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Computer Vision and Machine Learning with Rgb-D Sensors Hardback - 2014
by Ling Shao (Editor); Jungong Han (Editor); Pushmeet Kohli (Editor)
- Used
- Hardback
A$71.91
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Details
- Title Computer Vision and Machine Learning with Rgb-D Sensors
- Author Ling Shao (Editor); Jungong Han (Editor); Pushmeet Kohli (Editor)
- Binding Hardback
- Condition New
- Pages 316
- Volumes 1
- Language ENG
- Publisher Springer
- Publication date 2014
- Features Maps
- Bookseller's Inventory # G3319086502I2N00
- ISBN 9783319086507 / 3319086502
- Weight 1.4 lbs (0.64 kg)
- Dimensions 9.21 x 6.14 x 0.75 in (23.39 x 15.60 x 1.91 cm)
-
Themes
- Aspects (Academic): Science/Technology Aspects
- Category Computers - Other Applications
- Dewey Decimal Code 005.437
- Quantity available 1
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From the publisher
From the rear cover
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
