Abstract
The visual recognition and learning of objects, scenes, and activities is a highly relevant but simultaneously very difficult task, which has applications in many areas such as robotics, industry, consumer electronics, aerospace, transportation systems, or ambient assistant living. Just a decade ago, computer vision applications were mainly limited to machine vision, where the actual learning and recognition task were strongly restricted due to simplified environment conditions. But the more powerful new hardware becomes, the more are new approaches able to integrate uncertainties as well as dependencies between objects and a scene, allowing them to act in real world scenarios. Even approaches already proposed in the past, which were up to now unthinkable to calculate in real-time, are more and more revived.
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© 2015 Springer International Publishing Switzerland
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Spehr, J. (2015). Introduction. In: On Hierarchical Models for Visual Recognition and Learning of Objects, Scenes, and Activities. Studies in Systems, Decision and Control, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-319-11325-8_1
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DOI: https://doi.org/10.1007/978-3-319-11325-8_1
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11324-1
Online ISBN: 978-3-319-11325-8
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