Over the last few years, a few researchers have made attempts to bridge the gap between Training (high-resolution gallery) and Testing (degraded, low quality probes) sets for Face Recognition under the surveillance conditions, using efficient low-level processing and statistical learning methods. In this paper, this challenging task of FR in degraded conditions have been handled using a Bag-of-Words (BOW) based approach for FR, combined with Domain Adaptation (DA). An adaptive-SIFT feature, extracted with spatially varying density at the fiducial points on the face. The SIFT features are used to form the dictionary of BOW and then combined with Local Binary Patterns (LBP). The sampling of the keypoints is denser in the discriminative parts of the face, while it is sparsely sampled at some less-interesting (pre-decided) zones of the face. An unsupervised method of DA has been used to boost the performance of FR using a BOW-based face representation. The unsupervised method of DA considers the source domain as the Training set and the target domain as the Test set. The novelty and contribution of this work on FR for surveillance applications, is the estimation of the transformation from source to target, based on the eigen-vectors of the source and the target domains using the discriminative BOW-based representation which combines two discriminative features extracted from the face. Performance analysis of the proposed method using ROC and CMC measures along with retrieval results on two real-world surveillance face datasets, shows the superiority over recent state-of-the-art techniques.
{"title":"Unsupervised method of Domain Adaptation on representation of discriminatory regions of the face image for surveillance face datasets","authors":"Suranjana Samanta, Samik Banerjee, Sukhendu Das","doi":"10.1145/2708463.2709060","DOIUrl":"https://doi.org/10.1145/2708463.2709060","url":null,"abstract":"Over the last few years, a few researchers have made attempts to bridge the gap between Training (high-resolution gallery) and Testing (degraded, low quality probes) sets for Face Recognition under the surveillance conditions, using efficient low-level processing and statistical learning methods. In this paper, this challenging task of FR in degraded conditions have been handled using a Bag-of-Words (BOW) based approach for FR, combined with Domain Adaptation (DA). An adaptive-SIFT feature, extracted with spatially varying density at the fiducial points on the face. The SIFT features are used to form the dictionary of BOW and then combined with Local Binary Patterns (LBP). The sampling of the keypoints is denser in the discriminative parts of the face, while it is sparsely sampled at some less-interesting (pre-decided) zones of the face. An unsupervised method of DA has been used to boost the performance of FR using a BOW-based face representation. The unsupervised method of DA considers the source domain as the Training set and the target domain as the Test set. The novelty and contribution of this work on FR for surveillance applications, is the estimation of the transformation from source to target, based on the eigen-vectors of the source and the target domains using the discriminative BOW-based representation which combines two discriminative features extracted from the face. Performance analysis of the proposed method using ROC and CMC measures along with retrieval results on two real-world surveillance face datasets, shows the superiority over recent state-of-the-art techniques.","PeriodicalId":289647,"journal":{"name":"Indo-Japan Conference on Perception and Machine Intelligence","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126603102","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}
The paper proposes an efficient human expression recognition method in transformed domain using discrete Contourlet transform (DCT). DCT represents smooth contour information in different directions reflecting human perception and so relevant to recognize facial expressions more accurately. Each face is decomposed using discrete Contourlet transform up to fourth level and coefficients of high frequency and low frequency components with varied scales and angles are obtained. Logarithmic invariant moments of directional coefficients at different levels and histogram analysis are used to build the feature vectors for classification. To reduce dimension of the feature vectors, directional subbands are selected by analyzing the entropy of the feature vectors. Support Vector Machine (SVM) is applied to classify different expressions using the proposed method. Experimental results show promising performance applied on JAFFE and Cohn-Kanade database compare to other transformed domain methods.
{"title":"An Efficient Expression Recognition Method using Contourlet Transform","authors":"S. Biswas, J. Sil","doi":"10.1145/2708463.2709036","DOIUrl":"https://doi.org/10.1145/2708463.2709036","url":null,"abstract":"The paper proposes an efficient human expression recognition method in transformed domain using discrete Contourlet transform (DCT). DCT represents smooth contour information in different directions reflecting human perception and so relevant to recognize facial expressions more accurately. Each face is decomposed using discrete Contourlet transform up to fourth level and coefficients of high frequency and low frequency components with varied scales and angles are obtained. Logarithmic invariant moments of directional coefficients at different levels and histogram analysis are used to build the feature vectors for classification. To reduce dimension of the feature vectors, directional subbands are selected by analyzing the entropy of the feature vectors. Support Vector Machine (SVM) is applied to classify different expressions using the proposed method. Experimental results show promising performance applied on JAFFE and Cohn-Kanade database compare to other transformed domain methods.","PeriodicalId":289647,"journal":{"name":"Indo-Japan Conference on Perception and Machine Intelligence","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123660889","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}
Poulami Ghosh, A. Mazumder, S. Bhattacharyya, D. Tibarewala
The primary objective of the brain is to collect information from the body via nervous system and then either process those information or store them. These stored information are basically known as memory. Over the years Electroencephalography (EEG) signals have been used quite efficiently to evaluate the cognitive state of brain. In this paper, we aim to study the cognitive functioning of the brain when a user memorizes and then recognizes an object from a list of similar ones. For this purpose, we have employed wavelet transform to extract the relevant features pertaining to the two mental activities and support vector machine to distinguish between the two states. The classification accuracy, thus obtained, is above 79% for all subjects. It is also inferred from this study that during memorization the signals from the frontal and temporal lobes are dominant and during recognition, signals from the frontal and parietal lobes are dominant.
{"title":"An EEG Study on Working Memory and Cognition","authors":"Poulami Ghosh, A. Mazumder, S. Bhattacharyya, D. Tibarewala","doi":"10.1145/2708463.2709065","DOIUrl":"https://doi.org/10.1145/2708463.2709065","url":null,"abstract":"The primary objective of the brain is to collect information from the body via nervous system and then either process those information or store them. These stored information are basically known as memory. Over the years Electroencephalography (EEG) signals have been used quite efficiently to evaluate the cognitive state of brain. In this paper, we aim to study the cognitive functioning of the brain when a user memorizes and then recognizes an object from a list of similar ones. For this purpose, we have employed wavelet transform to extract the relevant features pertaining to the two mental activities and support vector machine to distinguish between the two states. The classification accuracy, thus obtained, is above 79% for all subjects. It is also inferred from this study that during memorization the signals from the frontal and temporal lobes are dominant and during recognition, signals from the frontal and parietal lobes are dominant.","PeriodicalId":289647,"journal":{"name":"Indo-Japan Conference on Perception and Machine Intelligence","volume":"10 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132765212","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}
In this paper, we propose a combined classifier approach based on Inner Distance Shape Context (IDSC) and Local Binary Pattern (LBP) to classify shapes accurately. The inner-distance is insensitive to shape articulations and the LBP is invariant to rotation and shift of the shape. The Dynamic Programming (DP) in case of IDSC and Earth Movers Distance (EMD) metric in case of LBP were respectively employed to obtain similarity and hence used to classify given query shape based on maximum similarity value. The experiments are conducted on publicly available shape datasets namely MPEG-7, Kimia-99, Kimia-216, Myth and Tools-2D and the results are presented by means of Bulls eye score and precision-recall metric. The comparative study is also provided with the well known approaches to determine the retrieval accuracy of the proposed approach. The experimental results demonstrate that the proposed approach yield significant improvements over baseline shape matching algorithms.
{"title":"An unification of Inner Distance Shape Context and Local Binary Pattern for Shape Representation and Classification","authors":"B. H. Shekar, Bharathi Pilar, J. Kittler","doi":"10.1145/2708463.2709062","DOIUrl":"https://doi.org/10.1145/2708463.2709062","url":null,"abstract":"In this paper, we propose a combined classifier approach based on Inner Distance Shape Context (IDSC) and Local Binary Pattern (LBP) to classify shapes accurately. The inner-distance is insensitive to shape articulations and the LBP is invariant to rotation and shift of the shape. The Dynamic Programming (DP) in case of IDSC and Earth Movers Distance (EMD) metric in case of LBP were respectively employed to obtain similarity and hence used to classify given query shape based on maximum similarity value. The experiments are conducted on publicly available shape datasets namely MPEG-7, Kimia-99, Kimia-216, Myth and Tools-2D and the results are presented by means of Bulls eye score and precision-recall metric. The comparative study is also provided with the well known approaches to determine the retrieval accuracy of the proposed approach. The experimental results demonstrate that the proposed approach yield significant improvements over baseline shape matching algorithms.","PeriodicalId":289647,"journal":{"name":"Indo-Japan Conference on Perception and Machine Intelligence","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131013131","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}
This paper presents use of hum of a person as a biometric cue for person recognition task. Mel Frequency Cepstral Coefficients (MFCC) is found to be state-of-the-art in the voice biometrics. However, it is magnitude-based features and ignores the phase information. This paper shows the effectiveness of phase-based information extracted via Modified Group Delay Function (MODGDF). The features developed by Mel filtering of MODGDF spectrum are called Modified Group Delay Cepstral Coefficients (MGDCC). The paper demonstrates two types of fusion strategies, viz., score-level and feature-level. The experimental results show that overall performance is improved by 3 % if a score-level fusion is employed between MFCC and MGDCC and 19.78 % by feature-level fusion in terms of % Equal Error Rate (EER). These experimental results clearly indicate that incorporating phase information along with magnitude-based features can effectively captures person-specific characteristics in humming.
{"title":"Significance of Phase-based Features for Person Recognition Using Humming","authors":"Hardik B. Sailor, Maulik C. Madhavi, H. Patil","doi":"10.1145/2708463.2709035","DOIUrl":"https://doi.org/10.1145/2708463.2709035","url":null,"abstract":"This paper presents use of hum of a person as a biometric cue for person recognition task. Mel Frequency Cepstral Coefficients (MFCC) is found to be state-of-the-art in the voice biometrics. However, it is magnitude-based features and ignores the phase information. This paper shows the effectiveness of phase-based information extracted via Modified Group Delay Function (MODGDF). The features developed by Mel filtering of MODGDF spectrum are called Modified Group Delay Cepstral Coefficients (MGDCC). The paper demonstrates two types of fusion strategies, viz., score-level and feature-level. The experimental results show that overall performance is improved by 3 % if a score-level fusion is employed between MFCC and MGDCC and 19.78 % by feature-level fusion in terms of % Equal Error Rate (EER). These experimental results clearly indicate that incorporating phase information along with magnitude-based features can effectively captures person-specific characteristics in humming.","PeriodicalId":289647,"journal":{"name":"Indo-Japan Conference on Perception and Machine Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131098692","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}
Meera Thapar Khanna, Karan Rai, S. Chaudhury, Brejesh Lall
The paper tackles the task of preserving depth cue in single images for human perception of depth despite compression artifact. The process is mainly divided into 2 steps; saliency map computation and image compression on the basis of salient regions. The idea is that the regions which are less blurred should be more salient rather than blurred regions. For saliency computation the proposed method first obtains the blur information for the image using frequency information. Then we generate saliency map by region combining on the basis of this blur index. These saliency values are used to vary the quality parameter over the blocks in the image for JPEG compression. Experimental results show that the proposed scheme yields good results over normal JPEG compresssion in terms of PSNR values.
{"title":"Perceptual Depth Preserving Saliency based Image Compression","authors":"Meera Thapar Khanna, Karan Rai, S. Chaudhury, Brejesh Lall","doi":"10.1145/2708463.2709063","DOIUrl":"https://doi.org/10.1145/2708463.2709063","url":null,"abstract":"The paper tackles the task of preserving depth cue in single images for human perception of depth despite compression artifact. The process is mainly divided into 2 steps; saliency map computation and image compression on the basis of salient regions. The idea is that the regions which are less blurred should be more salient rather than blurred regions. For saliency computation the proposed method first obtains the blur information for the image using frequency information. Then we generate saliency map by region combining on the basis of this blur index. These saliency values are used to vary the quality parameter over the blocks in the image for JPEG compression. Experimental results show that the proposed scheme yields good results over normal JPEG compresssion in terms of PSNR values.","PeriodicalId":289647,"journal":{"name":"Indo-Japan Conference on Perception and Machine Intelligence","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131883482","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}
A. Fathima, V. Vaidehi, S. Vasuhi, Mukund Murrali, S. K. Parulkar
In this paper, an approach for pose invariant face detection is proposed by estimating the pose of detected face image. The approach comprises of detection of frontal and profile faces, followed by degree of freedom (DoF) identification and accurate estimation of pose. For pose invariant face detection, cascaded structure of Haar-like features along with adaboost classification has been used in this paper. The detected face image forms the input for DoF identification and poses estimation. The identification of DoF involves the classification of pose as Roll, Pitch and Yaw orientations. The DoF is identified by using proposed optimized Haar-like features and random forest classifier. For pose estimation, Haar based descriptor is constructed to have an efficient estimation of the angle of rotation in Roll, Pitch and Yaw. Haar based descriptor with weighted voting algorithm gives the final estimate of pose. The accuracy of the proposed system is found to be efficient than the existing work.
{"title":"Pose Invariant Face Detection in Video","authors":"A. Fathima, V. Vaidehi, S. Vasuhi, Mukund Murrali, S. K. Parulkar","doi":"10.1145/2708463.2709054","DOIUrl":"https://doi.org/10.1145/2708463.2709054","url":null,"abstract":"In this paper, an approach for pose invariant face detection is proposed by estimating the pose of detected face image. The approach comprises of detection of frontal and profile faces, followed by degree of freedom (DoF) identification and accurate estimation of pose. For pose invariant face detection, cascaded structure of Haar-like features along with adaboost classification has been used in this paper. The detected face image forms the input for DoF identification and poses estimation. The identification of DoF involves the classification of pose as Roll, Pitch and Yaw orientations. The DoF is identified by using proposed optimized Haar-like features and random forest classifier. For pose estimation, Haar based descriptor is constructed to have an efficient estimation of the angle of rotation in Roll, Pitch and Yaw. Haar based descriptor with weighted voting algorithm gives the final estimate of pose. The accuracy of the proposed system is found to be efficient than the existing work.","PeriodicalId":289647,"journal":{"name":"Indo-Japan Conference on Perception and Machine Intelligence","volume":"405 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123243923","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}
Modern urban areas constitute of rich mix of mobile devices with capability to be continuously connected to social networking infrastructure. This constitutes an ecosystem of urban pervasive devices that is large, decentralized and dynamic. Such an emerging urban pervasive computing scenario cannot be managed by traditional approaches of coordination and require innovative mechanisms such as nature-inspired approach of situated self-organization. In our previous work we had proposed a novel unified model of bio-inspired self-organization mechanism for coordination in urban pervasive environment. In this paper we propose a formal framework of bio-inspired model proposed earlier, including details of agent model, formal algorithm for distributed coordination and theoretical analysis of the algorithm. We also present simulation results to demonstrate adaptability, robustness and scalability of proposed algorithm.
{"title":"Framework for analysis of bio-inspired self-organization in urban pervasive system","authors":"S. Mukherjee, S. Chaudhury","doi":"10.1145/2708463.2709043","DOIUrl":"https://doi.org/10.1145/2708463.2709043","url":null,"abstract":"Modern urban areas constitute of rich mix of mobile devices with capability to be continuously connected to social networking infrastructure. This constitutes an ecosystem of urban pervasive devices that is large, decentralized and dynamic. Such an emerging urban pervasive computing scenario cannot be managed by traditional approaches of coordination and require innovative mechanisms such as nature-inspired approach of situated self-organization. In our previous work we had proposed a novel unified model of bio-inspired self-organization mechanism for coordination in urban pervasive environment. In this paper we propose a formal framework of bio-inspired model proposed earlier, including details of agent model, formal algorithm for distributed coordination and theoretical analysis of the algorithm. We also present simulation results to demonstrate adaptability, robustness and scalability of proposed algorithm.","PeriodicalId":289647,"journal":{"name":"Indo-Japan Conference on Perception and Machine Intelligence","volume":"166 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133591469","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}
The process of 2D to 3D involves depth map estimation of input image and stereo pair generation using that depth image. Fully automatic techniques are not able to give very high quality output and methods which involve human operators are costly and time consuming. We propose a semiautomatic technique which incorporates monocular depth cues if these are not present in the input image. We propose a method which first includes defocusing factor in the input image semi-automatically and then estimates depth from focus cue. We also used motion parallax as another monocular depth cue and evaluate the performance of final depth map. Gradient Magnitude Similarity Deviation which is a highly efficient perceptual quality index is used for the performance evaluation of final depth map. GMSD score is lowest in case of final depth map which shows highest quality. Finally, stereo pair is generated using original image and its depth map.
{"title":"Stereo Pair Generation by Introducing Bokeh Effect in 2D Images","authors":"Nidhi Chahal, S. Chaudhury","doi":"10.1145/2708463.2709069","DOIUrl":"https://doi.org/10.1145/2708463.2709069","url":null,"abstract":"The process of 2D to 3D involves depth map estimation of input image and stereo pair generation using that depth image. Fully automatic techniques are not able to give very high quality output and methods which involve human operators are costly and time consuming. We propose a semiautomatic technique which incorporates monocular depth cues if these are not present in the input image. We propose a method which first includes defocusing factor in the input image semi-automatically and then estimates depth from focus cue. We also used motion parallax as another monocular depth cue and evaluate the performance of final depth map. Gradient Magnitude Similarity Deviation which is a highly efficient perceptual quality index is used for the performance evaluation of final depth map. GMSD score is lowest in case of final depth map which shows highest quality. Finally, stereo pair is generated using original image and its depth map.","PeriodicalId":289647,"journal":{"name":"Indo-Japan Conference on Perception and Machine Intelligence","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131514438","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}
Facial expression plays an important role as non-verbal information in dialogue communication. People complement verbal information with non-verbal information to hold a smooth conversation. To date, collaboration between information engineering and psychology has enabled development of human facial expression recognition technology. Most of the related studies, however, have ended up with a laboratory-based demonstration. In this paper, focusing on the facial expression database, we overview the facial expression recognition technology. Then we present a simple application using the current status of the facial expression recognition technology. Finally we show prospect of the technology with a more realistic application.
{"title":"Interactive Facial Expression Reader and Extension to First Impression Improver","authors":"T. Yamazaki, Ken'ichi Maehara, K. Enomoto","doi":"10.1145/2708463.2709059","DOIUrl":"https://doi.org/10.1145/2708463.2709059","url":null,"abstract":"Facial expression plays an important role as non-verbal information in dialogue communication. People complement verbal information with non-verbal information to hold a smooth conversation. To date, collaboration between information engineering and psychology has enabled development of human facial expression recognition technology. Most of the related studies, however, have ended up with a laboratory-based demonstration. In this paper, focusing on the facial expression database, we overview the facial expression recognition technology. Then we present a simple application using the current status of the facial expression recognition technology. Finally we show prospect of the technology with a more realistic application.","PeriodicalId":289647,"journal":{"name":"Indo-Japan Conference on Perception and Machine Intelligence","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134549317","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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