Dynamic Vision for Perception and Control of Motion [electronic resource] / by Ernst D. Dickmanns.

By: Dickmanns, Ernst D [author.]Contributor(s): SpringerLink (Online service)Material type: TextTextLanguage: English Publisher: London : Springer London, 2007Description: XVII, 474 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9781846286384Subject(s): Engineering | Artificial intelligence | Computer vision | Engineering | Control Engineering | Automotive and Aerospace Engineering, Traffic | Automation and Robotics | Signal, Image and Speech Processing | Artificial Intelligence (incl. Robotics) | Image Processing and Computer VisionAdditional physical formats: Printed edition:: No titleOnline resources: Click here to access online
Contents:
Basic Relations: Image Sequences — “the World” -- Subjects and Subject Classes -- Application Domains, Missions, and Situations -- Extraction of Visual Features -- Recursive State Estimation -- Beginnings of Spatiotemporal Road and Ego-state Recognition -- Initialization in Dynamic Scene Understanding -- Recursive Estimation of Road Parameters and Ego State while Cruising -- Perception of Crossroads -- Perception of Obstacles and Vehicles -- Sensor Requirements for Road Scenes -- Integrated Knowledge Representations for Dynamic Vision -- Mission Performance, Experimental Results -- Conclusions and Outlook.
In: Springer eBooksSummary: The application of machine vision to autonomous vehicles is an increasingly important area of research with exciting applications in industry, defense, and transportation likely in coming decades. Dynamic Vision for Perception and Control of Motion has been written by the world's leading expert on autonomous road-following vehicles and brings together twenty years of innovation in the field by Professor Dickmanns and his colleagues at the University of the German Federal Armed Forces in Munich. The book uniquely details an approach to real-time machine vision for the understanding of dynamic scenes, viewed from a moving platform that begins with spatio-temporal representations of motion for hypothesized objects whose parameters are adjusted by well-known prediction error feedback and recursive estimation techniques. A coherent and up-to-date coverage of the subject matter is presented, with the machine vision and control aspects detailed, along with reports on the mission performance of the first vehicles using these innovative techniques built at Munich. Pointers to the future development and likely applications of this hugely important field of research are presented. Dynamic Vision for Perception and Control of Motion will be a key reference for technologists working in autonomous vehicles and mobile robotics in general who wish to access the leading research in this field, as well as researchers and students working in machine vision and dynamic control interested in one of the most interesting and promising applications of these techniques.
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Basic Relations: Image Sequences — “the World” -- Subjects and Subject Classes -- Application Domains, Missions, and Situations -- Extraction of Visual Features -- Recursive State Estimation -- Beginnings of Spatiotemporal Road and Ego-state Recognition -- Initialization in Dynamic Scene Understanding -- Recursive Estimation of Road Parameters and Ego State while Cruising -- Perception of Crossroads -- Perception of Obstacles and Vehicles -- Sensor Requirements for Road Scenes -- Integrated Knowledge Representations for Dynamic Vision -- Mission Performance, Experimental Results -- Conclusions and Outlook.

The application of machine vision to autonomous vehicles is an increasingly important area of research with exciting applications in industry, defense, and transportation likely in coming decades. Dynamic Vision for Perception and Control of Motion has been written by the world's leading expert on autonomous road-following vehicles and brings together twenty years of innovation in the field by Professor Dickmanns and his colleagues at the University of the German Federal Armed Forces in Munich. The book uniquely details an approach to real-time machine vision for the understanding of dynamic scenes, viewed from a moving platform that begins with spatio-temporal representations of motion for hypothesized objects whose parameters are adjusted by well-known prediction error feedback and recursive estimation techniques. A coherent and up-to-date coverage of the subject matter is presented, with the machine vision and control aspects detailed, along with reports on the mission performance of the first vehicles using these innovative techniques built at Munich. Pointers to the future development and likely applications of this hugely important field of research are presented. Dynamic Vision for Perception and Control of Motion will be a key reference for technologists working in autonomous vehicles and mobile robotics in general who wish to access the leading research in this field, as well as researchers and students working in machine vision and dynamic control interested in one of the most interesting and promising applications of these techniques.

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