Pub Date : 2013-09-01DOI: 10.1109/IRANIANMVIP.2013.6780027
Nakisa Abounasr, H. Pourghassem
This paper presents two new approaches for facial expression recognition based on digital curvelet transform and local binary patterns from three orthogonal planes (LBP-TOP) for both still image and image sequences. The features are extracted by using the digital curvelet transform on facial regions in still image. In this approach, some sub-bands correspond to angle of facial region is used. These sub-bands consist of more frequency information. The digital curvelet coefficients and LBP-TOP are represented to combine spatio-temporal and spectral features for image sequences. The obtained results by our proposed approaches on the Cohn-Kanade facial expression database have acceptable recognition rates of 91.90% and 88.38% for still image and image sequences, respectively.
{"title":"Facial expression recognition based on combination of spatio-temporal and spectral features in local facial regions","authors":"Nakisa Abounasr, H. Pourghassem","doi":"10.1109/IRANIANMVIP.2013.6780027","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2013.6780027","url":null,"abstract":"This paper presents two new approaches for facial expression recognition based on digital curvelet transform and local binary patterns from three orthogonal planes (LBP-TOP) for both still image and image sequences. The features are extracted by using the digital curvelet transform on facial regions in still image. In this approach, some sub-bands correspond to angle of facial region is used. These sub-bands consist of more frequency information. The digital curvelet coefficients and LBP-TOP are represented to combine spatio-temporal and spectral features for image sequences. The obtained results by our proposed approaches on the Cohn-Kanade facial expression database have acceptable recognition rates of 91.90% and 88.38% for still image and image sequences, respectively.","PeriodicalId":297204,"journal":{"name":"2013 8th Iranian Conference on Machine Vision and Image Processing (MVIP)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121051282","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 : 2013-09-01DOI: 10.1109/IRANIANMVIP.2013.6779957
M. H. Karimi, D. Asemani
One of the most important applications of machine vision in various industries is automated inspection. Performance of automated inspection depends directly on the algorithm used for threshold selection. Common methods of automatic thresholding are based on image histogram. In previous methods, the threshold selection has been realized by dividing the histogram into two classes. Also, possibility of misdiagnosis is high especially for the textures without defect. This paper proposes a new statistical algorithm for automatic theresholding which can be optimally applied in the presence of different types of surface defects. The optimum threshold is obtained in the proposed algorithm so that a maximum between-class and minimum within-class variances are provided. Proposed methods demonstrate a better performance compared to classic histogram-based algorithm particularly for the textures without any considerable defects.
{"title":"A novel histogram thresholding method for surface defect detection","authors":"M. H. Karimi, D. Asemani","doi":"10.1109/IRANIANMVIP.2013.6779957","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2013.6779957","url":null,"abstract":"One of the most important applications of machine vision in various industries is automated inspection. Performance of automated inspection depends directly on the algorithm used for threshold selection. Common methods of automatic thresholding are based on image histogram. In previous methods, the threshold selection has been realized by dividing the histogram into two classes. Also, possibility of misdiagnosis is high especially for the textures without defect. This paper proposes a new statistical algorithm for automatic theresholding which can be optimally applied in the presence of different types of surface defects. The optimum threshold is obtained in the proposed algorithm so that a maximum between-class and minimum within-class variances are provided. Proposed methods demonstrate a better performance compared to classic histogram-based algorithm particularly for the textures without any considerable defects.","PeriodicalId":297204,"journal":{"name":"2013 8th Iranian Conference on Machine Vision and Image Processing (MVIP)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122637880","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 : 2013-09-01DOI: 10.1109/IRANIANMVIP.2013.6779948
Hadi Amirpour, A. Mousavinia, Nakisa Shamsi
Motion estimation is a vital task in video compression and many algorithms are proposed to reduce its computational complexity. In a conventional Full Search (FS) algorithm, all blocks are searched for a match in the search window, resulting in a very acceptable PSNR compared to the other methods. However it suffers from heavy computational overhead. Three Step Search (TSS) algorithm which limits the search space adaptively, is used in many applications for its simplicity and effectiveness. The PTSS algorithm proposed in this paper decreases the number of search blocks even more, using motion information obtained from its neighboring blocks. Experimental and simulation results show approximately a 20% speed enhancement with the same or slightly improved PSNR in comparison to TSS.
{"title":"Predictive Three Step Search (PTSS) algorithm for motion estimation","authors":"Hadi Amirpour, A. Mousavinia, Nakisa Shamsi","doi":"10.1109/IRANIANMVIP.2013.6779948","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2013.6779948","url":null,"abstract":"Motion estimation is a vital task in video compression and many algorithms are proposed to reduce its computational complexity. In a conventional Full Search (FS) algorithm, all blocks are searched for a match in the search window, resulting in a very acceptable PSNR compared to the other methods. However it suffers from heavy computational overhead. Three Step Search (TSS) algorithm which limits the search space adaptively, is used in many applications for its simplicity and effectiveness. The PTSS algorithm proposed in this paper decreases the number of search blocks even more, using motion information obtained from its neighboring blocks. Experimental and simulation results show approximately a 20% speed enhancement with the same or slightly improved PSNR in comparison to TSS.","PeriodicalId":297204,"journal":{"name":"2013 8th Iranian Conference on Machine Vision and Image Processing (MVIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115193380","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 : 2013-09-01DOI: 10.1109/IRANIANMVIP.2013.6780029
F. Yaghmaee, A. A. Gharahbagh
Display devices based on its different screens and resolutions, need image resizing to retain the image's quality. Common image resizing methods cannot save or protect important objects or its results are non-photorealistic. Seam carving as a new method has been widely used for content-aware image and video resizing with little distortion in comparison with common methods. Unfortunately, seam carving is a complex algorithm and for high resolution videos or images has a long run time and is not usable in real-time applications. In this paper, a novel fast method in order to accelerate simple seam carving and decrease computational burden is presented. In this method image is divided into three equal horizontal or vertical sections, while the traditional seam carving is applied to the middle section. In the top and down sections, the algorithm estimates seam with respect to the middle part seam using an approximated Dijkstra method. Experiments have demonstrated better computational efficiency of presented method when it faces the current seam carving method. It is also preserving the image's information as effectively as the original seam carving method.
{"title":"A fast seam carving method based on merging seams in subimages","authors":"F. Yaghmaee, A. A. Gharahbagh","doi":"10.1109/IRANIANMVIP.2013.6780029","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2013.6780029","url":null,"abstract":"Display devices based on its different screens and resolutions, need image resizing to retain the image's quality. Common image resizing methods cannot save or protect important objects or its results are non-photorealistic. Seam carving as a new method has been widely used for content-aware image and video resizing with little distortion in comparison with common methods. Unfortunately, seam carving is a complex algorithm and for high resolution videos or images has a long run time and is not usable in real-time applications. In this paper, a novel fast method in order to accelerate simple seam carving and decrease computational burden is presented. In this method image is divided into three equal horizontal or vertical sections, while the traditional seam carving is applied to the middle section. In the top and down sections, the algorithm estimates seam with respect to the middle part seam using an approximated Dijkstra method. Experiments have demonstrated better computational efficiency of presented method when it faces the current seam carving method. It is also preserving the image's information as effectively as the original seam carving method.","PeriodicalId":297204,"journal":{"name":"2013 8th Iranian Conference on Machine Vision and Image Processing (MVIP)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130955659","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 : 2013-09-01DOI: 10.1109/IRANIANMVIP.2013.6779972
V. Soleimani, F. H. Vincheh
In this paper, we present an interactive algorithm to separate foreground and background regions of natural images (natural image matting) using ant colony optimization. Today, image matting is one of the most challenging and interesting research fields in image processing. In our approach instead of preparing a trimap, the user specifies foreground and background regions by some red and blue scribbles. Then by minimizing local energy function of all pixels alpha matte is estimated. Our approach not only needs a little interaction with the user but also by applying ant colony algorithm on color images, finds homogenous regions of the image and yields good results compared with other methods. In other words, the local energy of a pixel is obtained using traveled path by the pixel ant and since the ant tends to move to pixels similar to beginning pixel, homogenous regions of the image are detected. Moreover, we use some techniques like vectorization in the implementation of our algorithm in order to decrease time complexity. Experimental results show our algorithm advantages.
{"title":"Ant colony alpha matte: A new approach for natural image matting","authors":"V. Soleimani, F. H. Vincheh","doi":"10.1109/IRANIANMVIP.2013.6779972","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2013.6779972","url":null,"abstract":"In this paper, we present an interactive algorithm to separate foreground and background regions of natural images (natural image matting) using ant colony optimization. Today, image matting is one of the most challenging and interesting research fields in image processing. In our approach instead of preparing a trimap, the user specifies foreground and background regions by some red and blue scribbles. Then by minimizing local energy function of all pixels alpha matte is estimated. Our approach not only needs a little interaction with the user but also by applying ant colony algorithm on color images, finds homogenous regions of the image and yields good results compared with other methods. In other words, the local energy of a pixel is obtained using traveled path by the pixel ant and since the ant tends to move to pixels similar to beginning pixel, homogenous regions of the image are detected. Moreover, we use some techniques like vectorization in the implementation of our algorithm in order to decrease time complexity. Experimental results show our algorithm advantages.","PeriodicalId":297204,"journal":{"name":"2013 8th Iranian Conference on Machine Vision and Image Processing (MVIP)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131696747","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 : 2013-09-01DOI: 10.1109/IRANIANMVIP.2013.6780024
H. Heidari, A. Chalechale, A. Mohammadabadi
Image retrieval tools can assist people in making efficient use of digital image collections; also it has become imperative to find efficient methods for the retrieval of these images. Most image processing algorithms are inherently parallel, so multithreading processors are suitable in such applications. In very big image databases, image processing takes very long time for run on a single core processor because of single thread execution of algorithms. GPU is more common in most image processing applications due to multithread execution of algorithms, programmability and low cost. In this paper we implement color moments and texture based image retrieval (entropy, standard deviation and local range) in parallel using CUDA programming model to run on GPUs. These features are applied to search images from a database which are similar to a query image. We evaluated our retrieval system using recall, precision, and average precision measures. Experimental results showed that parallel implementation led to an average speed up of 144.67×over the serial implementation when running on a NVIDIA GPU GeForce GT610M. Also the average precision and the average recall of proposed method are 61.968% and 55% respectively.
{"title":"Accelerating of color moments and texture features extraction using GPU based parallel computing","authors":"H. Heidari, A. Chalechale, A. Mohammadabadi","doi":"10.1109/IRANIANMVIP.2013.6780024","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2013.6780024","url":null,"abstract":"Image retrieval tools can assist people in making efficient use of digital image collections; also it has become imperative to find efficient methods for the retrieval of these images. Most image processing algorithms are inherently parallel, so multithreading processors are suitable in such applications. In very big image databases, image processing takes very long time for run on a single core processor because of single thread execution of algorithms. GPU is more common in most image processing applications due to multithread execution of algorithms, programmability and low cost. In this paper we implement color moments and texture based image retrieval (entropy, standard deviation and local range) in parallel using CUDA programming model to run on GPUs. These features are applied to search images from a database which are similar to a query image. We evaluated our retrieval system using recall, precision, and average precision measures. Experimental results showed that parallel implementation led to an average speed up of 144.67×over the serial implementation when running on a NVIDIA GPU GeForce GT610M. Also the average precision and the average recall of proposed method are 61.968% and 55% respectively.","PeriodicalId":297204,"journal":{"name":"2013 8th Iranian Conference on Machine Vision and Image Processing (MVIP)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116951372","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 : 2013-09-01DOI: 10.1109/IRANIANMVIP.2013.6779979
F. Tehranipour, R. Shishegar, Soheil Tehranipour, S. Setarehdan
One important issue in machine vision is using automatic attention control methods for monitoring CCTV cameras, in order to enhance the security of people in public. Result of automatic methods such as crowd density estimation can alert the operator in the case of risk probability increasing. In addition to overall crowd density, other parameters such as regional crowd density and the temporal and spatial criteria of each frame of video should be considered to control the operator's attention correctly. For this purpose, according to the gradual change of crowd density and risk probability in daily hours and uncertainty in our knowledge in evaluation of crowded places, we designed a fuzzy decision making system to make decisions about risk probability. The design of this system is based on the fact that the human visual system tends to direct attention to events that happen with low probability. The efficiency of this system is tested on real data and results are presented to demonstrate the practical applications of this system to aid the human operator.
{"title":"Attention control using fuzzy inference system in monitoring CCTV based on crowd density estimation","authors":"F. Tehranipour, R. Shishegar, Soheil Tehranipour, S. Setarehdan","doi":"10.1109/IRANIANMVIP.2013.6779979","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2013.6779979","url":null,"abstract":"One important issue in machine vision is using automatic attention control methods for monitoring CCTV cameras, in order to enhance the security of people in public. Result of automatic methods such as crowd density estimation can alert the operator in the case of risk probability increasing. In addition to overall crowd density, other parameters such as regional crowd density and the temporal and spatial criteria of each frame of video should be considered to control the operator's attention correctly. For this purpose, according to the gradual change of crowd density and risk probability in daily hours and uncertainty in our knowledge in evaluation of crowded places, we designed a fuzzy decision making system to make decisions about risk probability. The design of this system is based on the fact that the human visual system tends to direct attention to events that happen with low probability. The efficiency of this system is tested on real data and results are presented to demonstrate the practical applications of this system to aid the human operator.","PeriodicalId":297204,"journal":{"name":"2013 8th Iranian Conference on Machine Vision and Image Processing (MVIP)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124964908","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 : 2013-09-01DOI: 10.1109/IRANIANMVIP.2013.6780013
Mohammad Esmaeilpour, Azadeh Mansouri, Ahmad Mahmoudi-Aznaveh
In recent years, many efforts have been performed in order to design an algorithm assessing perceptual image quality based on human visual system. Although some impressive metrics have been presented, full reference image quality assessment (IQA) is still a challenging issue. In this paper, we present a new SVD-based IQA method in which the structural similarity between the reference and distorted image is utilized as a key factor for measuring the imposed distortions. The experimental results show that the proposed algorithm can effectively evaluated the image quality in a consistent manner with human visual perception.
{"title":"A new SVD-based image quality assessment","authors":"Mohammad Esmaeilpour, Azadeh Mansouri, Ahmad Mahmoudi-Aznaveh","doi":"10.1109/IRANIANMVIP.2013.6780013","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2013.6780013","url":null,"abstract":"In recent years, many efforts have been performed in order to design an algorithm assessing perceptual image quality based on human visual system. Although some impressive metrics have been presented, full reference image quality assessment (IQA) is still a challenging issue. In this paper, we present a new SVD-based IQA method in which the structural similarity between the reference and distorted image is utilized as a key factor for measuring the imposed distortions. The experimental results show that the proposed algorithm can effectively evaluated the image quality in a consistent manner with human visual perception.","PeriodicalId":297204,"journal":{"name":"2013 8th Iranian Conference on Machine Vision and Image Processing (MVIP)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125267565","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 : 2013-09-01DOI: 10.1109/IRANIANMVIP.2013.6779981
Hadi Yarmohammadi, M. Rahmati, Shahram Khadivi
During the last decades a large set of video archives is created and rapidly multimedia growth creates new challenge in the image processing world. A reliable system is needed to automate the process of this large amount of data. Video analyses are done in two different levels, low level and high level. There are many problems in video content analysis and in this work we analyzed content based video analysis. Our proposed method is based on Information theory. These systems consist of three main parts which includes: Shot Boundary Detection, Hierarchical video summarization, retrieve and index target video. System performance is evaluated on TRECVID2006 Database, results shown the usefulness of the proposed method.
{"title":"Content based video retrieval using information theory","authors":"Hadi Yarmohammadi, M. Rahmati, Shahram Khadivi","doi":"10.1109/IRANIANMVIP.2013.6779981","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2013.6779981","url":null,"abstract":"During the last decades a large set of video archives is created and rapidly multimedia growth creates new challenge in the image processing world. A reliable system is needed to automate the process of this large amount of data. Video analyses are done in two different levels, low level and high level. There are many problems in video content analysis and in this work we analyzed content based video analysis. Our proposed method is based on Information theory. These systems consist of three main parts which includes: Shot Boundary Detection, Hierarchical video summarization, retrieve and index target video. System performance is evaluated on TRECVID2006 Database, results shown the usefulness of the proposed method.","PeriodicalId":297204,"journal":{"name":"2013 8th Iranian Conference on Machine Vision and Image Processing (MVIP)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128467718","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 : 2013-09-01DOI: 10.1109/IRANIANMVIP.2013.6780011
Leila Malihi, K. Ansari-Asl, A. Behbahani
This research represents a method to detect malaria parasite in blood samples stained with giemsa. In order to increase the accuracy of detecting, at the first step, the red blood cell mask is extracted. It is due to the fact that most of malaria parasites exist in red blood cells. Then, stained elements of blood such as red blood cells, parasites and white blood cells are extracted. At the next step, red blood cell mask is located on the extracted stained elements to separate the possible parasites. Finally, color histogram, granulometry, gradient and flat texture features are extracted and used as classifier inputs. Here, five classifiers were used: support vector machines (SVM), nearest mean (NM), K nearest neighbors (KNN), 1-NN and Fisher. In this research K nearest neighbors classifier had the best accuracy, which was 91%.
{"title":"Malaria parasite detection in giemsa-stained blood cell images","authors":"Leila Malihi, K. Ansari-Asl, A. Behbahani","doi":"10.1109/IRANIANMVIP.2013.6780011","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2013.6780011","url":null,"abstract":"This research represents a method to detect malaria parasite in blood samples stained with giemsa. In order to increase the accuracy of detecting, at the first step, the red blood cell mask is extracted. It is due to the fact that most of malaria parasites exist in red blood cells. Then, stained elements of blood such as red blood cells, parasites and white blood cells are extracted. At the next step, red blood cell mask is located on the extracted stained elements to separate the possible parasites. Finally, color histogram, granulometry, gradient and flat texture features are extracted and used as classifier inputs. Here, five classifiers were used: support vector machines (SVM), nearest mean (NM), K nearest neighbors (KNN), 1-NN and Fisher. In this research K nearest neighbors classifier had the best accuracy, which was 91%.","PeriodicalId":297204,"journal":{"name":"2013 8th Iranian Conference on Machine Vision and Image Processing (MVIP)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132695118","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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