The Experts below are selected from a list of 43479 Experts worldwide ranked by ideXlab platform
Vikram Pakrashi - One of the best experts on this subject based on the ideXlab platform.
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regionally enhanced multiphase segmentation technique for damaged surfaces
Computer-aided Civil and Infrastructure Engineering, 2014Co-Authors: Michael Obyrne, Bidisha Ghosh, Franck Schoefs, Vikram PakrashiAbstract:Imaging-based damage detection techniques are increasingly being utilized alongside traditional visual inspection methods to provide owners/operators of Infrastructure with an efficient source of quantitative information for ensuring their continued safe and economic operation. However, there exists scope for significant development of improved damage detection algorithms that can characterize features of interest in challenging scenes with credibility. This article presents a new regionally enhanced multiphase segmentation (REMPS) technique that is designed to detect a broad range of damage forms on the surface of civil Infrastructure. The technique is successfully applied to a corroding Infrastructure Component in a harbour facility. REMPS integrates spatial and pixel relationships to identify, classify, and quantify the area of damaged regions to a high degree of accuracy. The image of interest is preprocessed through a contrast enhancement and color reduction scheme. Features in the image are then identified using a Sobel edge detector, followed by subsequent classification using a clustering-based filtering technique. Finally, support vector machines are used to classify pixels which are locally supplemented onto damaged regions to improve their size and shape characteristics. The performance of REMPS in different color spaces is investigated for best detection on the basis of receiver operating characteristics curves. The superiority of REMPS over existing segmentation approaches is demonstrated, in particular when considering high dynamic range imagery. It is shown that REMPS easily extends beyond the application presented and may be considered an effective and versatile standalone segmentation technique.
Michael Obyrne - One of the best experts on this subject based on the ideXlab platform.
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regionally enhanced multiphase segmentation technique for damaged surfaces
Computer-aided Civil and Infrastructure Engineering, 2014Co-Authors: Michael Obyrne, Bidisha Ghosh, Franck Schoefs, Vikram PakrashiAbstract:Imaging-based damage detection techniques are increasingly being utilized alongside traditional visual inspection methods to provide owners/operators of Infrastructure with an efficient source of quantitative information for ensuring their continued safe and economic operation. However, there exists scope for significant development of improved damage detection algorithms that can characterize features of interest in challenging scenes with credibility. This article presents a new regionally enhanced multiphase segmentation (REMPS) technique that is designed to detect a broad range of damage forms on the surface of civil Infrastructure. The technique is successfully applied to a corroding Infrastructure Component in a harbour facility. REMPS integrates spatial and pixel relationships to identify, classify, and quantify the area of damaged regions to a high degree of accuracy. The image of interest is preprocessed through a contrast enhancement and color reduction scheme. Features in the image are then identified using a Sobel edge detector, followed by subsequent classification using a clustering-based filtering technique. Finally, support vector machines are used to classify pixels which are locally supplemented onto damaged regions to improve their size and shape characteristics. The performance of REMPS in different color spaces is investigated for best detection on the basis of receiver operating characteristics curves. The superiority of REMPS over existing segmentation approaches is demonstrated, in particular when considering high dynamic range imagery. It is shown that REMPS easily extends beyond the application presented and may be considered an effective and versatile standalone segmentation technique.
Emin Gun Sirer - One of the best experts on this subject based on the ideXlab platform.
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weaver a high performance transactional graph database based on refinable timestamps
Very Large Data Bases, 2016Co-Authors: Ayush Dubey, Greg D Hill, Robert Escriva, Emin Gun SirerAbstract:Graph databases have become a common Infrastructure Component. Yet existing systems either operate on offline snapshots, provide weak consistency guarantees, or use expensive concurrency control techniques that limit performance. In this paper, we introduce a new distributed graph database, called Weaver, which enables efficient, transactional graph analyses as well as strictly serializable ACID transactions on dynamic graphs. The key insight that allows Weaver to combine strict serializability with horizontal scalability and high performance is a novel request ordering mechanism called refinable timestamps. This technique couples coarse-grained vector timestamps with a fine-grained timeline oracle to pay the overhead of strong consistency only when needed. Experiments show that Weaver enables a Bitcoin blockchain explorer that is 8x faster than Blockchain.info, and achieves 10.9x higher throughput than the Titan graph database on social network workloads and 4x lower latency than GraphLab on offline graph traversal workloads.
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weaver a high performance transactional graph database based on refinable timestamps
arXiv: Distributed Parallel and Cluster Computing, 2015Co-Authors: Ayush Dubey, Greg D Hill, Robert Escriva, Emin Gun SirerAbstract:Graph databases have become an increasingly common Infrastructure Component. Yet existing systems either operate on offline snapshots, provide weak consistency guarantees, or use expensive concurrency control techniques that limit performance. In this paper, we introduce a new distributed graph database, called Weaver, which enables efficient, transactional graph analyses as well as strictly serializable ACID transactions on dynamic graphs. The key insight that allows Weaver to combine strict serializability with horizontal scalability and high performance is a novel request ordering mechanism called refinable timestamps. This technique couples coarse-grained vector timestamps with a fine-grained timeline oracle to pay the overhead of strong consistency only when needed. Experiments show that Weaver enables a Bitcoin blockchain explorer that is 8x faster than Blockchain.info, and achieves 12x higher throughput than the Titan graph database on social network workloads and 4x lower latency than GraphLab on offline graph traversal workloads.
Franck Schoefs - One of the best experts on this subject based on the ideXlab platform.
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regionally enhanced multiphase segmentation technique for damaged surfaces
Computer-aided Civil and Infrastructure Engineering, 2014Co-Authors: Michael Obyrne, Bidisha Ghosh, Franck Schoefs, Vikram PakrashiAbstract:Imaging-based damage detection techniques are increasingly being utilized alongside traditional visual inspection methods to provide owners/operators of Infrastructure with an efficient source of quantitative information for ensuring their continued safe and economic operation. However, there exists scope for significant development of improved damage detection algorithms that can characterize features of interest in challenging scenes with credibility. This article presents a new regionally enhanced multiphase segmentation (REMPS) technique that is designed to detect a broad range of damage forms on the surface of civil Infrastructure. The technique is successfully applied to a corroding Infrastructure Component in a harbour facility. REMPS integrates spatial and pixel relationships to identify, classify, and quantify the area of damaged regions to a high degree of accuracy. The image of interest is preprocessed through a contrast enhancement and color reduction scheme. Features in the image are then identified using a Sobel edge detector, followed by subsequent classification using a clustering-based filtering technique. Finally, support vector machines are used to classify pixels which are locally supplemented onto damaged regions to improve their size and shape characteristics. The performance of REMPS in different color spaces is investigated for best detection on the basis of receiver operating characteristics curves. The superiority of REMPS over existing segmentation approaches is demonstrated, in particular when considering high dynamic range imagery. It is shown that REMPS easily extends beyond the application presented and may be considered an effective and versatile standalone segmentation technique.
Bidisha Ghosh - One of the best experts on this subject based on the ideXlab platform.
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regionally enhanced multiphase segmentation technique for damaged surfaces
Computer-aided Civil and Infrastructure Engineering, 2014Co-Authors: Michael Obyrne, Bidisha Ghosh, Franck Schoefs, Vikram PakrashiAbstract:Imaging-based damage detection techniques are increasingly being utilized alongside traditional visual inspection methods to provide owners/operators of Infrastructure with an efficient source of quantitative information for ensuring their continued safe and economic operation. However, there exists scope for significant development of improved damage detection algorithms that can characterize features of interest in challenging scenes with credibility. This article presents a new regionally enhanced multiphase segmentation (REMPS) technique that is designed to detect a broad range of damage forms on the surface of civil Infrastructure. The technique is successfully applied to a corroding Infrastructure Component in a harbour facility. REMPS integrates spatial and pixel relationships to identify, classify, and quantify the area of damaged regions to a high degree of accuracy. The image of interest is preprocessed through a contrast enhancement and color reduction scheme. Features in the image are then identified using a Sobel edge detector, followed by subsequent classification using a clustering-based filtering technique. Finally, support vector machines are used to classify pixels which are locally supplemented onto damaged regions to improve their size and shape characteristics. The performance of REMPS in different color spaces is investigated for best detection on the basis of receiver operating characteristics curves. The superiority of REMPS over existing segmentation approaches is demonstrated, in particular when considering high dynamic range imagery. It is shown that REMPS easily extends beyond the application presented and may be considered an effective and versatile standalone segmentation technique.