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Computer Vision and Machine Learning with RGB-D Sensors

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Cover of 'Computer Vision and Machine Learning with RGB-D Sensors'

Table of Contents

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    Book Overview
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    Chapter 1 3D Depth Cameras in Vision: Benefits and Limitations of the Hardware
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    Chapter 2 A State of the Art Report on Multiple RGB-D Sensor Research and on Publicly Available RGB-D Datasets
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    Chapter 3 Calibration Between Depth and Color Sensors for Commodity Depth Cameras
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    Chapter 4 Depth Map Denoising via CDT-Based Joint Bilateral Filter
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    Chapter 5 Human Performance Capture Using Multiple Handheld Kinects
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    Chapter 6 Human-Centered 3D Home Applications via Low-Cost RGBD Cameras
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    Chapter 7 Matching of 3D Objects Based on 3D Curves
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    Chapter 8 Using Sparse Optical Flow for Two-Phase Gas Flow Capturing with Multiple Kinect
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    Chapter 9 RGB-D Sensor-Based Computer Vision Assistive Technology for Visually Impaired Persons
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    Chapter 10 RGB-D Human Identification and Tracking in a Smart Environment
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    Chapter 11 Feature Descriptors for Depth-Based Hand Gesture Recognition
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    Chapter 12 Hand Parsing and Gesture Recognition with a Commodity Depth Camera
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    Chapter 13 Learning Fast Hand Pose Recognition
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    Chapter 14 Real-Time Hand Gesture Recognition Using RGB-D Sensor
Attention for Chapter 6: Human-Centered 3D Home Applications via Low-Cost RGBD Cameras
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Citations

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Readers on

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6 Mendeley
Summary Dimensions citations
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Chapter title
Human-Centered 3D Home Applications via Low-Cost RGBD Cameras
Chapter number 6
Book title
Computer Vision and Machine Learning with RGB-D Sensors
Published by
Springer, Cham, January 2014
DOI 10.1007/978-3-319-08651-4_6
Book ISBNs
978-3-31-908650-7, 978-3-31-908651-4
Authors

Zhenbao Liu, Shuhui Bu, Junwei Han

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 33%
Student > Ph. D. Student 1 17%
Professor > Associate Professor 1 17%
Student > Doctoral Student 1 17%
Unknown 1 17%
Readers by discipline Count As %
Computer Science 3 50%
Psychology 2 33%
Unknown 1 17%

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