Pub Date : 2000-06-13DOI: 10.1109/CVBVS.2000.855246
I. Pavlidis, P. Symosek
Automatic face recognition systems have made great strides. They still, however cannot cope with changes due to lighting and cannot detect disguises, Both of these issues are critical for the employment of face recognition systems in high security applications, such as embassy perimeters, federal plazas, mid the like. We propose novel imaging solutions that address these difficult problems. We demonstrate with theoretical and experimental arguments that a dual-band fusion system in the near infrared can segment human faces much more accurately than traditional visible band face detection systems. Face detection is useful by itself as an early warning method in certain surveillance applications. Accurate face delineation can also improve the performance of face recognition systems in certain difficult scenarios, particularly in outside environments. We also demonstrate with theoretical and experimental arguments that the upper band of the near infrared (1.4-2.4 /spl mu/m) is particularly advantageous for disguise detection purposes. This is attributable to the unique and universal properties of the human skin in this sub-band. We conclude the paper with a description of our ongoing and future efforts.
{"title":"The imaging issue in an automatic face/disguise detection system","authors":"I. Pavlidis, P. Symosek","doi":"10.1109/CVBVS.2000.855246","DOIUrl":"https://doi.org/10.1109/CVBVS.2000.855246","url":null,"abstract":"Automatic face recognition systems have made great strides. They still, however cannot cope with changes due to lighting and cannot detect disguises, Both of these issues are critical for the employment of face recognition systems in high security applications, such as embassy perimeters, federal plazas, mid the like. We propose novel imaging solutions that address these difficult problems. We demonstrate with theoretical and experimental arguments that a dual-band fusion system in the near infrared can segment human faces much more accurately than traditional visible band face detection systems. Face detection is useful by itself as an early warning method in certain surveillance applications. Accurate face delineation can also improve the performance of face recognition systems in certain difficult scenarios, particularly in outside environments. We also demonstrate with theoretical and experimental arguments that the upper band of the near infrared (1.4-2.4 /spl mu/m) is particularly advantageous for disguise detection purposes. This is attributable to the unique and universal properties of the human skin in this sub-band. We conclude the paper with a description of our ongoing and future efforts.","PeriodicalId":231063,"journal":{"name":"Proceedings IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications (Cat. No.PR00640)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125961849","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 : 2000-06-13DOI: 10.1109/CVBVS.2000.855257
E. Yfantis, T. Lazarakis, A. Popovich, A. Angelopoulos, G. Bebis
An algorithm of cancer recognition in ultrasound images is developed in this paper. In order for cancer to survive it develops its own blood supply system, which is different than the supply system of normal tissue. The velocity of the blood flowing through the cancerous blood vessels is different than the velocity of the blood flowing through blood vessels of normal tissue. Due to this fact the ultrasound signal is absorbed differently in the cancerous areas than in the normal tissue areas. The energy of the signal, the continuity of the signal, the autocorrelation function and frequency domain properties are different in the normal tissue than in cancerous tissue. All of these indicators are weighted here for the purpose of classifying the image of the tissue as being cancerous or non-cancerous. Preliminary results based on limited number of ultrasound images show that our method has the ability to recognize cancer in ultrasound images.
{"title":"On cancer recognition of ultrasound images","authors":"E. Yfantis, T. Lazarakis, A. Popovich, A. Angelopoulos, G. Bebis","doi":"10.1109/CVBVS.2000.855257","DOIUrl":"https://doi.org/10.1109/CVBVS.2000.855257","url":null,"abstract":"An algorithm of cancer recognition in ultrasound images is developed in this paper. In order for cancer to survive it develops its own blood supply system, which is different than the supply system of normal tissue. The velocity of the blood flowing through the cancerous blood vessels is different than the velocity of the blood flowing through blood vessels of normal tissue. Due to this fact the ultrasound signal is absorbed differently in the cancerous areas than in the normal tissue areas. The energy of the signal, the continuity of the signal, the autocorrelation function and frequency domain properties are different in the normal tissue than in cancerous tissue. All of these indicators are weighted here for the purpose of classifying the image of the tissue as being cancerous or non-cancerous. Preliminary results based on limited number of ultrasound images show that our method has the ability to recognize cancer in ultrasound images.","PeriodicalId":231063,"journal":{"name":"Proceedings IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications (Cat. No.PR00640)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134007985","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 : 2000-06-13DOI: 10.1109/CVBVS.2000.855255
I. Pavlidis, J. Levine, P. Baukol
We propose a revolutionary concept for detecting suspects engaged in illegal and potentially harmful activities in or around critical military or civilian installations. We investigate the use of thermal image analysis to detect at a distance facial patterns of anxiety, alertness, and/or fearfulness. This is a totally novel approach to the problem of biometric identification. Instead of focusing on the question "who are you" we focus instead on the question "what are you about to do". Documented preliminary results clearly indicate the feasibility of the idea.
{"title":"Thermal imaging for anxiety detection","authors":"I. Pavlidis, J. Levine, P. Baukol","doi":"10.1109/CVBVS.2000.855255","DOIUrl":"https://doi.org/10.1109/CVBVS.2000.855255","url":null,"abstract":"We propose a revolutionary concept for detecting suspects engaged in illegal and potentially harmful activities in or around critical military or civilian installations. We investigate the use of thermal image analysis to detect at a distance facial patterns of anxiety, alertness, and/or fearfulness. This is a totally novel approach to the problem of biometric identification. Instead of focusing on the question \"who are you\" we focus instead on the question \"what are you about to do\". Documented preliminary results clearly indicate the feasibility of the idea.","PeriodicalId":231063,"journal":{"name":"Proceedings IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications (Cat. No.PR00640)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126762432","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 : 2000-06-13DOI: 10.1109/CVBVS.2000.855247
D. G. Arnold, K. Sturtz
Research in the calculation and exploitation of geometric object/image relations suggests new approaches to automatic target recognition (ATR). The resulting conceptual advances have efficiently solved the geometry problem and have transformed the ATR problem into a correspondence problem. This paper provides an analysis of the elementary algorithms which exploit object/image relations as a basis for object recognition. The goal is to develop an understanding for the critical components to address for developing a practical system. 3D laser radar is used as an example.
{"title":"Complexity analysis of ATR algorithms based on invariants","authors":"D. G. Arnold, K. Sturtz","doi":"10.1109/CVBVS.2000.855247","DOIUrl":"https://doi.org/10.1109/CVBVS.2000.855247","url":null,"abstract":"Research in the calculation and exploitation of geometric object/image relations suggests new approaches to automatic target recognition (ATR). The resulting conceptual advances have efficiently solved the geometry problem and have transformed the ATR problem into a correspondence problem. This paper provides an analysis of the elementary algorithms which exploit object/image relations as a basis for object recognition. The goal is to develop an understanding for the critical components to address for developing a practical system. 3D laser radar is used as an example.","PeriodicalId":231063,"journal":{"name":"Proceedings IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications (Cat. No.PR00640)","volume":"23 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121928346","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 : 2000-06-13DOI: 10.1109/CVBVS.2000.855245
F. Prokoski
Infrared identification (IRID) is a biometric which offers the security of fingerprints with the convenience of facial recognition. Twenty years after being first proposed IRID systems are becoming marketable. During the intervening time, IR camera technology has improved significantly and price reductions have been dramatic. Further improvements in IR camera sensitivity, array size, and variable optics, as well as further price reductions, are coming, just as interest is growing in biometrics. Several approaches have been developed, tested, and evaluated to extract and analyze features from infrared images of the face and body. IRID has been seen to have significant advantages over other biometrics in certain applications-not only in the traditional biometric security markers, but also in communications and health care.
{"title":"History, current status, and future of infrared identification","authors":"F. Prokoski","doi":"10.1109/CVBVS.2000.855245","DOIUrl":"https://doi.org/10.1109/CVBVS.2000.855245","url":null,"abstract":"Infrared identification (IRID) is a biometric which offers the security of fingerprints with the convenience of facial recognition. Twenty years after being first proposed IRID systems are becoming marketable. During the intervening time, IR camera technology has improved significantly and price reductions have been dramatic. Further improvements in IR camera sensitivity, array size, and variable optics, as well as further price reductions, are coming, just as interest is growing in biometrics. Several approaches have been developed, tested, and evaluated to extract and analyze features from infrared images of the face and body. IRID has been seen to have significant advantages over other biometrics in certain applications-not only in the traditional biometric security markers, but also in communications and health care.","PeriodicalId":231063,"journal":{"name":"Proceedings IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications (Cat. No.PR00640)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126332070","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 : 2000-06-13DOI: 10.1109/CVBVS.2000.855251
A. Bors, E. Hancock, Richard C. Wilson
Surface analysis is important for automatic terrain cartography and for airborne navigation. This paper proposes a new approach to shape-from-shading (SFS) in synthetic aperture radar (SAR) images. The SFS problem is embedded in a Bayesian framework. We maximize the surface orientation probability using SAR image statistics, local smoothing and constraints imposed by object discontinuities. We model the statistics of the SAR image distribution as a product between the Rayleigh and Bessel functions. We derive the optimal edge detector for this distribution. The resulting edges are classified as ridges and ravines according to a statistical test. Afterwards, the edges are used as constraints in the estimation of the surface normals. We propose various smoothing algorithms for the vector field of surface normals using robust statistics and surface curvature consistency. The results provided by these algorithms are compared with those given by local averaging.
{"title":"3-D terrain from synthetic aperture radar images","authors":"A. Bors, E. Hancock, Richard C. Wilson","doi":"10.1109/CVBVS.2000.855251","DOIUrl":"https://doi.org/10.1109/CVBVS.2000.855251","url":null,"abstract":"Surface analysis is important for automatic terrain cartography and for airborne navigation. This paper proposes a new approach to shape-from-shading (SFS) in synthetic aperture radar (SAR) images. The SFS problem is embedded in a Bayesian framework. We maximize the surface orientation probability using SAR image statistics, local smoothing and constraints imposed by object discontinuities. We model the statistics of the SAR image distribution as a product between the Rayleigh and Bessel functions. We derive the optimal edge detector for this distribution. The resulting edges are classified as ridges and ravines according to a statistical test. Afterwards, the edges are used as constraints in the estimation of the surface normals. We propose various smoothing algorithms for the vector field of surface normals using robust statistics and surface curvature consistency. The results provided by these algorithms are compared with those given by local averaging.","PeriodicalId":231063,"journal":{"name":"Proceedings IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications (Cat. No.PR00640)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134251110","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 : 2000-06-13DOI: 10.1109/CVBVS.2000.855252
B. Bhanu, Yingqiang Lin
Recognition of occluded objects in synthetic aperture radar (SAR) images is a significant problem for automatic target recognition. In this paper, we present a hidden Markov modeling (HMM) based approach for recognizing objects in synthetic aperture radar (SAR) images. We identify the peculiar characteristics of SAR sensors and using these characteristics we develop feature based multiple models for a given SAR image of an object. The models exploiting the relative geometry of feature locations or the amplitude of SAR radar return are based on sequentialization of scattering centers extracted from SAR images. In order to improve performance we integrate these models synergistically using their probabalistic estimates for recognition of a particular target at a specific azimuth. Experimental results are presented using both synthetic and real SAR images.
{"title":"Recognition of occluded targets using stochastic models","authors":"B. Bhanu, Yingqiang Lin","doi":"10.1109/CVBVS.2000.855252","DOIUrl":"https://doi.org/10.1109/CVBVS.2000.855252","url":null,"abstract":"Recognition of occluded objects in synthetic aperture radar (SAR) images is a significant problem for automatic target recognition. In this paper, we present a hidden Markov modeling (HMM) based approach for recognizing objects in synthetic aperture radar (SAR) images. We identify the peculiar characteristics of SAR sensors and using these characteristics we develop feature based multiple models for a given SAR image of an object. The models exploiting the relative geometry of feature locations or the amplitude of SAR radar return are based on sequentialization of scattering centers extracted from SAR images. In order to improve performance we integrate these models synergistically using their probabalistic estimates for recognition of a particular target at a specific azimuth. Experimental results are presented using both synthetic and real SAR images.","PeriodicalId":231063,"journal":{"name":"Proceedings IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications (Cat. No.PR00640)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128603444","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 : 2000-06-13DOI: 10.1109/CVBVS.2000.855254
B. Nelson
A forward looking infrared sensor used in conjunction with a vehicular based landmine detection system is described. The paper first describes the requirements for vehicularly-deployed forward-looking infrared sensors. This is followed by descriptions of the basic forward looking infrared sensor, its components and its integration to the vehicular platform. This is followed by a description of a novel method that is used to define regions of interest within the infrared images in real time. Next the specific features that are extracted from the infrared images that are used in target classification are provided. The paper then describes a fuzzy inference system that evaluates the extracted data features and generates a mine confidence value that is used for final declaration of potential targets as mines or clutter. Specific examples of attained performance both with and without the fuzzy inference system are provided to demonstrate the efficacy of the fuzzy inference system in evaluating data features and performing the classification. Lastly, the paper describes other areas where the described methods are being successfully applied.
{"title":"Region of interest identification, feature extraction, and information fusion in a forward looking infrared sensor used in landmine detection","authors":"B. Nelson","doi":"10.1109/CVBVS.2000.855254","DOIUrl":"https://doi.org/10.1109/CVBVS.2000.855254","url":null,"abstract":"A forward looking infrared sensor used in conjunction with a vehicular based landmine detection system is described. The paper first describes the requirements for vehicularly-deployed forward-looking infrared sensors. This is followed by descriptions of the basic forward looking infrared sensor, its components and its integration to the vehicular platform. This is followed by a description of a novel method that is used to define regions of interest within the infrared images in real time. Next the specific features that are extracted from the infrared images that are used in target classification are provided. The paper then describes a fuzzy inference system that evaluates the extracted data features and generates a mine confidence value that is used for final declaration of potential targets as mines or clutter. Specific examples of attained performance both with and without the fuzzy inference system are provided to demonstrate the efficacy of the fuzzy inference system in evaluating data features and performing the classification. Lastly, the paper describes other areas where the described methods are being successfully applied.","PeriodicalId":231063,"journal":{"name":"Proceedings IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications (Cat. No.PR00640)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128570003","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 : 2000-06-13DOI: 10.1109/CVBVS.2000.855248
Qing Li, Manli Zhou, Jian Liu
Motivated by the need of the numerous real-world applications, 3D object recognition has become an active research field. The representation describes the sensed data and the object models, and it is a key issue in the recognition process, which decides the match strategy and the effectiveness and robustness of the recognition system. In this paper, we propose an improved 3D object representation first, which computes the local signatures of a given basis polygon on the surface mesh, and converts the signatures to a 2D array called the distance-angle (DA) images by weighted bilinear interpolation. This representation is adaptive to free-form objects and resistant to occlusion and clutter. Compared with the original representation, it has a more distinct meaning, easier operation, and adaptation to different resolutions and irregular triangle meshes. Secondly, based on the improved representation, a novel 3D recognition algorithm is presented, which has multiresolution mesh based, coarse-to-fine recognition. By matching the DA image of a polygon in the scene surface mesh with the DA images of models at low resolution, a model candidate set is obtained. The set is filtered in the neighborhood of the matched polygons in a high-resolution mesh and verified by the model candidate sets of other polygons. Experiments show that this algorithm needs less computation and is very accurate and robust.
{"title":"Multi-resolution mesh based 3D object recognition","authors":"Qing Li, Manli Zhou, Jian Liu","doi":"10.1109/CVBVS.2000.855248","DOIUrl":"https://doi.org/10.1109/CVBVS.2000.855248","url":null,"abstract":"Motivated by the need of the numerous real-world applications, 3D object recognition has become an active research field. The representation describes the sensed data and the object models, and it is a key issue in the recognition process, which decides the match strategy and the effectiveness and robustness of the recognition system. In this paper, we propose an improved 3D object representation first, which computes the local signatures of a given basis polygon on the surface mesh, and converts the signatures to a 2D array called the distance-angle (DA) images by weighted bilinear interpolation. This representation is adaptive to free-form objects and resistant to occlusion and clutter. Compared with the original representation, it has a more distinct meaning, easier operation, and adaptation to different resolutions and irregular triangle meshes. Secondly, based on the improved representation, a novel 3D recognition algorithm is presented, which has multiresolution mesh based, coarse-to-fine recognition. By matching the DA image of a polygon in the scene surface mesh with the DA images of models at low resolution, a model candidate set is obtained. The set is filtered in the neighborhood of the matched polygons in a high-resolution mesh and verified by the model candidate sets of other polygons. Experiments show that this algorithm needs less computation and is very accurate and robust.","PeriodicalId":231063,"journal":{"name":"Proceedings IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications (Cat. No.PR00640)","volume":"15 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127645530","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 : 2000-06-13DOI: 10.1109/CVBVS.2000.855253
H. Haussecker
This paper incorporates physical models of heat transport into motion analysis in infrared image sequences. Physical transport processes, such as heat diffusion and decay, are causing time dependent brightness variations, violating the common brightness constancy assumption. Previous approaches to optical flow computation have accommodated violations of brightness constancy with the use of robust statistics or with generalized brightness constancy constraints that allow generic types of contrast and illumination changes. Here, we consider realistic models of brightness variation that have time-dependent physical causes. We simultaneously estimate the optical flow and the relevant physical parameters, such as the heat diffusion and decay constants. The estimation problem is formulated for a wide class of physical models using total least squares (TLS), with confidence bounds on the parameters.
{"title":"Simultaneous estimation of optical flow and heat transport in infrared image sequences","authors":"H. Haussecker","doi":"10.1109/CVBVS.2000.855253","DOIUrl":"https://doi.org/10.1109/CVBVS.2000.855253","url":null,"abstract":"This paper incorporates physical models of heat transport into motion analysis in infrared image sequences. Physical transport processes, such as heat diffusion and decay, are causing time dependent brightness variations, violating the common brightness constancy assumption. Previous approaches to optical flow computation have accommodated violations of brightness constancy with the use of robust statistics or with generalized brightness constancy constraints that allow generic types of contrast and illumination changes. Here, we consider realistic models of brightness variation that have time-dependent physical causes. We simultaneously estimate the optical flow and the relevant physical parameters, such as the heat diffusion and decay constants. The estimation problem is formulated for a wide class of physical models using total least squares (TLS), with confidence bounds on the parameters.","PeriodicalId":231063,"journal":{"name":"Proceedings IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications (Cat. No.PR00640)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128043490","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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