Pub Date : 2010-10-01DOI: 10.1109/IRANIANMVIP.2010.5941154
S. M. Entezarmahdi, M. Yazdi
In this paper two transform based super resolution methods are presented for enhancing the resolution of a stationary image. In the first method, neural network is trained by wavelet transform coefficients of lower resolution of a given image, and then this neural network are used to estimate wavelet details subbands of that given image. In this way, by using these estimated subbands as wavelet details and the given image as the approximation image, a super-resolution image is made using the inverse wavelet transform. In the second proposed method, the wavelet transform is replaced by contourlet transform and the same mentioned procedure is applied. These two methods have been compared with each other and with the bicubic method on different types of images. The experimental results demonstrate the superiority performance of the proposed methods compared with regular stationary image resolution enhancing methods.
{"title":"Stationary image resolution enhancement on the basis of contourlet and wavelet transforms by means of the artificial neural network","authors":"S. M. Entezarmahdi, M. Yazdi","doi":"10.1109/IRANIANMVIP.2010.5941154","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941154","url":null,"abstract":"In this paper two transform based super resolution methods are presented for enhancing the resolution of a stationary image. In the first method, neural network is trained by wavelet transform coefficients of lower resolution of a given image, and then this neural network are used to estimate wavelet details subbands of that given image. In this way, by using these estimated subbands as wavelet details and the given image as the approximation image, a super-resolution image is made using the inverse wavelet transform. In the second proposed method, the wavelet transform is replaced by contourlet transform and the same mentioned procedure is applied. These two methods have been compared with each other and with the bicubic method on different types of images. The experimental results demonstrate the superiority performance of the proposed methods compared with regular stationary image resolution enhancing methods.","PeriodicalId":350778,"journal":{"name":"2010 6th Iranian Conference on Machine Vision and Image Processing","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125039317","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 : 2010-10-01DOI: 10.1109/IRANIANMVIP.2010.5941168
M. Owjimehr, M. Yazdi, A. Z. Asli
Transmitting the face image data through wireless fading channels have been widely used for face recognition and automatic surveillance applications and many techniques can be used to do that. However, due to the noise and wireless fading channels, the perfect recovery cannot be achieved. So there are needs to use efficient techniques for image recovery and denoising. The wavelet and contourlet transforms along with some denoising schemes such as Hard thresholding to estimate the true coefficients from noisy ones have been already used. In this paper, we propose to use Wavelet-Based Contourlet Transform (WBCT) comprised with Block thresholding to more efficiently denoise and recovery transmitted face images. The simulation results show that for general face images the WBCT is quite competitive to the contourlet and wavelet transforms in the SNR sense and in visual aspects.
{"title":"A new scheme of face image encoding through wireless fading channels using WBCT and Block thresholding","authors":"M. Owjimehr, M. Yazdi, A. Z. Asli","doi":"10.1109/IRANIANMVIP.2010.5941168","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941168","url":null,"abstract":"Transmitting the face image data through wireless fading channels have been widely used for face recognition and automatic surveillance applications and many techniques can be used to do that. However, due to the noise and wireless fading channels, the perfect recovery cannot be achieved. So there are needs to use efficient techniques for image recovery and denoising. The wavelet and contourlet transforms along with some denoising schemes such as Hard thresholding to estimate the true coefficients from noisy ones have been already used. In this paper, we propose to use Wavelet-Based Contourlet Transform (WBCT) comprised with Block thresholding to more efficiently denoise and recovery transmitted face images. The simulation results show that for general face images the WBCT is quite competitive to the contourlet and wavelet transforms in the SNR sense and in visual aspects.","PeriodicalId":350778,"journal":{"name":"2010 6th Iranian Conference on Machine Vision and Image Processing","volume":"201 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122460167","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 : 2010-10-01DOI: 10.1109/IRANIANMVIP.2010.5941135
A. Chalechale, A. Faramarzi
There are several studies in the literature comparing different approaches. Most of these comparisons are based on objective tests (i.e.; efficiency and effectiveness of the approaches are obtained and compared). In this paper we conducted a novel subjective test, where human perception is incorporated to the evaluation process. Five known methods in the image retrieval literature are implemented and compared for closeness to human perception and also for their search time. Here, 1) similarity, 2) symmetry, and 3) area of trademarks retrieved by five different methods are evaluated and scored by humans. Experimental results illustrate that the correlation method is the nearest to human's perception in all fields. Experiments also show that the EPNH method is more efficient (much more shorter time) than the correlation method, while the semantic powers of these two are close together.
{"title":"Incorporating efficiency and human judgment in image retrieval for trademark matching","authors":"A. Chalechale, A. Faramarzi","doi":"10.1109/IRANIANMVIP.2010.5941135","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941135","url":null,"abstract":"There are several studies in the literature comparing different approaches. Most of these comparisons are based on objective tests (i.e.; efficiency and effectiveness of the approaches are obtained and compared). In this paper we conducted a novel subjective test, where human perception is incorporated to the evaluation process. Five known methods in the image retrieval literature are implemented and compared for closeness to human perception and also for their search time. Here, 1) similarity, 2) symmetry, and 3) area of trademarks retrieved by five different methods are evaluated and scored by humans. Experimental results illustrate that the correlation method is the nearest to human's perception in all fields. Experiments also show that the EPNH method is more efficient (much more shorter time) than the correlation method, while the semantic powers of these two are close together.","PeriodicalId":350778,"journal":{"name":"2010 6th Iranian Conference on Machine Vision and Image Processing","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128733855","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 : 2010-10-01DOI: 10.1109/IRANIANMVIP.2010.5941152
T. Sedghi, Majid Fakheri, M. Shayesteh
The focus of this paper is to enhance retrieval performance and also to provide a better similarity distance computation. We develop a modified clustering algorithm for image retrieval where hierarchical algorithm is used to generate the initial number of clusters and the cluster centres. Experimental results show that the proposed method yields higher retrieval accuracy compared to the several conventional methods. Our work offers improvement in image segmentation and retrieval accuracy.
{"title":"Region and content based image retrieval using advanced image processing techniques","authors":"T. Sedghi, Majid Fakheri, M. Shayesteh","doi":"10.1109/IRANIANMVIP.2010.5941152","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941152","url":null,"abstract":"The focus of this paper is to enhance retrieval performance and also to provide a better similarity distance computation. We develop a modified clustering algorithm for image retrieval where hierarchical algorithm is used to generate the initial number of clusters and the cluster centres. Experimental results show that the proposed method yields higher retrieval accuracy compared to the several conventional methods. Our work offers improvement in image segmentation and retrieval accuracy.","PeriodicalId":350778,"journal":{"name":"2010 6th Iranian Conference on Machine Vision and Image Processing","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130331592","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 : 2010-10-01DOI: 10.1109/IRANIANMVIP.2010.5941133
M. Nikzad, H. Moghaddam
This paper introduces a new incremental evolutionary optimization method based on evolutionary group algorithm (EGA). The EGA was presented as an approach to overcome time-consuming drawbacks related to general evolutionary algorithms in large scale content-based image indexing retrieval (CBIR) optimization tasks. Here, we consider another challengeable limitation of usual evolutionary learning and optimization systems: learning in the scale-varying and dynamic environments. Hence, we present a new strategy based on EGA that is enhanced with the ability of incremental learning. Evaluation results on scale-varying and simulated dynamic CBIR systems show that the proposed method can continuously obtain good performance in the presence of environmental or scale changes.
{"title":"An incremental evolutionary method for optimizing dynamic image retrieval systems","authors":"M. Nikzad, H. Moghaddam","doi":"10.1109/IRANIANMVIP.2010.5941133","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941133","url":null,"abstract":"This paper introduces a new incremental evolutionary optimization method based on evolutionary group algorithm (EGA). The EGA was presented as an approach to overcome time-consuming drawbacks related to general evolutionary algorithms in large scale content-based image indexing retrieval (CBIR) optimization tasks. Here, we consider another challengeable limitation of usual evolutionary learning and optimization systems: learning in the scale-varying and dynamic environments. Hence, we present a new strategy based on EGA that is enhanced with the ability of incremental learning. Evaluation results on scale-varying and simulated dynamic CBIR systems show that the proposed method can continuously obtain good performance in the presence of environmental or scale changes.","PeriodicalId":350778,"journal":{"name":"2010 6th Iranian Conference on Machine Vision and Image Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133720738","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 : 2010-10-01DOI: 10.1109/IRANIANMVIP.2010.5941149
M. Hosseini, H. Soltanian-Zadeh, S. Akhlaghpoor, A. Behrad
Air trapping is an abnormal retention of air that occurs after expiration state in the lungs, observed in all types of bronchiolar and obstructive lung diseases such as chronic obstructive pulmonary disease (COPD), asthma, and bronchiolitis obliterans syndrome. Air trapping is often incidentally diagnosed on computed tomography (CT) scanning but this method needs doctors so it is subjective and depends on their experience. In this paper, we present a novel method for evaluation of air trapping in the lungs for detection of COPD in CT images. The proposed method finds volumetric variations of the lungs from inspiration to expiration. To this end, trachea CT images at full inspiration and expiration are compared and the volumetric variations are used to classify the subjects. In the evaluated cases, the proposed method is able to estimate air trapping in the lungs from CT images without human intervention. This method may assist radiologists to measure and evaluate air trapping for detection of COPD as a computer aided diagnosis (CAD) system.
{"title":"A new scheme for evaluation of air-trapping in CT images","authors":"M. Hosseini, H. Soltanian-Zadeh, S. Akhlaghpoor, A. Behrad","doi":"10.1109/IRANIANMVIP.2010.5941149","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941149","url":null,"abstract":"Air trapping is an abnormal retention of air that occurs after expiration state in the lungs, observed in all types of bronchiolar and obstructive lung diseases such as chronic obstructive pulmonary disease (COPD), asthma, and bronchiolitis obliterans syndrome. Air trapping is often incidentally diagnosed on computed tomography (CT) scanning but this method needs doctors so it is subjective and depends on their experience. In this paper, we present a novel method for evaluation of air trapping in the lungs for detection of COPD in CT images. The proposed method finds volumetric variations of the lungs from inspiration to expiration. To this end, trachea CT images at full inspiration and expiration are compared and the volumetric variations are used to classify the subjects. In the evaluated cases, the proposed method is able to estimate air trapping in the lungs from CT images without human intervention. This method may assist radiologists to measure and evaluate air trapping for detection of COPD as a computer aided diagnosis (CAD) system.","PeriodicalId":350778,"journal":{"name":"2010 6th Iranian Conference on Machine Vision and Image Processing","volume":"224 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129649935","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 : 2010-10-01DOI: 10.1109/IRANIANMVIP.2010.5941164
M. Baghelani, Jafar Karami Eshkaftaki, A. Ebrahimi
Human fingerprints contain ridges and valleys which to gather forms distinctive patterns. These details are called minutiae, which permanent throughout whole lifetime, and then can be used as identification marks for fingerprint verification. Fingerprint image may have a poor quality that couldn't use directly for recognition processes and then must be pre-processed first. This paper proposes a novel mapping which can be used instead of traditional pre-processing algorithms. The proposed mapping changes the overall fingerprint image configuration and mappes it to another image which is more convenience for common recognition steps. This algorithm is tested over FVC2002 fingerprint database and the results were satisfying.
{"title":"A novel mapping for fingerprint image enhancement","authors":"M. Baghelani, Jafar Karami Eshkaftaki, A. Ebrahimi","doi":"10.1109/IRANIANMVIP.2010.5941164","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941164","url":null,"abstract":"Human fingerprints contain ridges and valleys which to gather forms distinctive patterns. These details are called minutiae, which permanent throughout whole lifetime, and then can be used as identification marks for fingerprint verification. Fingerprint image may have a poor quality that couldn't use directly for recognition processes and then must be pre-processed first. This paper proposes a novel mapping which can be used instead of traditional pre-processing algorithms. The proposed mapping changes the overall fingerprint image configuration and mappes it to another image which is more convenience for common recognition steps. This algorithm is tested over FVC2002 fingerprint database and the results were satisfying.","PeriodicalId":350778,"journal":{"name":"2010 6th Iranian Conference on Machine Vision and Image Processing","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130108495","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 : 2010-10-01DOI: 10.1109/IRANIANMVIP.2010.5941153
M. Saeidi, R. Amirfattahi, A. Amini, M. Sajadi
The objective of signature verification is to distinguish forgery signature from genuine one. Online signature is the one which is registered through electronic devices such as digitizers and stored on computers in time sequence form. In this kind of signatures in addition to location information, time information such as speed and acceleration is stored. In this paper after accomplishment of some pre-processing procedures like normalization of signature size, smoothing and elimination of rotation on signatures using algorithms based on extremum matching of signals and ant colony, their time duration will be equalized. Afterwards, similarities between signatures will be determined using extended regression and finally will try to distinguish between forgery signatures from genuine one using support vector machine (SVM). The suggested online verification system is tested on SVC2004 signature set which is related to the first international signature verification competition and results are compared to respective results of participants. The results state that suggested method exhibits equal error rate (EER) of %4.3 in skilled forger group.
{"title":"Online signature verification using combination of two classifiers","authors":"M. Saeidi, R. Amirfattahi, A. Amini, M. Sajadi","doi":"10.1109/IRANIANMVIP.2010.5941153","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941153","url":null,"abstract":"The objective of signature verification is to distinguish forgery signature from genuine one. Online signature is the one which is registered through electronic devices such as digitizers and stored on computers in time sequence form. In this kind of signatures in addition to location information, time information such as speed and acceleration is stored. In this paper after accomplishment of some pre-processing procedures like normalization of signature size, smoothing and elimination of rotation on signatures using algorithms based on extremum matching of signals and ant colony, their time duration will be equalized. Afterwards, similarities between signatures will be determined using extended regression and finally will try to distinguish between forgery signatures from genuine one using support vector machine (SVM). The suggested online verification system is tested on SVC2004 signature set which is related to the first international signature verification competition and results are compared to respective results of participants. The results state that suggested method exhibits equal error rate (EER) of %4.3 in skilled forger group.","PeriodicalId":350778,"journal":{"name":"2010 6th Iranian Conference on Machine Vision and Image Processing","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128952327","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 : 2010-10-01DOI: 10.1109/IRANIANMVIP.2010.5941145
Mohieddin Moradi, S. Mozaffari, A. Orouji
Video text information plays an important role in semantic-based video analysis, indexing and retrieval. In this paper, we proposed a novel Farsi text detection approach based on intrinsic characteristics of Farsi text lines, which is more robust to complex backgrounds and various font styles. First, by an edge detector operator, all the possible edges in vertical, horizontal, 45 and 135 degrees are extracted. Then, for extracting text strokes, some pre-processing such as dilation and erosion are done according to the font size. Afterward, by finding the edges cross points, corners map is extracted. To discard non-text corners and finding real font size, histogram analysis is done. After finding real font size, input image is rescaled and a new corner map is extracted. Finally, the detected candidate text areas undergo the empirical rules analysis to identify text areas and project profile analysis for verification and text lines extraction. Experimental results demonstrate that the proposed method is robust to font size, font colour, and background complexity.
{"title":"Farsi/Arabic text extraction from video images by corner detection","authors":"Mohieddin Moradi, S. Mozaffari, A. Orouji","doi":"10.1109/IRANIANMVIP.2010.5941145","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941145","url":null,"abstract":"Video text information plays an important role in semantic-based video analysis, indexing and retrieval. In this paper, we proposed a novel Farsi text detection approach based on intrinsic characteristics of Farsi text lines, which is more robust to complex backgrounds and various font styles. First, by an edge detector operator, all the possible edges in vertical, horizontal, 45 and 135 degrees are extracted. Then, for extracting text strokes, some pre-processing such as dilation and erosion are done according to the font size. Afterward, by finding the edges cross points, corners map is extracted. To discard non-text corners and finding real font size, histogram analysis is done. After finding real font size, input image is rescaled and a new corner map is extracted. Finally, the detected candidate text areas undergo the empirical rules analysis to identify text areas and project profile analysis for verification and text lines extraction. Experimental results demonstrate that the proposed method is robust to font size, font colour, and background complexity.","PeriodicalId":350778,"journal":{"name":"2010 6th Iranian Conference on Machine Vision and Image Processing","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129206341","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 : 2010-10-01DOI: 10.1109/IRANIANMVIP.2010.5941136
Mahdi Yazdian Dehkordi, M. Nikzad, Vahid Reza Ekhlas, Z. Azimifar
License Plate Detection (LPD) is the most difficult, critical and time consuming task in license plate recognition (LPR) systems. In this paper, a novel texture-based method is proposed for fast and robust LPD. First, a new filter called Peak-Valley filter is applied on the lines of the image. This filter not only extracts the remarkable gray level changes as consecutive peaks and valleys, but also simultaneously removes the undesirable small variations. Secondly, a sequential Peak-Valley partitioning is utilized to segment the transitions into some groups. Afterward, a neural network is employed to find true candidate lines and finally the candidate lines are aggregated to form the plates regions. According to our experiments, the proposed method correctly detects all plates presented in the image regardless of their styles and without considering the whole image. Experimental results showed that this approach can apply on real-time application for outdoor complex scenes.
{"title":"A novel approach for fast and robust multiple license plate detection","authors":"Mahdi Yazdian Dehkordi, M. Nikzad, Vahid Reza Ekhlas, Z. Azimifar","doi":"10.1109/IRANIANMVIP.2010.5941136","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941136","url":null,"abstract":"License Plate Detection (LPD) is the most difficult, critical and time consuming task in license plate recognition (LPR) systems. In this paper, a novel texture-based method is proposed for fast and robust LPD. First, a new filter called Peak-Valley filter is applied on the lines of the image. This filter not only extracts the remarkable gray level changes as consecutive peaks and valleys, but also simultaneously removes the undesirable small variations. Secondly, a sequential Peak-Valley partitioning is utilized to segment the transitions into some groups. Afterward, a neural network is employed to find true candidate lines and finally the candidate lines are aggregated to form the plates regions. According to our experiments, the proposed method correctly detects all plates presented in the image regardless of their styles and without considering the whole image. Experimental results showed that this approach can apply on real-time application for outdoor complex scenes.","PeriodicalId":350778,"journal":{"name":"2010 6th Iranian Conference on Machine Vision and Image Processing","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126716014","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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