The Experts below are selected from a list of 12084 Experts worldwide ranked by ideXlab platform
J.c. Cockburn - One of the best experts on this subject based on the ideXlab platform.
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A Modular Architecture for real-time feature-based tracking
2004 IEEE International Conference on Acoustics Speech and Signal Processing, 2004Co-Authors: B. Castaneda, Y. Luzanov, J.c. CockburnAbstract:A Modular Architecture for real-time feature-based tracking is presented. This Architecture takes advantage of temporal and spatial information contained in a video stream, combining robust classifiers with motion estimation to achieve real-time performance. The relationship among features is exploited to obtain a robust detection and a stable tracking. The effectiveness of this Architecture is demonstrated in a face tracking system using eyes and lips as features. A pre-processing stage based on skin color segmentation, density maps and low intensity characteristics of facial features reduces the number of image regions that are candidates for eyes and lips. Support vector machines are then used in the classification process, whereas a combination of Kalman filters and template matching is used for tracking.
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ICASSP (5) - A Modular Architecture for real-time feature-based tracking
2004 IEEE International Conference on Acoustics Speech and Signal Processing, 2004Co-Authors: B. Castaneda, Y. Luzanov, J.c. CockburnAbstract:A Modular Architecture for real-time feature-based tracking is presented. This Architecture takes advantage of temporal and spatial information contained in a video stream, combining robust classifiers with motion estimation to achieve real-time performance. The relationship among features is exploited to obtain a robust detection and a stable tracking. The effectiveness of this Architecture is demonstrated in a face tracking system using eyes and lips as features. A pre-processing stage based on skin color segmentation, density maps and low intensity characteristics of facial features reduces the number of image regions that are candidates for eyes and lips. Support vector machines are then used in the classification process, whereas a combination of Kalman filters and template matching is used for tracking.
B. Castaneda - One of the best experts on this subject based on the ideXlab platform.
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A Modular Architecture for real-time feature-based tracking
2004 IEEE International Conference on Acoustics Speech and Signal Processing, 2004Co-Authors: B. Castaneda, Y. Luzanov, J.c. CockburnAbstract:A Modular Architecture for real-time feature-based tracking is presented. This Architecture takes advantage of temporal and spatial information contained in a video stream, combining robust classifiers with motion estimation to achieve real-time performance. The relationship among features is exploited to obtain a robust detection and a stable tracking. The effectiveness of this Architecture is demonstrated in a face tracking system using eyes and lips as features. A pre-processing stage based on skin color segmentation, density maps and low intensity characteristics of facial features reduces the number of image regions that are candidates for eyes and lips. Support vector machines are then used in the classification process, whereas a combination of Kalman filters and template matching is used for tracking.
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ICASSP (5) - A Modular Architecture for real-time feature-based tracking
2004 IEEE International Conference on Acoustics Speech and Signal Processing, 2004Co-Authors: B. Castaneda, Y. Luzanov, J.c. CockburnAbstract:A Modular Architecture for real-time feature-based tracking is presented. This Architecture takes advantage of temporal and spatial information contained in a video stream, combining robust classifiers with motion estimation to achieve real-time performance. The relationship among features is exploited to obtain a robust detection and a stable tracking. The effectiveness of this Architecture is demonstrated in a face tracking system using eyes and lips as features. A pre-processing stage based on skin color segmentation, density maps and low intensity characteristics of facial features reduces the number of image regions that are candidates for eyes and lips. Support vector machines are then used in the classification process, whereas a combination of Kalman filters and template matching is used for tracking.
Gideon Schreiber - One of the best experts on this subject based on the ideXlab platform.
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the Modular Architecture of protein protein binding interfaces
Proceedings of the National Academy of Sciences of the United States of America, 2005Co-Authors: Dana Reichmann, Ofer Rahat, Shira Albeck, Ran Meged, Gideon SchreiberAbstract:Protein–protein interactions are essential for life. Yet, our understanding of the general principles governing binding is not complete. In the present study, we show that the interface between proteins is built in a Modular fashion; each module is comprised of a number of closely interacting residues, with few interactions between the modules. The boundaries between modules are defined by clustering the contact map of the interface. We show that mutations in one module do not affect residues located in a neighboring module. As a result, the structural and energetic consequences of the deletion of entire modules are surprisingly small. To the contrary, within their module, mutations cause complex energetic and structural consequences. Experimentally, this phenomenon is shown on the interaction between TEM1-βlactamase and β-lactamase inhibitor protein (BLIP) by using multiple-mutant analysis and x-ray crystallography. Replacing an entire module of five interface residues with Ala created a large cavity in the interface, with no effect on the detailed structure of the remaining interface. The Modular Architecture of binding sites, which resembles human engineering design, greatly simplifies the design of new protein interactions and provides a feasible view of how these interactions evolved.
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The Modular Architecture of protein–protein binding interfaces
Proceedings of the National Academy of Sciences of the United States of America, 2004Co-Authors: Dana Reichmann, Ofer Rahat, Shira Albeck, Ran Meged, Gideon SchreiberAbstract:Protein–protein interactions are essential for life. Yet, our understanding of the general principles governing binding is not complete. In the present study, we show that the interface between proteins is built in a Modular fashion; each module is comprised of a number of closely interacting residues, with few interactions between the modules. The boundaries between modules are defined by clustering the contact map of the interface. We show that mutations in one module do not affect residues located in a neighboring module. As a result, the structural and energetic consequences of the deletion of entire modules are surprisingly small. To the contrary, within their module, mutations cause complex energetic and structural consequences. Experimentally, this phenomenon is shown on the interaction between TEM1-βlactamase and β-lactamase inhibitor protein (BLIP) by using multiple-mutant analysis and x-ray crystallography. Replacing an entire module of five interface residues with Ala created a large cavity in the interface, with no effect on the detailed structure of the remaining interface. The Modular Architecture of binding sites, which resembles human engineering design, greatly simplifies the design of new protein interactions and provides a feasible view of how these interactions evolved.
Guoqian Jiang - One of the best experts on this subject based on the ideXlab platform.
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Developing a Modular Architecture for creation of rule-based clinical diagnostic criteria
Biodata Mining, 2016Co-Authors: Na Hong, Jyotishman Pathak, Christopher G Chute, Guoqian JiangAbstract:Background With recent advances in computerized patient records system, there is an urgent need for producing computable and standards-based clinical diagnostic criteria. Notably, constructing rule-based clinical diagnosis criteria has become one of the goals in the International Classification of Diseases (ICD)-11 revision. However, few studies have been done in building a unified Architecture to support the need for diagnostic criteria computerization. In this study, we present a Modular Architecture for enabling the creation of rule-based clinical diagnostic criteria leveraging Semantic Web technologies.
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Developing a Modular Architecture for creation of rule-based clinical diagnostic criteria
BioData Mining, 2016Co-Authors: Na Hong, Jyotishman Pathak, Christopher G Chute, Guoqian JiangAbstract:Background: With recent advances in computerized patient records system, there is an urgent need for producing computable and standards-based clinical diagnostic criteria. Notably, constructing rule-based clinical diagnosis criteria has become one of the goals in the International Classification of Diseases (ICD)-11 revision. However, few studies have been done in building a unified Architecture to support the need for diagnostic criteria computerization. In this study, we present a Modular Architecture for enabling the creation of rule-based clinical diagnostic criteria leveraging Semantic Web technologies. Methods and results: The Architecture consists of two modules: an authoring module that utilizes a standards-based information model and a translation module that leverages Semantic Web Rule Language (SWRL). In a prototype implementation, we created a diagnostic criteria upper ontology (DCUO) that integrates ICD-11 content model with the Quality Data Model (QDM). Using the DCUO, we developed a transformation tool that converts QDM-based diagnostic criteria into Semantic Web Rule Language (SWRL) representation. We evaluated the domain coverage of the upper ontology model using randomly selected diagnostic criteria from broad domains (n = 20). We also tested the transformation algorithms using 6 QDM templates for ontology population and 15 QDM-based criteria data for rule generation. As the results, the first draft of DCUO contains 14 root classes, 21 subclasses, 6 object properties and 1 data property. Investigation Findings, and Signs and Symptoms are the two most commonly used element types. All 6 HQMF templates are successfully parsed and populated into their corresponding domain specific ontologies and 14 rules (93.3 %) passed the rule validation. Conclusion: Our efforts in developing and prototyping a Modular Architecture provide useful insight into how to build a scalable solution to support diagnostic criteria representation and computerization. Copyright © 2016 The Author(s).
Y. Luzanov - One of the best experts on this subject based on the ideXlab platform.
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A Modular Architecture for real-time feature-based tracking
2004 IEEE International Conference on Acoustics Speech and Signal Processing, 2004Co-Authors: B. Castaneda, Y. Luzanov, J.c. CockburnAbstract:A Modular Architecture for real-time feature-based tracking is presented. This Architecture takes advantage of temporal and spatial information contained in a video stream, combining robust classifiers with motion estimation to achieve real-time performance. The relationship among features is exploited to obtain a robust detection and a stable tracking. The effectiveness of this Architecture is demonstrated in a face tracking system using eyes and lips as features. A pre-processing stage based on skin color segmentation, density maps and low intensity characteristics of facial features reduces the number of image regions that are candidates for eyes and lips. Support vector machines are then used in the classification process, whereas a combination of Kalman filters and template matching is used for tracking.
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ICASSP (5) - A Modular Architecture for real-time feature-based tracking
2004 IEEE International Conference on Acoustics Speech and Signal Processing, 2004Co-Authors: B. Castaneda, Y. Luzanov, J.c. CockburnAbstract:A Modular Architecture for real-time feature-based tracking is presented. This Architecture takes advantage of temporal and spatial information contained in a video stream, combining robust classifiers with motion estimation to achieve real-time performance. The relationship among features is exploited to obtain a robust detection and a stable tracking. The effectiveness of this Architecture is demonstrated in a face tracking system using eyes and lips as features. A pre-processing stage based on skin color segmentation, density maps and low intensity characteristics of facial features reduces the number of image regions that are candidates for eyes and lips. Support vector machines are then used in the classification process, whereas a combination of Kalman filters and template matching is used for tracking.
