Chatterjee sromona (15 risultati)
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Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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Da: California Books, Miami, FL, U.S.A.California Books
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EUR 57,81
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Da: Ria Christie Collections, Uxbridge, Regno UnitoRia Christie Collections
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EUR 48,92
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Da: Chiron Media, Wallingford, Regno UnitoChiron Media
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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EUR 47,70
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
Contatta il venditoreVenditore con 5 stelleCondizione: Usato - Come nuovo
EUR 53,45
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Condizione: As New. Unread book in perfect condition.
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Da: Buchpark, Trebbin, GermaniaBuchpark
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EUR 54,70
EUR 105,00 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Condizione: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | Road maintenance has traditionally been a time consuming, expensive, and manual process. Timely maintenance of roads helps in lowering rehabilitation costs, accidents, environmental pollution, while facilitating increased connectivity, tra…de, and growth. Easily acquirable front-view scene images are seen to be used lately for infrastructure management and road maintenance as they provide quicker, low-cost, and flexible solutions. Such scene images can easily be acquired using standard commodity cameras. In this dissertation, machine learning based approaches have been developed to analyze front-view scene images for detecting cracks automatically on road surfaces across different locations and under various conditions. This work thus contributes toward automated approaches to detect different kinds of cracks on road surfaces, thereby proposing a low-cost solution to road maintenance practices. As a result, different components are developed in this work which are sketched together to form a Decision Support System for the task of crack detection. In this study primarily three algorithmic approaches have been developed. Firstly, an unsupervised graph-based hierarchical clustering technique for road area segmentation has been developed, thus helping in detecting the road area in scene images. Secondly, a classifier and superpixel based supervised learning approach consisting of systematically identifying relevant features for detecting superpixels containing cracks has been developed. Thirdly, an unsupervised learning approach consisting of Gamma Mixture Fuzzy Model based clustering technique and keypoint matching mechanisms have been designed in this work for detecting which road pixels are crack pixels in images. Finally, this study integrates the findings and approaches to propose a Decision Support System for crack detection on road surfaces of easily acquirable front-view scene images. Evaluations performed on an experimentally collected diverse front-view scene image dataset show promising results for crack detection using the developed approaches in this work.
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- Print on Demand
Da: PBShop.store US, Wood Dale, IL, U.S.A.PBShop.store US
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EUR 51,16
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PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Da: PBShop.store UK, Fairford, GLOS, Regno UnitoPBShop.store UK
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EUR 49,37
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PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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EUR 59,88
EUR 23,00 spedizioneSpedito da Germania a U.S.A.Quantità: 2 disponibili
Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Road maintenance has traditionally been a time consuming, expensive, and manual process. Timely maintenance of roads helps in lowering rehabilitation costs, accidents, environmental pollution, while facilitating increased connectiv…ity, trade, and growth. Easily acquirable front-view scene images are seen to be used lately for infrastructure management and road maintenance as they provide quicker, low-cost, and flexible solutions. Such scene images can easily be acquired using standard commodity cameras. In this dissertation, machine learning based approaches have been developed to analyze front-view scene images for detecting cracks automatically on road surfaces across different locations and under various conditions. This work thus contributes toward automated approaches to detect different kinds of cracks on road surfaces, thereby proposing a low-cost solution to road maintenance practices. As a result, different components are developed in this work which are sketched together to form a Decision Support System for the task of crack detection. In this study primarily three algorithmic approaches have been developed. Firstly, an unsupervised graph-based hierarchical clustering technique for road area segmentation has been developed, thus helping in detecting the road area in scene images. Secondly, a classifier and superpixel based supervised learning approach consisting of systematically identifying relevant features for detecting superpixels containing cracks has been developed. Thirdly, an unsupervised learning approach consisting of Gamma Mixture Fuzzy Model based clustering technique and keypoint matching mechanisms have been designed in this work for detecting which road pixels are crack pixels in images. Finally, this study integrates the findings and approaches to propose a Decision Support System for crack detection on road surfaces of easily acquirable front-view scene images. Evaluations performed on an experimentally collected diverse front-view scene image dataset show promising results for crack detection using the developed approaches in this work. 198 pp. Englisch.
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Da: moluna, Greven, Germaniamoluna
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EUR 59,88
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. KlappentextrnrnRoad maintenance has traditionally been a time consuming, expensive, and manual process. Timely maintenance of roads helps in lowering rehabilitation costs, accidents, environmental pollution, while fa…cilitating increased connecti.
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 59,88
EUR 60,00 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Road maintenance has traditionally been a time consuming, expensive, and manual process. Timely maintenance of roads helps in lowering rehabilitation costs, accidents, environmental pollution, while facilitating increased connectivity,… trade, and growth. Easily acquirable front-view scene images are seen to be used lately for infrastructure management and road maintenance as they provide quicker, low-cost, and flexible solutions. Such scene images can easily be acquired using standard commodity cameras. In this dissertation, machine learning based approaches have been developed to analyze front-view scene images for detecting cracks automatically on road surfaces across different locations and under various conditions. This work thus contributes toward automated approaches to detect different kinds of cracks on road surfaces, thereby proposing a low-cost solution to road maintenance practices. As a result, different components are developed in this work which are sketched together to form a Decision Support System for the task of crack detection. In this study primarily three algorithmic approaches have been developed. Firstly, an unsupervised graph-based hierarchical clustering technique for road area segmentation has been developed, thus helping in detecting the road area in scene images. Secondly, a classifier and superpixel based supervised learning approach consisting of systematically identifying relevant features for detecting superpixels containing cracks has been developed. Thirdly, an unsupervised learning approach consisting of Gamma Mixture Fuzzy Model based clustering technique and keypoint matching mechanisms have been designed in this work for detecting which road pixels are crack pixels in images. Finally, this study integrates the findings and approaches to propose a Decision Support System for crack detection on road surfaces of easily acquirable front-view scene images. Evaluations performed on an experimentally collected diverse front-view scene image dataset show promising results for crack detection using the developed approaches in this work. 198 pp. Englisch.
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 59,88
EUR 61,32 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Road maintenance has traditionally been a time consuming, expensive, and manual process. Timely maintenance of roads helps in lowering rehabilitation costs, accidents, environmental pollution, while facilitating increased connectivity,…trade, and growth. Easily acquirable front-view scene images are seen to be used lately for infrastructure management and road maintenance as they provide quicker, low-cost, and flexible solutions. Such scene images can easily be acquired using standard commodity cameras. In this dissertation, machine learning based approaches have been developed to analyze front-view scene images for detecting cracks automatically on road surfaces across different locations and under various conditions. This work thus contributes toward automated approaches to detect different kinds of cracks on road surfaces, thereby proposing a low-cost solution to road maintenance practices. As a result, different components are developed in this work which are sketched together to form a Decision Support System for the task of crack detection. In this study primarily three algorithmic approaches have been developed. Firstly, an unsupervised graph-based hierarchical clustering technique for road area segmentation has been developed, thus helping in detecting the road area in scene images. Secondly, a classifier and superpixel based supervised learning approach consisting of systematically identifying relevant features for detecting superpixels containing cracks has been developed. Thirdly, an unsupervised learning approach consisting of Gamma Mixture Fuzzy Model based clustering technique and keypoint matching mechanisms have been designed in this work for detecting which road pixels are crack pixels in images. Finally, this study integrates the findings and approaches to propose a Decision Support System for crack detection on road surfaces of easily acquirable front-view scene images. Evaluations performed on an experimentally collected diverse front-view scene image dataset show promising results for crack detection using the developed approaches in this work.
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Da: preigu, Osnabrück, Germaniapreigu
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 59,88
EUR 70,00 spedizioneSpedito da Germania a U.S.A.Quantità: 5 disponibili
Taschenbuch. Condizione: Neu. Computer Vision and Machine Learning in Sustainable Mobility: The Case of Road Surface Defects | Sromona Chatterjee | Taschenbuch | Göttinger Wirtschaftsinformatik | Kartoniert / Broschiert | Englisch | 2020 | Cuvillier | EAN 9783736972582 | Verantwortliche Person für die EU: Cuvillier Verlag, Nonne…nstieg 8, 37075 Göttingen, info[at]cuvillier[dot]de | Anbieter: preigu Print on Demand.
