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Computer vision and machine learning with RGB-D sensors [electronic resource]

Computer vision and machine learning with RGB-D sensors [electronic resource]

Material type
E-Book(소장)
Personal Author
Shao, Ling.
Title Statement
Computer vision and machine learning with RGB-D sensors [electronic resource] / edited by Ling Shao ... [et al.].
Publication, Distribution, etc
Cham :   Springer International Publishing :   Imprint: Springer,   2014.  
Physical Medium
1 online resource (x, 316 p.) : ill. (some col.).
Series Statement
Advances in Computer Vision and Pattern Recognition,2191-6586
ISBN
9783319086514
요약
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.
General Note
Title from e-Book title page.  
Content Notes
Part I: Surveys -- 3D Depth Cameras in Vision: Benefits and Limitations of the Hardware -- A State-of-the-Art Report on Multiple RGB-D Sensor Research and on Publicly Available RGB-D Datasets -- Part II: Reconstruction, Mapping and Synthesis -- Calibration Between Depth and Color Sensors for Commodity Depth Cameras -- Depth Map Denoising via CDT-Based Joint Bilateral Filter -- Human Performance Capture Using Multiple Handheld Kinects -- Human Centered 3D Home Applications via Low-Cost RGBD Cameras -- Matching of 3D Objects Based on 3D Curves -- Using Sparse Optical Flow for Two-Phase Gas Flow Capturing with Multiple Kinects -- Part III: Detection, Segmentation and Tracking -- RGB-D Sensor-Based Computer Vision Assistive Technology for Visually Impaired Persons -- RGB-D Human Identification and Tracking in a Smart Environment -- Part IV: Learning-Based Recognition -- Feature Descriptors for Depth-Based Hand Gesture Recognition -- Hand Parsing and Gesture Recognition with a Commodity Depth Camera -- Learning Fast Hand Pose Recognition -- Real time Hand-Gesture Recognition Using RGB-D Sensor.
Bibliography, Etc. Note
Includes bibliographical references and index.
이용가능한 다른형태자료
Issued also as a book.  
Subject Added Entry-Topical Term
Computer vision. Machine learning.
Short cut
URL
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006 m d
007 cr
008 200916s2014 sz a ob 001 0 eng d
020 ▼a 9783319086514
040 ▼a 211009 ▼c 211009 ▼d 211009
082 0 4 ▼a 006.37 ▼2 23
084 ▼a 006.37 ▼2 DDCK
090 ▼a 006.37
245 0 0 ▼a Computer vision and machine learning with RGB-D sensors ▼h [electronic resource] / ▼c edited by Ling Shao ... [et al.].
260 ▼a Cham : ▼b Springer International Publishing : ▼b Imprint: Springer, ▼c 2014.
300 ▼a 1 online resource (x, 316 p.) : ▼b ill. (some col.).
490 1 ▼a Advances in Computer Vision and Pattern Recognition, ▼x 2191-6586
500 ▼a Title from e-Book title page.
504 ▼a Includes bibliographical references and index.
505 0 ▼a Part I: Surveys -- 3D Depth Cameras in Vision: Benefits and Limitations of the Hardware -- A State-of-the-Art Report on Multiple RGB-D Sensor Research and on Publicly Available RGB-D Datasets -- Part II: Reconstruction, Mapping and Synthesis -- Calibration Between Depth and Color Sensors for Commodity Depth Cameras -- Depth Map Denoising via CDT-Based Joint Bilateral Filter -- Human Performance Capture Using Multiple Handheld Kinects -- Human Centered 3D Home Applications via Low-Cost RGBD Cameras -- Matching of 3D Objects Based on 3D Curves -- Using Sparse Optical Flow for Two-Phase Gas Flow Capturing with Multiple Kinects -- Part III: Detection, Segmentation and Tracking -- RGB-D Sensor-Based Computer Vision Assistive Technology for Visually Impaired Persons -- RGB-D Human Identification and Tracking in a Smart Environment -- Part IV: Learning-Based Recognition -- Feature Descriptors for Depth-Based Hand Gesture Recognition -- Hand Parsing and Gesture Recognition with a Commodity Depth Camera -- Learning Fast Hand Pose Recognition -- Real time Hand-Gesture Recognition Using RGB-D Sensor.
520 ▼a 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.
530 ▼a Issued also as a book.
538 ▼a Mode of access: World Wide Web.
650 0 ▼a Computer vision.
650 0 ▼a Machine learning.
700 1 ▼a Shao, Ling.
830 0 ▼a Advances in Computer Vision and Pattern Recognition.
856 4 0 ▼u https://oca.korea.ac.kr/link.n2s?url=http://dx.doi.org/10.1007/978-3-319-08651-4
945 ▼a KLPA
991 ▼a E-Book(소장)

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Main Library/e-Book Collection/ Call Number CR 006.37 Accession No. E14032856 Availability Loan can not(reference room) Due Date Make a Reservation Service M

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