of transfer learning with a small number of samples. All experiments are done on Kimia Path24 dataset which consists of 27,055 histopathology training patches in 24 tissue texture classes along with 1,325 test patches for evaluation. The result shows that pre-trained networks are quite competitive against training from scratch. As well, fine-tuning does not seem to add any tangible improvement for VGG16 to justify additional training while we observed considerable improvement in retrieval and classification accuracy when we fine-tuned the Inception structure.","PeriodicalId":316356,"journal":{"name":"2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126914452","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 : 2017-09-30DOI: 10.1109/IPTA.2017.8853988
Houssem Chatbri, Marlon Oliveira, Kevin McGuinness, S. Little, K. Kameyama, P. Kwan, Alistair Sutherland, N. O’Connor
In this work, we present a method for automatic topic classification of educational videos using a speech transcript transform. Our method works as follows: First, speech recognition is used to generate video transcripts. Then, the transcripts are converted into images using a statistical cooccurrence transformation that we designed. Finally, a classifier is used to produce video category labels for a transcript image input. For our classifiers, we report results using a convolutional neural network (CNN) and a principal component analysis (PCA) model. In order to evaluate our method, we used the Khan Academy on a Stick dataset that contains 2,545 videos, where each video is labeled with one or two of 13 categories. Experiments show that our method is effective and strongly competitive against other supervised learning-based methods.
{"title":"Educational video classification by using a transcript to image transform and supervised learning","authors":"Houssem Chatbri, Marlon Oliveira, Kevin McGuinness, S. Little, K. Kameyama, P. Kwan, Alistair Sutherland, N. O’Connor","doi":"10.1109/IPTA.2017.8853988","DOIUrl":"https://doi.org/10.1109/IPTA.2017.8853988","url":null,"abstract":"In this work, we present a method for automatic topic classification of educational videos using a speech transcript transform. Our method works as follows: First, speech recognition is used to generate video transcripts. Then, the transcripts are converted into images using a statistical cooccurrence transformation that we designed. Finally, a classifier is used to produce video category labels for a transcript image input. For our classifiers, we report results using a convolutional neural network (CNN) and a principal component analysis (PCA) model. In order to evaluate our method, we used the Khan Academy on a Stick dataset that contains 2,545 videos, where each video is labeled with one or two of 13 categories. Experiments show that our method is effective and strongly competitive against other supervised learning-based methods.","PeriodicalId":316356,"journal":{"name":"2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121979826","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 : 2017-03-25DOI: 10.1109/IPTA.2017.8310084
A. Rhodes, Jordan M. Witte, B. Jedynak, Melanie Mitchell
We develop a general-purpose algorithm using a Bayesian optimization framework for the efficient refinement of object proposals. While recent research has achieved substantial progress for object localization and related objectives in computer vision, current state-of-the-art object localization procedures are nevertheless encumbered by inefficiency and inaccuracy. We present a novel, computationally efficient method for refining inaccurate bounding-box proposals for a target object using Bayesian optimization. Offline, image features from a convolutional neural network are used to train a model to predict an object proposal's offset distance from a target object. Online, this model is used in a Bayesian active search to improve inaccurate object proposals. In experiments, we compare our approach to a state-of-the-art bounding-box regression method for localization refinement of pedestrian object proposals.
{"title":"Bayesian optimization for refining object proposals","authors":"A. Rhodes, Jordan M. Witte, B. Jedynak, Melanie Mitchell","doi":"10.1109/IPTA.2017.8310084","DOIUrl":"https://doi.org/10.1109/IPTA.2017.8310084","url":null,"abstract":"We develop a general-purpose algorithm using a Bayesian optimization framework for the efficient refinement of object proposals. While recent research has achieved substantial progress for object localization and related objectives in computer vision, current state-of-the-art object localization procedures are nevertheless encumbered by inefficiency and inaccuracy. We present a novel, computationally efficient method for refining inaccurate bounding-box proposals for a target object using Bayesian optimization. Offline, image features from a convolutional neural network are used to train a model to predict an object proposal's offset distance from a target object. Online, this model is used in a Bayesian active search to improve inaccurate object proposals. In experiments, we compare our approach to a state-of-the-art bounding-box regression method for localization refinement of pedestrian object proposals.","PeriodicalId":316356,"journal":{"name":"2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133519490","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 : 2015-07-28DOI: 10.1109/IPTA.2017.8310112
Luiz G. Hafemann, R. Sabourin, Luiz Oliveira
The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. The objective of signature verification systems is to discriminate if a given signature is genuine (produced by the claimed individual), or a forgery (produced by an impostor). This has demonstrated to be a challenging task, in particular in the offline (static) scenario, that uses images of scanned signatures, where the dynamic information about the signing process is not available. Many advancements have been proposed in the literature in the last 5–10 years, most notably the application of Deep Learning methods to learn feature representations from signature images. In this paper, we present how the problem has been handled in the past few decades, analyze the recent advancements in the field, and the potential directions for future research.
{"title":"Offline handwritten signature verification — Literature review","authors":"Luiz G. Hafemann, R. Sabourin, Luiz Oliveira","doi":"10.1109/IPTA.2017.8310112","DOIUrl":"https://doi.org/10.1109/IPTA.2017.8310112","url":null,"abstract":"The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. The objective of signature verification systems is to discriminate if a given signature is genuine (produced by the claimed individual), or a forgery (produced by an impostor). This has demonstrated to be a challenging task, in particular in the offline (static) scenario, that uses images of scanned signatures, where the dynamic information about the signing process is not available. Many advancements have been proposed in the literature in the last 5–10 years, most notably the application of Deep Learning methods to learn feature representations from signature images. In this paper, we present how the problem has been handled in the past few decades, analyze the recent advancements in the field, and the potential directions for future research.","PeriodicalId":316356,"journal":{"name":"2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125284268","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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