Pub Date : 2014-10-01DOI: 10.1109/ICIP.2014.7025772
G. Kordelas, D. Alexiadis, P. Daras, E. Izquierdo
In this paper the scanline optimization used for stereo matching, is revisited. In order to improve the performance of this semi-global technique, a new criterion to check depth discontinuity, is introduced. This criterion is defined according to the mean-shift-based image segmentation result. Additionally, this work proposes the employment of a pixel dissimilarity metric for the computation of the cost term, which is then provided to the guided image filter approach to estimate the initial cost volume. The algorithm is tested on the four images of the online Middlebury stereo evaluation benchmark. Moreover, it is tested on 27 additional Middlebury stereo pairs for assessing thoroughly its performance. The extended comparison verifies the efficiency of this work.
{"title":"Revisiting guided image filter based stereo matching and scanline optimization for improved disparity estimation","authors":"G. Kordelas, D. Alexiadis, P. Daras, E. Izquierdo","doi":"10.1109/ICIP.2014.7025772","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025772","url":null,"abstract":"In this paper the scanline optimization used for stereo matching, is revisited. In order to improve the performance of this semi-global technique, a new criterion to check depth discontinuity, is introduced. This criterion is defined according to the mean-shift-based image segmentation result. Additionally, this work proposes the employment of a pixel dissimilarity metric for the computation of the cost term, which is then provided to the guided image filter approach to estimate the initial cost volume. The algorithm is tested on the four images of the online Middlebury stereo evaluation benchmark. Moreover, it is tested on 27 additional Middlebury stereo pairs for assessing thoroughly its performance. The extended comparison verifies the efficiency of this work.","PeriodicalId":6856,"journal":{"name":"2014 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79416870","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 : 2014-10-01DOI: 10.1109/ICIP.2014.7025929
Jin Wang, Jian-Feng Cai, Yunhui Shi, Baocai Yin
Dictionary learning for sparse representation has been an active topic in the field of image processing. Most existing dictionary learning schemes focus on the representation ability of the learned dictionary. However, according to the theory of compressive sensing, the mutual incoherence of the dictionary is of crucial role in the sparse coding. Thus incoherent dictionary is desirable to improve the performance of sparse representation based image restoration. In this paper, we propose a new incoherent dictionary learning model that minimizes the representation error and the mutual incoherence by incorporating the constraint of mutual incoherence into the dictionary update model. The optimal incoherent dictionary is achieved by seeking an optimization solution. An efficient algorithm is developed to solve the optimization problem iteratively. Experimental results on image denoising demonstrate that the proposed scheme achieves better recovery quality and converges faster than K-SVD while keeping lower computation complexity.
{"title":"Incoherent dictionary learning for sparse representation based image denoising","authors":"Jin Wang, Jian-Feng Cai, Yunhui Shi, Baocai Yin","doi":"10.1109/ICIP.2014.7025929","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025929","url":null,"abstract":"Dictionary learning for sparse representation has been an active topic in the field of image processing. Most existing dictionary learning schemes focus on the representation ability of the learned dictionary. However, according to the theory of compressive sensing, the mutual incoherence of the dictionary is of crucial role in the sparse coding. Thus incoherent dictionary is desirable to improve the performance of sparse representation based image restoration. In this paper, we propose a new incoherent dictionary learning model that minimizes the representation error and the mutual incoherence by incorporating the constraint of mutual incoherence into the dictionary update model. The optimal incoherent dictionary is achieved by seeking an optimization solution. An efficient algorithm is developed to solve the optimization problem iteratively. Experimental results on image denoising demonstrate that the proposed scheme achieves better recovery quality and converges faster than K-SVD while keeping lower computation complexity.","PeriodicalId":6856,"journal":{"name":"2014 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79483692","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 : 2014-10-01DOI: 10.1109/ICIP.2014.7025008
Dongeek Shin, Ahmed Kirmani, Vivek K Goyal, J. Shapiro
Capturing depth and reflectivity images at low light levels from active illumination of a scene has wide-ranging applications. Conventionally, even with single-photon detectors, hundreds of photon detections are needed at each pixel to mitigate Poisson noise. We introduce a robust method for estimating depth and reflectivity using on the order of 1 detected photon per pixel averaged over the scene. Our computational imager combines physically accurate single-photon counting statistics with exploitation of the spatial correlations present in real-world reflectivity and 3D structure. Experiments conducted in the presence of strong background light demonstrate that our computational imager is able to accurately recover scene depth and reflectivity, while traditional maximum likelihood-based imaging methods lead to estimates that are highly noisy. Our framework increases photon efficiency 100-fold over traditional processing and thus will be useful for rapid, low-power, and noise-tolerant active optical imaging.
{"title":"Computational 3D and reflectivity imaging with high photon efficiency","authors":"Dongeek Shin, Ahmed Kirmani, Vivek K Goyal, J. Shapiro","doi":"10.1109/ICIP.2014.7025008","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025008","url":null,"abstract":"Capturing depth and reflectivity images at low light levels from active illumination of a scene has wide-ranging applications. Conventionally, even with single-photon detectors, hundreds of photon detections are needed at each pixel to mitigate Poisson noise. We introduce a robust method for estimating depth and reflectivity using on the order of 1 detected photon per pixel averaged over the scene. Our computational imager combines physically accurate single-photon counting statistics with exploitation of the spatial correlations present in real-world reflectivity and 3D structure. Experiments conducted in the presence of strong background light demonstrate that our computational imager is able to accurately recover scene depth and reflectivity, while traditional maximum likelihood-based imaging methods lead to estimates that are highly noisy. Our framework increases photon efficiency 100-fold over traditional processing and thus will be useful for rapid, low-power, and noise-tolerant active optical imaging.","PeriodicalId":6856,"journal":{"name":"2014 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81572726","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 : 2014-10-01DOI: 10.1109/ICIP.2014.7025980
G. Haro
We propose a 3D reconstruction algorithm based on silhouettes and color images. It is robust to inconsistent silhouettes, often common in real applications due to occlusions, errors in the background subtraction, noise or even calibration errors. The recovery of the shape that best fits the available data is formulated as a continuous energy minimization problem. The energy is based on the error between the silhouettes and the shape plus a regularization term based on a photo-consistency measure that places the surface at photo-consistent locations. The visibility is modeled as a function of the shape. The proposed photo-consistency measure takes visibility into account, although the presented variational framework can use different photo-consistency computations.
{"title":"Shape from silhouette consensus and photo-consistency","authors":"G. Haro","doi":"10.1109/ICIP.2014.7025980","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025980","url":null,"abstract":"We propose a 3D reconstruction algorithm based on silhouettes and color images. It is robust to inconsistent silhouettes, often common in real applications due to occlusions, errors in the background subtraction, noise or even calibration errors. The recovery of the shape that best fits the available data is formulated as a continuous energy minimization problem. The energy is based on the error between the silhouettes and the shape plus a regularization term based on a photo-consistency measure that places the surface at photo-consistent locations. The visibility is modeled as a function of the shape. The proposed photo-consistency measure takes visibility into account, although the presented variational framework can use different photo-consistency computations.","PeriodicalId":6856,"journal":{"name":"2014 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81589926","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 : 2014-10-01DOI: 10.1109/ICIP.2014.7025024
B. Özkalayci, Aydin Alatan
An energy based planar depth representation is proposed to obtain an efficient depth compression tool for 3DV applications. The proposed segmentation-based depth compression approach is designed by reflecting the rate-distortion tradeoff into the energy terms. A PEARL based algorithm is developed to obtain the planar approximations of depth images. Lastly depth reconstruction and novel view rendering results of the proposal compared with the state-of-the-art methods. The experiments show that the planar approach performs superior rendering results than JPEG 2000 and HEVC standards.
{"title":"MRF-based planar co-segmentation for depth compression","authors":"B. Özkalayci, Aydin Alatan","doi":"10.1109/ICIP.2014.7025024","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025024","url":null,"abstract":"An energy based planar depth representation is proposed to obtain an efficient depth compression tool for 3DV applications. The proposed segmentation-based depth compression approach is designed by reflecting the rate-distortion tradeoff into the energy terms. A PEARL based algorithm is developed to obtain the planar approximations of depth images. Lastly depth reconstruction and novel view rendering results of the proposal compared with the state-of-the-art methods. The experiments show that the planar approach performs superior rendering results than JPEG 2000 and HEVC standards.","PeriodicalId":6856,"journal":{"name":"2014 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84288293","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 : 2014-10-01DOI: 10.1109/ICIP.2014.7025712
Nan Xu, R. N. Spreng, P. Doerschuk
Resting-state functional MRI (rs fMRI) is widely used to non-invasively study human brain networks. Network functional connectivity is estimated by calculating the standard correlation between blood-oxygen-level dependent (BOLD) signals in specific regions of interests (ROIs). However, standard correlation fails to characterize the causality and the direction of information flow between regions, which are important factors in characterizing a network. Here, we use causal linear time-invariant models, with the impulse response duration estimated by Information Criteria, to describe the effective connectivity between ROIs. To do so, we replace the standard correlation between BOLD signals with a correlation between a BOLD signal and a prediction via the model of that BOLD signal. Prediction correlation is then used in a network analysis similar to that used with standard correlation. Our results include the causality information, the direction of information flow, and the possibility of delays in information flow. This approach replicates the local and distributed network architecture of the human brain previously observed with standard correlations, as well as providing novel insight into the directed interactivity of regions comprising these networks.
{"title":"Directed interactivity of large-scale brain networks: Introducing a new method for estimating resting-state effective connectivity MRI","authors":"Nan Xu, R. N. Spreng, P. Doerschuk","doi":"10.1109/ICIP.2014.7025712","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025712","url":null,"abstract":"Resting-state functional MRI (rs fMRI) is widely used to non-invasively study human brain networks. Network functional connectivity is estimated by calculating the standard correlation between blood-oxygen-level dependent (BOLD) signals in specific regions of interests (ROIs). However, standard correlation fails to characterize the causality and the direction of information flow between regions, which are important factors in characterizing a network. Here, we use causal linear time-invariant models, with the impulse response duration estimated by Information Criteria, to describe the effective connectivity between ROIs. To do so, we replace the standard correlation between BOLD signals with a correlation between a BOLD signal and a prediction via the model of that BOLD signal. Prediction correlation is then used in a network analysis similar to that used with standard correlation. Our results include the causality information, the direction of information flow, and the possibility of delays in information flow. This approach replicates the local and distributed network architecture of the human brain previously observed with standard correlations, as well as providing novel insight into the directed interactivity of regions comprising these networks.","PeriodicalId":6856,"journal":{"name":"2014 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84497694","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 : 2014-10-01DOI: 10.1109/ICIP.2014.7025713
R. Curtin, M. Ismail, A. Farag, C. Sites, S. Elshazly, R. Falk
This paper describes a unique method for tubular object visualization. The method involves rendering the exterior of the tube invisible while keeping the interior visible. This “One-sided-transparency” technique renders a more complete view of the tube's interior. When applied to virtual colonoscopy (VC), it compares favorably to existing methods. It provides more complete images, reduces computational time, and reduces memory requirements while preserving VCs benefits for patients and practitioners. The approach also has various potential uses outside of VC.
{"title":"One-sided transparency: A novel visualization for tubular objects","authors":"R. Curtin, M. Ismail, A. Farag, C. Sites, S. Elshazly, R. Falk","doi":"10.1109/ICIP.2014.7025713","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025713","url":null,"abstract":"This paper describes a unique method for tubular object visualization. The method involves rendering the exterior of the tube invisible while keeping the interior visible. This “One-sided-transparency” technique renders a more complete view of the tube's interior. When applied to virtual colonoscopy (VC), it compares favorably to existing methods. It provides more complete images, reduces computational time, and reduces memory requirements while preserving VCs benefits for patients and practitioners. The approach also has various potential uses outside of VC.","PeriodicalId":6856,"journal":{"name":"2014 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85107449","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 : 2014-10-01DOI: 10.1109/ICIP.2014.7026176
Nikhil S. Narayan, P. Marziliano, J. Kanagalingam, C. Hobbs
Contrary to the popular belief of treating speckle related pixels as noise and filtering an ultrasound image for speckle noise removal, the practical importance and use of these pixels in performing a multi-organ segmentation of the thyroid gland is studied in this research work. In this work, speckle related pixels are classified into three echogenic levels and then used to segment an ultrasound image of the thyroid gland into the trachea, carotid, muscles and thyroid. Novel techniques are introduced to estimate the anterior boundaries of the thyroid gland using low pass filtered intensity gradients of the hyperechoic speckle pixels in transverse and longitudinal ultrasound scans, respectively. An energy functional similar to active contour models is defined to segment that carotid artery using hypoechoic speckle pixels. The proposed technique was executed on 88 images of the thyroid gland. Clinical significance of using speckles to segment is determined by validating on 32 images of the thyroid gland by measuring the overlap with the Ground Truth segmentation obtained from two expert doctors using Dice coefficient as the overlap measure.
{"title":"Speckle in ultrasound images: Friend or FOE?","authors":"Nikhil S. Narayan, P. Marziliano, J. Kanagalingam, C. Hobbs","doi":"10.1109/ICIP.2014.7026176","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7026176","url":null,"abstract":"Contrary to the popular belief of treating speckle related pixels as noise and filtering an ultrasound image for speckle noise removal, the practical importance and use of these pixels in performing a multi-organ segmentation of the thyroid gland is studied in this research work. In this work, speckle related pixels are classified into three echogenic levels and then used to segment an ultrasound image of the thyroid gland into the trachea, carotid, muscles and thyroid. Novel techniques are introduced to estimate the anterior boundaries of the thyroid gland using low pass filtered intensity gradients of the hyperechoic speckle pixels in transverse and longitudinal ultrasound scans, respectively. An energy functional similar to active contour models is defined to segment that carotid artery using hypoechoic speckle pixels. The proposed technique was executed on 88 images of the thyroid gland. Clinical significance of using speckles to segment is determined by validating on 32 images of the thyroid gland by measuring the overlap with the Ground Truth segmentation obtained from two expert doctors using Dice coefficient as the overlap measure.","PeriodicalId":6856,"journal":{"name":"2014 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85109520","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 : 2014-10-01DOI: 10.1109/ICIP.2014.7025309
S. Ueno, S. Naito, Tsuhan Chen
In this paper, we propose an efficient calibration method to estimate the pointing direction via a human pointing gesture to facilitate robot interaction. The ways in which pointing gestures are used by humans to indicate an object are individually diverse. In addition, people do not always point at the object carefully, which means there is a divergence between the line from the eye to the tip of the index finger and the line of sight. Hence, we focus on adapting to these individual ways of pointing to improve the accuracy of target object identification by means of an effective calibration process. We model these individual ways as two offsets, the horizontal offset and the vertical offset. After locating the head and fingertip positions, we learn these offsets for each individual through a training process with the person pointing at the camera. Experimental results show that our proposed method outperforms other conventional head-hand, head-fingertip, and eye-fingertip-based pointing recognition methods.
{"title":"An efficient method for human pointing estimation for robot interaction","authors":"S. Ueno, S. Naito, Tsuhan Chen","doi":"10.1109/ICIP.2014.7025309","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025309","url":null,"abstract":"In this paper, we propose an efficient calibration method to estimate the pointing direction via a human pointing gesture to facilitate robot interaction. The ways in which pointing gestures are used by humans to indicate an object are individually diverse. In addition, people do not always point at the object carefully, which means there is a divergence between the line from the eye to the tip of the index finger and the line of sight. Hence, we focus on adapting to these individual ways of pointing to improve the accuracy of target object identification by means of an effective calibration process. We model these individual ways as two offsets, the horizontal offset and the vertical offset. After locating the head and fingertip positions, we learn these offsets for each individual through a training process with the person pointing at the camera. Experimental results show that our proposed method outperforms other conventional head-hand, head-fingertip, and eye-fingertip-based pointing recognition methods.","PeriodicalId":6856,"journal":{"name":"2014 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86006515","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 : 2014-10-01DOI: 10.1109/ICIP.2014.7026027
Guangliang Cheng, Ying Wang, Yongchao Gong, Feiyun Zhu, Chunhong Pan
In this paper, we propose a graph cuts (GC) based probability propagation approach to automatically extract road network from complex remote sensing images. First, the support vector machine (SVM) classifier with a sigmoid model is applied to assign each pixel a posterior probability of being labelled as road class, which avoids the weaknesses of hard labels in general SVM. Then a GC based probability propagation algorithm is employed to keep the extracted road results smooth and coherent, which can reduce the connections between roads and road-like objects. Finally, a road-geometrical prior is considered to refine the extraction result, so that the non-road objects in images can be removed. Experimental results on two remote sensing image datasets indicate the validity and effectiveness of our method by comparing with two other approaches.
{"title":"Urban road extraction via graph cuts based probability propagation","authors":"Guangliang Cheng, Ying Wang, Yongchao Gong, Feiyun Zhu, Chunhong Pan","doi":"10.1109/ICIP.2014.7026027","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7026027","url":null,"abstract":"In this paper, we propose a graph cuts (GC) based probability propagation approach to automatically extract road network from complex remote sensing images. First, the support vector machine (SVM) classifier with a sigmoid model is applied to assign each pixel a posterior probability of being labelled as road class, which avoids the weaknesses of hard labels in general SVM. Then a GC based probability propagation algorithm is employed to keep the extracted road results smooth and coherent, which can reduce the connections between roads and road-like objects. Finally, a road-geometrical prior is considered to refine the extraction result, so that the non-road objects in images can be removed. Experimental results on two remote sensing image datasets indicate the validity and effectiveness of our method by comparing with two other approaches.","PeriodicalId":6856,"journal":{"name":"2014 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77228193","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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