Pub Date : 2013-07-14DOI: 10.1109/ICWAPR.2013.6599286
C. Guo, Yuanyan Tang, Changsong Liu, Jia Duan
This paper describes a post-processing approach for Japanese character recognition based on dictionary. By the analysis of experimental data in the processing of OCR, we find that some segmentation and recognition results do not conform to the rules of lexical and just generate the character based on the shape. If the fonts of pending recognized characters are similar with the others, it will easily lead to going wrong in the processing of OCR. For these errors we put forward an idea based on the Limited Length Segmentation Matching and the Bayesian Statistical Classifier. Through the above method, most of the font recognized mistakes can be solved. By the experimental results, it can be proved that this method is an effective way to improve the recognized rate of Japanese character.
{"title":"A Japanese OCR post-processing approach based on dictionary matching","authors":"C. Guo, Yuanyan Tang, Changsong Liu, Jia Duan","doi":"10.1109/ICWAPR.2013.6599286","DOIUrl":"https://doi.org/10.1109/ICWAPR.2013.6599286","url":null,"abstract":"This paper describes a post-processing approach for Japanese character recognition based on dictionary. By the analysis of experimental data in the processing of OCR, we find that some segmentation and recognition results do not conform to the rules of lexical and just generate the character based on the shape. If the fonts of pending recognized characters are similar with the others, it will easily lead to going wrong in the processing of OCR. For these errors we put forward an idea based on the Limited Length Segmentation Matching and the Bayesian Statistical Classifier. Through the above method, most of the font recognized mistakes can be solved. By the experimental results, it can be proved that this method is an effective way to improve the recognized rate of Japanese character.","PeriodicalId":236156,"journal":{"name":"2013 International Conference on Wavelet Analysis and Pattern Recognition","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134095197","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 : 2013-07-14DOI: 10.1109/ICWAPR.2013.6599319
Yi Tang, Hong Chen
Single-image super-resolution is firstly treated as a problem of matrix-value regression. By using matrix-value regression techniques, some desired properties are found. Firstly, the matrix-value regression technique greatly promotes the efficiency of learning from image pairs. As a result, the matrix-value regression based super-resolution algorithm can be smoothly applied to big data setting. Secondly, the matrix-value regression technique makes it possible to design a patch-to-patch super-resolution algorithm. As far as we know, it is the first patch-to-patch algorithm in the field of single-image super-resolution. Experimental results have shown the efficiency of the matrix-value regression based super-resolution algorithm in the training process. Meanwhile, it is also shown that the performance of the proposed algorithm is competitive to most of state-of-the-art super-resolution algorithms.
{"title":"Matrix-value regression for single-image super-resolution","authors":"Yi Tang, Hong Chen","doi":"10.1109/ICWAPR.2013.6599319","DOIUrl":"https://doi.org/10.1109/ICWAPR.2013.6599319","url":null,"abstract":"Single-image super-resolution is firstly treated as a problem of matrix-value regression. By using matrix-value regression techniques, some desired properties are found. Firstly, the matrix-value regression technique greatly promotes the efficiency of learning from image pairs. As a result, the matrix-value regression based super-resolution algorithm can be smoothly applied to big data setting. Secondly, the matrix-value regression technique makes it possible to design a patch-to-patch super-resolution algorithm. As far as we know, it is the first patch-to-patch algorithm in the field of single-image super-resolution. Experimental results have shown the efficiency of the matrix-value regression based super-resolution algorithm in the training process. Meanwhile, it is also shown that the performance of the proposed algorithm is competitive to most of state-of-the-art super-resolution algorithms.","PeriodicalId":236156,"journal":{"name":"2013 International Conference on Wavelet Analysis and Pattern Recognition","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123528283","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 : 2013-07-14DOI: 10.1109/ICWAPR.2013.6599316
Junjie Hu, D. Yeung
As the Internet refreshes every day, a large scale of images are generated online which present a challenge to image classification problems. Firstly, the classifier once trained by the old training set is not able to describe all the characteristics of a class when new samples appear. Secondly, to train a classifier using all the upcoming samples can take a long time so that the speed of updating the classifier is much slower than the speed of new data generation. Thirdly, the newly generated images may be duplicate or similar to current training samples with minor variance, hence training by these minor informative images will waste lots of time and resources, Samples being continuously misclassified by the updated classifiers should be laid with more weight in future update process than other easily classified samples. In this paper, we propose an Incremental learning method using Error and Sensitivity Analysis (IESA) of Multiple Classifier System (MCS) for upcoming images. Radial Basis Function Neural Network (RBFNN) is used to classify upcoming images firstly and misclassified images with large sensitivity are selected for the following updating process. Experimental results on a large scale image dataset convince the efficiency of the IESA strategy.
{"title":"Incremental learning using error and sensitivity analysis of MCS for Image classification","authors":"Junjie Hu, D. Yeung","doi":"10.1109/ICWAPR.2013.6599316","DOIUrl":"https://doi.org/10.1109/ICWAPR.2013.6599316","url":null,"abstract":"As the Internet refreshes every day, a large scale of images are generated online which present a challenge to image classification problems. Firstly, the classifier once trained by the old training set is not able to describe all the characteristics of a class when new samples appear. Secondly, to train a classifier using all the upcoming samples can take a long time so that the speed of updating the classifier is much slower than the speed of new data generation. Thirdly, the newly generated images may be duplicate or similar to current training samples with minor variance, hence training by these minor informative images will waste lots of time and resources, Samples being continuously misclassified by the updated classifiers should be laid with more weight in future update process than other easily classified samples. In this paper, we propose an Incremental learning method using Error and Sensitivity Analysis (IESA) of Multiple Classifier System (MCS) for upcoming images. Radial Basis Function Neural Network (RBFNN) is used to classify upcoming images firstly and misclassified images with large sensitivity are selected for the following updating process. Experimental results on a large scale image dataset convince the efficiency of the IESA strategy.","PeriodicalId":236156,"journal":{"name":"2013 International Conference on Wavelet Analysis and Pattern Recognition","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116332391","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 : 2013-07-14DOI: 10.1109/ICWAPR.2013.6599299
L. Gang, Ning Shangkun, You Yugan, Wen Guang-lei, Zheng Siguo
Moving target detection is an important part of video target tracking. Good moving target detection makes video track more effective. This paper proposes a new algorithm based on the traditional three-frame differential method comparison. The shortage of traditional three-frame differential method is pointed out. Combined with Canny edge detection algorithm, the improved three frame differential algorithm makes moving target detected containing more complete information. This new algorithm takes advantage of good performances of three-frame-difference method and background subtraction method adequately. The proposed method is simple and experimental results show that it can accurately detect moving targets.
{"title":"An improved moving objects detection algorithm","authors":"L. Gang, Ning Shangkun, You Yugan, Wen Guang-lei, Zheng Siguo","doi":"10.1109/ICWAPR.2013.6599299","DOIUrl":"https://doi.org/10.1109/ICWAPR.2013.6599299","url":null,"abstract":"Moving target detection is an important part of video target tracking. Good moving target detection makes video track more effective. This paper proposes a new algorithm based on the traditional three-frame differential method comparison. The shortage of traditional three-frame differential method is pointed out. Combined with Canny edge detection algorithm, the improved three frame differential algorithm makes moving target detected containing more complete information. This new algorithm takes advantage of good performances of three-frame-difference method and background subtraction method adequately. The proposed method is simple and experimental results show that it can accurately detect moving targets.","PeriodicalId":236156,"journal":{"name":"2013 International Conference on Wavelet Analysis and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126552541","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 : 2013-07-14DOI: 10.1109/ICWAPR.2013.6599322
Xiaoyan Liu, Xiangchu Feng, Yu Han
The non-local means method (NLM) is widely used in image denoising. However, the performance of this method heavily depends on the choice of smoothness parameters. In this paper, we present a novel multi-scale non-local means method (MNLM) for image denoising. By introducing the multi-scale decomposition of images, our method can avoid the difficulty of choosing the smoothness parameters. Compared with the classical NLM method, MNLM not only improves the accuracy of the measurement of similarity, but also generates better denoising results.
{"title":"Multiscale nonlocal means for image denoising","authors":"Xiaoyan Liu, Xiangchu Feng, Yu Han","doi":"10.1109/ICWAPR.2013.6599322","DOIUrl":"https://doi.org/10.1109/ICWAPR.2013.6599322","url":null,"abstract":"The non-local means method (NLM) is widely used in image denoising. However, the performance of this method heavily depends on the choice of smoothness parameters. In this paper, we present a novel multi-scale non-local means method (MNLM) for image denoising. By introducing the multi-scale decomposition of images, our method can avoid the difficulty of choosing the smoothness parameters. Compared with the classical NLM method, MNLM not only improves the accuracy of the measurement of similarity, but also generates better denoising results.","PeriodicalId":236156,"journal":{"name":"2013 International Conference on Wavelet Analysis and Pattern Recognition","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132840365","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 : 2013-07-14DOI: 10.1109/ICWAPR.2013.6599310
Songzhao Xie, Chengyou Wang, Zhiqiang Yang
In order to avoid data redundancy, many methods of compressing Bayer images before interpolation were proposed. Structure conversion presented by Koh has been an effective method for Bayer patterned images to improve the compression quality. On this basis, Xie proposed an improved structure conversion algorithm to further improve the compression performance. Combining the improved structure conversion using 9/7 wavelet transform with lifting scheme and set partitioning in hierarchical trees (SPIHT) algorithm, this paper proposes an efficient method in digital cameras with color filter array (CFA). Experimental results show that the proposed algorithm outperforms the improved structure conversion algorithm both in objective and subjective aspects.
{"title":"Image compression using wavelet transform with lifting scheme and SPIHT in digital cameras for Bayer CFA","authors":"Songzhao Xie, Chengyou Wang, Zhiqiang Yang","doi":"10.1109/ICWAPR.2013.6599310","DOIUrl":"https://doi.org/10.1109/ICWAPR.2013.6599310","url":null,"abstract":"In order to avoid data redundancy, many methods of compressing Bayer images before interpolation were proposed. Structure conversion presented by Koh has been an effective method for Bayer patterned images to improve the compression quality. On this basis, Xie proposed an improved structure conversion algorithm to further improve the compression performance. Combining the improved structure conversion using 9/7 wavelet transform with lifting scheme and set partitioning in hierarchical trees (SPIHT) algorithm, this paper proposes an efficient method in digital cameras with color filter array (CFA). Experimental results show that the proposed algorithm outperforms the improved structure conversion algorithm both in objective and subjective aspects.","PeriodicalId":236156,"journal":{"name":"2013 International Conference on Wavelet Analysis and Pattern Recognition","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134131193","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 proposed image denoising method investigates a novel similar block searching strategy based on non-local Markov-Chain Monte Carlo (MCMC) sampling with alterable direction. Firstly, observed image is decomposed with 2-D wavelet transform to obtain a series sub-band images in spatial Following, the similar matching block clusters of each sub-band image in spatial are obtained by taking the different sampling which obey different directional elliptical Gaussian distributions. The matrix of similar patches cluster is decomposed by singular value decomposition method, and the image noise is suppressed by applying the low rank structure from decomposing. The simulation results show that the proposed method outperforms the Block Method of 3-Dimension (BM3DJ and the Non-Local Means (NLM) methods in computational-complexity. The proposed method has a better performance in protecting image details compared with the NLM method, and has some advantages over the BM3D method in terms of visual quality.
{"title":"An image denoising method based on Markov-Chain Monte Carlo sampling with alterable direction and low rank approximation","authors":"Liang Luo, Xiangchu Feng, Xiaoping Li, Xiaoyan Liu, Xueqin Zhou","doi":"10.1109/ICWAPR.2013.6599298","DOIUrl":"https://doi.org/10.1109/ICWAPR.2013.6599298","url":null,"abstract":"The proposed image denoising method investigates a novel similar block searching strategy based on non-local Markov-Chain Monte Carlo (MCMC) sampling with alterable direction. Firstly, observed image is decomposed with 2-D wavelet transform to obtain a series sub-band images in spatial Following, the similar matching block clusters of each sub-band image in spatial are obtained by taking the different sampling which obey different directional elliptical Gaussian distributions. The matrix of similar patches cluster is decomposed by singular value decomposition method, and the image noise is suppressed by applying the low rank structure from decomposing. The simulation results show that the proposed method outperforms the Block Method of 3-Dimension (BM3DJ and the Non-Local Means (NLM) methods in computational-complexity. The proposed method has a better performance in protecting image details compared with the NLM method, and has some advantages over the BM3D method in terms of visual quality.","PeriodicalId":236156,"journal":{"name":"2013 International Conference on Wavelet Analysis and Pattern Recognition","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133257856","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 : 2013-07-14DOI: 10.1109/ICWAPR.2013.6599312
Q. Xie, J. He, L. Qian, S. Mita, X. Chen, A. Jiang
This paper solves the image fusion problem by TV-L1 energy function. The energy function mainly consists of two components. One ensures the injection of correlated detail spatial information by using the total variation (TV) method. The other integrates the detail information from gradient representation into the fused result based on the TV method. The spectral information is preserved through L1 norm based on data fitting term. The main feature of the fusion formulation is that it obtains more accurate spectral information through L1 norm and directly injects the fused result with the spatial gradient information with TV term. Since the energy function is non-smooth, the corresponding fused band with the minimum energy is obtained through primal-dual hybrid gradient algorithm. Experimental results demonstrate the superiority of the proposed method over some classical methods.
{"title":"Image fusion based on TV-L1 function","authors":"Q. Xie, J. He, L. Qian, S. Mita, X. Chen, A. Jiang","doi":"10.1109/ICWAPR.2013.6599312","DOIUrl":"https://doi.org/10.1109/ICWAPR.2013.6599312","url":null,"abstract":"This paper solves the image fusion problem by TV-L1 energy function. The energy function mainly consists of two components. One ensures the injection of correlated detail spatial information by using the total variation (TV) method. The other integrates the detail information from gradient representation into the fused result based on the TV method. The spectral information is preserved through L1 norm based on data fitting term. The main feature of the fusion formulation is that it obtains more accurate spectral information through L1 norm and directly injects the fused result with the spatial gradient information with TV term. Since the energy function is non-smooth, the corresponding fused band with the minimum energy is obtained through primal-dual hybrid gradient algorithm. Experimental results demonstrate the superiority of the proposed method over some classical methods.","PeriodicalId":236156,"journal":{"name":"2013 International Conference on Wavelet Analysis and Pattern Recognition","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133891024","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 : 2013-07-14DOI: 10.1109/ICWAPR.2013.6599296
N. Ikawa, A. Morimoto, R. Ashino
The auditory steady-state response (ASSR) is one of auditory evoked brain responses applied to objective audiometry test. ASSR evoked by an amplitude-modulated tone is recorded as a waveform with the same frequency as the stimulus modulation frequency. Since the 40-Hz ASSR can be measured when subjects are awake, a rapid objective audiometry test has been desired for the 40-Hz ASSR. In the previous paper, based on wavelet analysis, we proposed a design of procedure of averaging waveform of 40-Hz ASSR for our original objective audiometry device. In this paper, we present detail examination using complex continuous wavelet analysis of characteristics of brain waves obtained by our original objective audiometry device. We also propose a Meyer type band-pass filter to extract the waveform data of around 40 Hz. The effectiveness of band pass filter is shown by an experiment.
{"title":"An application of wavelet analysis to procedure of averaging waveform of 40-Hz auditory steady-state response","authors":"N. Ikawa, A. Morimoto, R. Ashino","doi":"10.1109/ICWAPR.2013.6599296","DOIUrl":"https://doi.org/10.1109/ICWAPR.2013.6599296","url":null,"abstract":"The auditory steady-state response (ASSR) is one of auditory evoked brain responses applied to objective audiometry test. ASSR evoked by an amplitude-modulated tone is recorded as a waveform with the same frequency as the stimulus modulation frequency. Since the 40-Hz ASSR can be measured when subjects are awake, a rapid objective audiometry test has been desired for the 40-Hz ASSR. In the previous paper, based on wavelet analysis, we proposed a design of procedure of averaging waveform of 40-Hz ASSR for our original objective audiometry device. In this paper, we present detail examination using complex continuous wavelet analysis of characteristics of brain waves obtained by our original objective audiometry device. We also propose a Meyer type band-pass filter to extract the waveform data of around 40 Hz. The effectiveness of band pass filter is shown by an experiment.","PeriodicalId":236156,"journal":{"name":"2013 International Conference on Wavelet Analysis and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126120894","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 : 2013-07-14DOI: 10.1109/ICWAPR.2013.6599317
Ying Xu, Fei Luo, Yikui Zhai, Junying Gan
Fusion biometric recognition modal contributes in two aspects. It can not only improve the biometric recognition accuracy, but also gives a comparatively safe strategy, since it is difficult for intruders to achieve multi-biometric information simultaneously, especially the iris information. In this paper, a novel biometric fusion recognition modal with iris and facial images based on biomimetic pattern recognition is proposed. The Contourlet transform (CT) and two directional two dimensional principal component analysis (2D)2PCA are used here to extract the iris feature and the facial feature respectively, and a new fusion feature vector was formed on the combination of the previous iris and facial features. Lastly, the fusion feature vector is used to construct the covering of high dimensional space using biomimetic pattern recognition method, in which the hyper-sausage neuron is adopted. Furthermore, a fixed random matrix is used here to reduce the computational complexity and improve the recognition efficiency. Experiments on the public union database show that the proposed modal can achieve the state-of-the-art recognition accuracy while keeping the enrollment process safe.
{"title":"Joint iris and facial recognition based on feature fusion and biomimetic pattern recognition","authors":"Ying Xu, Fei Luo, Yikui Zhai, Junying Gan","doi":"10.1109/ICWAPR.2013.6599317","DOIUrl":"https://doi.org/10.1109/ICWAPR.2013.6599317","url":null,"abstract":"Fusion biometric recognition modal contributes in two aspects. It can not only improve the biometric recognition accuracy, but also gives a comparatively safe strategy, since it is difficult for intruders to achieve multi-biometric information simultaneously, especially the iris information. In this paper, a novel biometric fusion recognition modal with iris and facial images based on biomimetic pattern recognition is proposed. The Contourlet transform (CT) and two directional two dimensional principal component analysis (2D)2PCA are used here to extract the iris feature and the facial feature respectively, and a new fusion feature vector was formed on the combination of the previous iris and facial features. Lastly, the fusion feature vector is used to construct the covering of high dimensional space using biomimetic pattern recognition method, in which the hyper-sausage neuron is adopted. Furthermore, a fixed random matrix is used here to reduce the computational complexity and improve the recognition efficiency. Experiments on the public union database show that the proposed modal can achieve the state-of-the-art recognition accuracy while keeping the enrollment process safe.","PeriodicalId":236156,"journal":{"name":"2013 International Conference on Wavelet Analysis and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126034227","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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