Ernst Dieter Dickmanns

Dynamic Vision for Perception and Control of Motion (eBook, PDF)

• Format: PDF

Produktbeschreibung

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. This volume 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. It includes mission performance reports of the first vehicles using these innovative techniques. In addition, it provides pointers to the future development and likely applications of this hugely important field of research.

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Produktdetails

• Verlag: Springer London
• Seitenzahl: 474
• Erscheinungstermin: 2. Juni 2007
• Englisch
• ISBN-13: 9781846286384
• Artikelnr.: 37350958

Autorenporträt

From the reviewers: "Prof. Dickmanns is THE most authoritative person in the field; there is only one Dickmanns", Stephano Soatto, UCLA.

"He is one of the world's leading experts in autonomous road-following vehicles, he has conducted seminal research that is 2nd to none. To win such an author is very prestiguous for Springer", Ulrich Zehmzow, Univ. Essex.

Professor Dr.-Ing. Ernst D. Dickmanns has been professor at the University of the Bundeswehr (FRG Army) at Munich for over 30 years. For the last two decades he and his research group have conducted the most influential work worldwide on automous vehicles, producing seminal work on perception systems for dynamic vision. He is considered to have shaped research on autonomous vehicles and to have defined the modern field in this area.

Inhaltsangabe

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.