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Computer Vision and Machine Learning with Rgb-D Sensors 2014 Edition
Contributor(s): Shao, Ling (Editor), Han, Jungong (Editor), Kohli, Pushmeet (Editor)
ISBN: 3319086502     ISBN-13: 9783319086507
Publisher: Springer
OUR PRICE:   $52.24  
Product Type: Hardcover - Other Formats
Published: August 2014
Qty:
Additional Information
BISAC Categories:
- Computers | Computer Graphics
- Computers | User Interfaces
- Computers | Human-computer Interaction (hci)
Dewey: 005.437
Series: Advances in Computer Vision and Pattern Recognition
Physical Information: 0.75" H x 6.14" W x 9.21" (1.40 lbs) 316 pages
 
Descriptions, Reviews, Etc.
Publisher Description:

The combination of high-resolution visual and depth sensing, supported by machine learning, opens up new opportunities to solve real-world problems in computer vision.

This authoritative text/reference presents an interdisciplinary selection of important, cutting-edge research on RGB-D based computer vision. Divided into four sections, the book opens with a detailed survey of the field, followed by a focused examination of RGB-D based 3D reconstruction, mapping and synthesis. The work continues with a section devoted to novel techniques that employ depth data for object detection, segmentation and tracking, and concludes with examples of accurate human action interpretation aided by depth sensors.

Topics and features: discusses the calibration of color and depth cameras, the reduction of noise on depth maps, and methods for capturing human performance in 3D; reviews a selection of applications which use RGB-D information to reconstruct human figures, evaluate energy consumption, and obtain accurate action classification; presents an innovative approach for 3D object retrieval, and for the reconstruction of gas flow from multiple Kinect cameras; describes an RGB-D computer vision system designed to assist the visually impaired, and another for smart-environment sensing to assist elderly and disabled people; examines the effective features that characterize static hand poses, and introduces a unified framework to enforce both temporal and spatial constraints for hand parsing; proposes a new classifier architecture for real-time hand pose recognition, and a novel hand segmentation and gesture recognition system.

Researchers and practitioners working in computer vision, HCI and machine learning will find this to be a must-read text. The book also serves as a useful reference for graduate students studying computer vision, pattern recognition or multimedia.