Many tracking methods face a fundamental dilemma in practice: tracking has to be computationally efficient but verifying if or not the tracker is following the true target tends to be demanding, especially when the background is cluttered and/or when occlusion occurs. Due to the lack of a good solution to this problem, many existing methods tend to be either computationally intensive with the use of sophisticated image observation models, or vulnerable to the false alarms. This greatly threatens long-duration robust tracking. This paper presents a novel solution to this dilemma by integrating into the tracking process a set of auxiliary objects that are automatically discovered in the video on the fly by data mining. Auxiliary objects have three properties at least in a short time interval: (1) persistent co-occurrence with the target; (2) consistent motion correlation with the target; and (3) easy to track. The collaborative tracking of these auxiliary objects leads to an efficient computation as well as a strong verification. Our extensive experiments have exhibited exciting performance in very challenging real-world testing cases.
{"title":"Intelligent Collaborative Tracking by Mining Auxiliary Objects","authors":"Ming Yang, Ying Wu, S. Lao","doi":"10.1109/CVPR.2006.157","DOIUrl":"https://doi.org/10.1109/CVPR.2006.157","url":null,"abstract":"Many tracking methods face a fundamental dilemma in practice: tracking has to be computationally efficient but verifying if or not the tracker is following the true target tends to be demanding, especially when the background is cluttered and/or when occlusion occurs. Due to the lack of a good solution to this problem, many existing methods tend to be either computationally intensive with the use of sophisticated image observation models, or vulnerable to the false alarms. This greatly threatens long-duration robust tracking. This paper presents a novel solution to this dilemma by integrating into the tracking process a set of auxiliary objects that are automatically discovered in the video on the fly by data mining. Auxiliary objects have three properties at least in a short time interval: (1) persistent co-occurrence with the target; (2) consistent motion correlation with the target; and (3) easy to track. The collaborative tracking of these auxiliary objects leads to an efficient computation as well as a strong verification. Our extensive experiments have exhibited exciting performance in very challenging real-world testing cases.","PeriodicalId":421737,"journal":{"name":"2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)","volume":"251 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114246516","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}
S. P. Mallick, Sameer Agarwal, D. Kriegman, Serge J. Belongie, B. Carragher, C. Potter
This paper addresses the problem of reconstructing the density of a scene from multiple projection images produced by modalities such as x-ray, electron microscopy, etc. where an image value is related to the integral of the scene density along a 3D line segment between a radiation source and a point on the image plane. While computed tomography (CT) addresses this problem when the absolute orientation of the image plane and radiation source directions are known, this paper addresses the problem when the orientations are unknown - it is akin to the structure-from-motion (SFM) problem when the extrinsic camera parameters are unknown. We study the problem within the context of reconstructing the density of protein macro-molecules in Cryogenic Electron Microscopy (cryo-EM), where images are very noisy and existing techniques use several thousands of images. In a non-degenerate configuration, the viewing planes corresponding to two projections, intersect in a line in 3D. Using the geometry of the imaging setup, it is possible to determine the projections of this 3D line on the two image planes. In turn, the problem can be formulated as a type of orthographic structure from motion from line correspondences where the line correspondences between two views are unreliable due to image noise. We formulate the task as the problem of denoising a correspondence matrix and present a Bayesian solution to it. Subsequently, the absolute orientation of each projection is determined followed by density reconstruction. We show results on cryo-EM images of proteins and compare our results to that of Electron Micrograph Analysis (EMAN) - a widely used reconstruction tool in cryo-EM.
{"title":"Structure and View Estimation for Tomographic Reconstruction: A Bayesian Approach","authors":"S. P. Mallick, Sameer Agarwal, D. Kriegman, Serge J. Belongie, B. Carragher, C. Potter","doi":"10.1109/CVPR.2006.295","DOIUrl":"https://doi.org/10.1109/CVPR.2006.295","url":null,"abstract":"This paper addresses the problem of reconstructing the density of a scene from multiple projection images produced by modalities such as x-ray, electron microscopy, etc. where an image value is related to the integral of the scene density along a 3D line segment between a radiation source and a point on the image plane. While computed tomography (CT) addresses this problem when the absolute orientation of the image plane and radiation source directions are known, this paper addresses the problem when the orientations are unknown - it is akin to the structure-from-motion (SFM) problem when the extrinsic camera parameters are unknown. We study the problem within the context of reconstructing the density of protein macro-molecules in Cryogenic Electron Microscopy (cryo-EM), where images are very noisy and existing techniques use several thousands of images. In a non-degenerate configuration, the viewing planes corresponding to two projections, intersect in a line in 3D. Using the geometry of the imaging setup, it is possible to determine the projections of this 3D line on the two image planes. In turn, the problem can be formulated as a type of orthographic structure from motion from line correspondences where the line correspondences between two views are unreliable due to image noise. We formulate the task as the problem of denoising a correspondence matrix and present a Bayesian solution to it. Subsequently, the absolute orientation of each projection is determined followed by density reconstruction. We show results on cryo-EM images of proteins and compare our results to that of Electron Micrograph Analysis (EMAN) - a widely used reconstruction tool in cryo-EM.","PeriodicalId":421737,"journal":{"name":"2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114601255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A new approach to template tracking is presented, incorporating three distinct contributions. Firstly, an explicit definition for a feature track is given. Secondly, the advantages of an image preprocessing stage are demonstrated and, in particular, the effectiveness of highly compressed image patch data stored in k-d trees for fast and discriminatory image patch searches. Thirdly, the k-d trees are used to generate multiple track hypotheses which are efficiently merged to give the optimal solution using dynamic programming. The explicit separation of feature detection and trajectory determination creates the basis for the novel use of k-d trees and dynamic programming. Multiple appearances and occlusion handling are seamlessly integrated into this framework. Appearance variation through the sequence is robustly handled in an iterative process. The work presented is a significant foundation for a powerful off-line feature tracking system, particularly in the context of interactive applications.
{"title":"Interactive Feature Tracking using K-D Trees and Dynamic Programming","authors":"Aeron Buchanan, A. Fitzgibbon","doi":"10.1109/CVPR.2006.158","DOIUrl":"https://doi.org/10.1109/CVPR.2006.158","url":null,"abstract":"A new approach to template tracking is presented, incorporating three distinct contributions. Firstly, an explicit definition for a feature track is given. Secondly, the advantages of an image preprocessing stage are demonstrated and, in particular, the effectiveness of highly compressed image patch data stored in k-d trees for fast and discriminatory image patch searches. Thirdly, the k-d trees are used to generate multiple track hypotheses which are efficiently merged to give the optimal solution using dynamic programming. The explicit separation of feature detection and trajectory determination creates the basis for the novel use of k-d trees and dynamic programming. Multiple appearances and occlusion handling are seamlessly integrated into this framework. Appearance variation through the sequence is robustly handled in an iterative process. The work presented is a significant foundation for a powerful off-line feature tracking system, particularly in the context of interactive applications.","PeriodicalId":421737,"journal":{"name":"2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115748773","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}
We introduce a family of spectral partitioning methods. Edge separators of a graph are produced by iteratively reweighting the edges until the graph disconnects into the prescribed number of components. At each iteration a small number of eigenvectors with small eigenvalue are computed and used to determine the reweighting. In this way spectral rounding directly produces discrete solutions where as current spectral algorithms must map the continuous eigenvectors to discrete solutions by employing a heuristic geometric separator (e.g. k-means). We show that spectral rounding compares favorably to current spectral approximations on the Normalized Cut criterion (NCut). Results are given for natural image segmentation, medical image segmentation, and clustering. A practical version is shown to converge.
{"title":"Graph Partitioning by Spectral Rounding: Applications in Image Segmentation and Clustering","authors":"David Tolliver, G. Miller","doi":"10.1109/CVPR.2006.129","DOIUrl":"https://doi.org/10.1109/CVPR.2006.129","url":null,"abstract":"We introduce a family of spectral partitioning methods. Edge separators of a graph are produced by iteratively reweighting the edges until the graph disconnects into the prescribed number of components. At each iteration a small number of eigenvectors with small eigenvalue are computed and used to determine the reweighting. In this way spectral rounding directly produces discrete solutions where as current spectral algorithms must map the continuous eigenvectors to discrete solutions by employing a heuristic geometric separator (e.g. k-means). We show that spectral rounding compares favorably to current spectral approximations on the Normalized Cut criterion (NCut). Results are given for natural image segmentation, medical image segmentation, and clustering. A practical version is shown to converge.","PeriodicalId":421737,"journal":{"name":"2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116277051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A combination of techniques that is becoming increasingly popular is the construction of part-based object representations using the outputs of interest-point detectors. Our contributions in this paper are twofold: first, we propose a primal-sketch-based set of image tokens that are used for object representation and detection. Second, top-down information is introduced based on an efficient method for the evaluation of the likelihood of hypothesized part locations. This allows us to use graphical model techniques to complement bottom-up detection, by proposing and finding the parts of the object that were missed by the front-end feature detection stage. Detection results for four object categories validate the merits of this joint top-down and bottom-up approach.
{"title":"Bottom-Up & Top-down Object Detection using Primal Sketch Features and Graphical Models","authors":"Iasonas Kokkinos, P. Maragos, A. Yuille","doi":"10.1109/CVPR.2006.74","DOIUrl":"https://doi.org/10.1109/CVPR.2006.74","url":null,"abstract":"A combination of techniques that is becoming increasingly popular is the construction of part-based object representations using the outputs of interest-point detectors. Our contributions in this paper are twofold: first, we propose a primal-sketch-based set of image tokens that are used for object representation and detection. Second, top-down information is introduced based on an efficient method for the evaluation of the likelihood of hypothesized part locations. This allows us to use graphical model techniques to complement bottom-up detection, by proposing and finding the parts of the object that were missed by the front-end feature detection stage. Detection results for four object categories validate the merits of this joint top-down and bottom-up approach.","PeriodicalId":421737,"journal":{"name":"2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123283831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a new model of object classes which incorporates appearance and shape information jointly. Modeling objects appearance by distributions of visual words has recently proven successful. Here appearancebased models are augmented by capturing the spatial arrangement of visual words. Compact spatial modeling without loss of discrimination is achieved through the introduction of adaptive vector quantized correlograms, which we call correlatons. Efficiency is further improved by means of integral images. The robustness of our new models to geometric transformations, severe occlusions and missing information is also demonstrated. The accuracy of discrimination of the proposed models is assessed with respect to existing databases with large numbers of object classes viewed under general conditions, and shown to outperform appearance-only models.
{"title":"Discriminative Object Class Models of Appearance and Shape by Correlatons","authors":"S. Savarese, J. Winn, A. Criminisi","doi":"10.1109/CVPR.2006.102","DOIUrl":"https://doi.org/10.1109/CVPR.2006.102","url":null,"abstract":"This paper presents a new model of object classes which incorporates appearance and shape information jointly. Modeling objects appearance by distributions of visual words has recently proven successful. Here appearancebased models are augmented by capturing the spatial arrangement of visual words. Compact spatial modeling without loss of discrimination is achieved through the introduction of adaptive vector quantized correlograms, which we call correlatons. Efficiency is further improved by means of integral images. The robustness of our new models to geometric transformations, severe occlusions and missing information is also demonstrated. The accuracy of discrimination of the proposed models is assessed with respect to existing databases with large numbers of object classes viewed under general conditions, and shown to outperform appearance-only models.","PeriodicalId":421737,"journal":{"name":"2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124813956","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}
Gaussian mean-shift (GMS) is a clustering algorithm that has been shown to produce good image segmentations (where each pixel is represented as a feature vector with spatial and range components). GMS operates by defining a Gaussian kernel density estimate for the data and clustering together points that converge to the same mode under a fixed-point iterative scheme. However, the algorithm is slow, since its complexity is O(kN2), where N is the number of pixels and k the average number of iterations per pixel. We study four acceleration strategies for GMS based on the spatial structure of images and on the fact that GMS is an expectation-maximisation (EM) algorithm: spatial discretisation, spatial neighbourhood, sparse EM and EM-Newton algorithm. We show that the spatial discretisation strategy can accelerate GMS by one to two orders of magnitude while achieving essentially the same segmentation; and that the other strategies attain speedups of less than an order of magnitude.
{"title":"Acceleration Strategies for Gaussian Mean-Shift Image Segmentation","authors":"M. A. Carreira-Perpiñán","doi":"10.1109/CVPR.2006.44","DOIUrl":"https://doi.org/10.1109/CVPR.2006.44","url":null,"abstract":"Gaussian mean-shift (GMS) is a clustering algorithm that has been shown to produce good image segmentations (where each pixel is represented as a feature vector with spatial and range components). GMS operates by defining a Gaussian kernel density estimate for the data and clustering together points that converge to the same mode under a fixed-point iterative scheme. However, the algorithm is slow, since its complexity is O(kN2), where N is the number of pixels and k the average number of iterations per pixel. We study four acceleration strategies for GMS based on the spatial structure of images and on the fact that GMS is an expectation-maximisation (EM) algorithm: spatial discretisation, spatial neighbourhood, sparse EM and EM-Newton algorithm. We show that the spatial discretisation strategy can accelerate GMS by one to two orders of magnitude while achieving essentially the same segmentation; and that the other strategies attain speedups of less than an order of magnitude.","PeriodicalId":421737,"journal":{"name":"2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123623649","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}
We develop a classification algorithm for hybrid autoregressive models of human motion for the purpose of videobased analysis and recognition. We assume that some temporal statistics are extracted from the images, and we use them to infer a dynamical system that explicitly models contact forces. We then develop a distance between such models that explicitly factors out exogenous inputs that are not unique to an individual or her gait. We show that such a distance is more discriminative than the distance between simple linear systems, where most of the energy is devoted to modeling the dynamics of spurious nuisances such as contact forces.
{"title":"Classifying Human Dynamics Without Contact Forces","authors":"A. Bissacco, Stefano Soatto","doi":"10.1109/CVPR.2006.75","DOIUrl":"https://doi.org/10.1109/CVPR.2006.75","url":null,"abstract":"We develop a classification algorithm for hybrid autoregressive models of human motion for the purpose of videobased analysis and recognition. We assume that some temporal statistics are extracted from the images, and we use them to infer a dynamical system that explicitly models contact forces. We then develop a distance between such models that explicitly factors out exogenous inputs that are not unique to an individual or her gait. We show that such a distance is more discriminative than the distance between simple linear systems, where most of the energy is devoted to modeling the dynamics of spurious nuisances such as contact forces.","PeriodicalId":421737,"journal":{"name":"2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122716220","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}
Many sources of information relevant to computer vision and machine learning tasks are often underused. One example is the similarity between the elements from a novel source, such as a speaker, writer, or printed font. By comparing instances emitted by a source, we help ensure that similar instances are given the same label. Previous approaches have clustered instances prior to recognition. We propose a probabilistic framework that unifies similarity with prior identity and contextual information. By fusing information sources in a single model, we eliminate unrecoverable errors that result from processing the information in separate stages and improve overall accuracy. The framework also naturally integrates dissimilarity information, which has previously been ignored. We demonstrate with an application in printed character recognition from images of signs in natural scenes.
{"title":"Improving Recognition of Novel Input with Similarity","authors":"Jerod J. Weinman, E. Learned-Miller","doi":"10.1109/CVPR.2006.151","DOIUrl":"https://doi.org/10.1109/CVPR.2006.151","url":null,"abstract":"Many sources of information relevant to computer vision and machine learning tasks are often underused. One example is the similarity between the elements from a novel source, such as a speaker, writer, or printed font. By comparing instances emitted by a source, we help ensure that similar instances are given the same label. Previous approaches have clustered instances prior to recognition. We propose a probabilistic framework that unifies similarity with prior identity and contextual information. By fusing information sources in a single model, we eliminate unrecoverable errors that result from processing the information in separate stages and improve overall accuracy. The framework also naturally integrates dissimilarity information, which has previously been ignored. We demonstrate with an application in printed character recognition from images of signs in natural scenes.","PeriodicalId":421737,"journal":{"name":"2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)","volume":"197 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125298014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper we propose diffusion distance, a new dissimilarity measure between histogram-based descriptors. We define the difference between two histograms to be a temperature field. We then study the relationship between histogram similarity and a diffusion process, showing how diffusion handles deformation as well as quantization effects. As a result, the diffusion distance is derived as the sum of dissimilarities over scales. Being a cross-bin histogram distance, the diffusion distance is robust to deformation, lighting change and noise in histogram-based local descriptors. In addition, it enjoys linear computational complexity which significantly improves previously proposed cross-bin distances with quadratic complexity or higher. We tested the proposed approach on both shape recognition and interest point matching tasks using several multi-dimensional histogram-based descriptors including shape context, SIFT, and spin images. In all experiments, the diffusion distance performs excellently in both accuracy and efficiency in comparison with other state-of-the-art distance measures. In particular, it performs as accurately as the Earth Mover’s Distance with much greater efficiency.
{"title":"Diffusion Distance for Histogram Comparison","authors":"Haibin Ling, K. Okada","doi":"10.1109/CVPR.2006.99","DOIUrl":"https://doi.org/10.1109/CVPR.2006.99","url":null,"abstract":"In this paper we propose diffusion distance, a new dissimilarity measure between histogram-based descriptors. We define the difference between two histograms to be a temperature field. We then study the relationship between histogram similarity and a diffusion process, showing how diffusion handles deformation as well as quantization effects. As a result, the diffusion distance is derived as the sum of dissimilarities over scales. Being a cross-bin histogram distance, the diffusion distance is robust to deformation, lighting change and noise in histogram-based local descriptors. In addition, it enjoys linear computational complexity which significantly improves previously proposed cross-bin distances with quadratic complexity or higher. We tested the proposed approach on both shape recognition and interest point matching tasks using several multi-dimensional histogram-based descriptors including shape context, SIFT, and spin images. In all experiments, the diffusion distance performs excellently in both accuracy and efficiency in comparison with other state-of-the-art distance measures. In particular, it performs as accurately as the Earth Mover’s Distance with much greater efficiency.","PeriodicalId":421737,"journal":{"name":"2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129278327","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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