Unifying Perspectives in Computational and Robot Vision
Unifying Perspectives in Computational and Robot Vision by Danica Kragic, published by Springer US on November 29, 2010, is a softcover reprint of the hardcover first edition from 2008, comprising 206 pages in English. This book addresses the existing gap between the research conducted in the fields of computer vision and robotics, highlighting the common characteristics and interests that both domains share. It discusses the significant theoretical and methodological advancements in computer vision over the past decade, which many robotics researchers may not be fully aware of, and emphasizes the importance of integrating vision as a component of a holistic system in robotics.
Readers will find a collection of state-of-the-art methods that leverage computer vision and machine learning techniques applied to robotic applications. The text explores the necessity of considering the entire perception-action loop in robotics, illustrating how vision research must be intertwined with system control for effective outcomes. This edition serves as a resource for those interested in the intersection of technology, artificial intelligence, and automation, providing insights into how advancements in computer vision can enhance robotic capabilities.
Official synopsis Publisher
Currently there is a gap between the research conducted in computer vision and robotics communities. There are many characteristics in common in computer vision research and vision research in robotics. Despite having these common interests, however, “pure” computer vision has seen significant theoretical and methodological advances during the last decade which many of the robotics researchers are not fully aware of. On the other hand, the manipulation and control capabilities of robots as well as the range of application areas have developed greatly. In robotics, vision can not be considered an isolated component, but it is instead a part of a system resulting in an action. Thus, in robotics the vision research must include consideration of the control of the system, in other words, the entire perception-action loop. This requires that a holistic system approach is useful and could provide significant advances in this application domain. Assembled here is a collection of some of the state of the art methods that are using computer vision and machine learning techniques and apply them in robotic applications.
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