Infrastructure computer vision
Contributor(s): Brilakis, Ioannis [ed.] | Haas, Carl [ed.].
Publisher: Oxford Butterworth-Heinemann 2020Description: xvii, 389p.ISBN: 9780128155035.Subject(s): Computer visionDDC classification: 006.37 | In3 Summary: Infrastructure Computer Vision delves into this field of computer science that works on enabling computers to see, identify, process images and provide appropriate output in the same way that human vision does. However, implementing these advanced information and sensing technologies is difficult for many engineers. This book provides civil engineers with the technical detail of this advanced technology and how to apply it to their individual projects. Explains how to best capture raw geometrical and visual data from infrastructure scenes and assess their quality Offers valuable insights on how to convert the raw data into actionable information and knowledge stored in Digital Twins Bridges the gap between the theoretical aspects and real-life applications of computer visionItem type | Current location | Collection | Call number | Status | Date due | Barcode | Item holds |
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PK Kelkar Library, IIT Kanpur | General Stacks | 006.37 In3 (Browse shelf) | Available | A185222 |
Browsing PK Kelkar Library, IIT Kanpur Shelves , Collection code: General Stacks Close shelf browser
006.37 F98 Fusion in computer vision | 006.37 G346V VISIBILITY ALGORITHMS IN THE PLANE | 006.37 H999n Natural image statistics | 006.37 In3 Infrastructure computer vision | 006.37 In89 An invitation to 3-D vision | 006.37 K679c Concise computer vision | 006.37 L135o OpenCV 2 computer vision application programming cookbook |
Infrastructure Computer Vision delves into this field of computer science that works on enabling computers to see, identify, process images and provide appropriate output in the same way that human vision does. However, implementing these advanced information and sensing technologies is difficult for many engineers. This book provides civil engineers with the technical detail of this advanced technology and how to apply it to their individual projects.
Explains how to best capture raw geometrical and visual data from infrastructure scenes and assess their quality
Offers valuable insights on how to convert the raw data into actionable information and knowledge stored in Digital Twins
Bridges the gap between the theoretical aspects and real-life applications of computer vision
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