On Hierarchical Models for Visual Recognition and Learning of Objects, Scenes, and Activities (Paperback, Softcover reprint of the original 1st ed. 2015)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Studies in Systems, Decision and Control, 11
Release date:	October 2016
First published:	2015
Authors:	Jens Spehr
Dimensions:	235 x 155 x 12mm (L x W x T)
Format:	Paperback
Pages:	199
Edition:	Softcover reprint of the original 1st ed. 2015
ISBN-13:	978-3-319-35862-8
Categories:	Books > Computing & IT > Applications of computing > Pattern recognition Books > Computing & IT > Applications of computing > Artificial intelligence > General Books > Computing & IT > Applications of computing > Image processing > General Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Automatic control engineering > Robotics Promotions
LSN:	3-319-35862-6
Barcode:	9783319358628

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