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On Hierarchical Models for Visual Recognition and Learning of Objects, Scenes, and Activities (Hardcover, 2015 ed.)
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On Hierarchical Models for Visual Recognition and Learning of Objects, Scenes, and Activities (Hardcover, 2015 ed.)
Series: Studies in Systems, Decision and Control, 11
Expected to ship within 12 - 17 working days
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In many computer vision applications, objects have to be learned
and recognized in images or image sequences. This book presents new
probabilistic hierarchical models that allow an efficient
representation of multiple objects of different categories, scales,
rotations, and views. The idea is to exploit similarities between
objects and object parts in order to share calculations and avoid
redundant information. Furthermore inference approaches for fast
and robust detection are presented. These new approaches combine
the idea of compositional and similarity hierarchies and overcome
limitations of previous methods. Besides classical object
recognition the book shows the use for detection of human poses in
a project for gait analysis. The use of activity detection is
presented for the design of environments for ageing, to identify
activities and behavior patterns in smart homes. In a presented
project for parking spot detection using an intelligent vehicle,
the proposed approaches are used to hierarchically model the
environment of the vehicle for an efficient and robust
interpretation of the scene in real-time.
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