Description: On Hierarchical Models for Visual Recognition and Learning of Objects, Scenes, and Activities Please note: this item is printed on demand and will take extra time before it can be dispatched to you (up to 20 working days). Author(s): Jens Spehr Format: Paperback Publisher: Springer International Publishing AG, Switzerland Imprint: Springer International Publishing AG ISBN-13: 9783319358628, 978-3319358628 Synopsis 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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Book Title: On Hierarchical Models for Visual Recognition and Learning of ...
Item Height: 235 mm
Item Width: 155 mm
Series: Studies in Systems, Decision and Control
Author: Jens Spehr
Publication Name: On Hierarchical Models for Visual Recognition and Learning of Objects, Scenes, and Activities
Format: Paperback
Language: English
Publisher: Springer International Publishing A&G
Subject: Engineering & Technology, Computer Science
Publication Year: 2016
Type: Textbook
Item Weight: 3343 g
Number of Pages: 199 Pages
