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Computer Vision and Machine Learning with RGB-D Sensors

Aus der Reihe Advances in Computer Vision and Pattern Recognition

49,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

05.08.2014

Abbildungen

X, 316 p. 163 illus., 148 illus. in color.

Herausgeber

Ling Shao + weitere

Verlag

Springer

Seitenzahl

316

Maße (L/B/H)

24,1/16/2,4 cm

Gewicht

658 g

Sprache

Englisch

ISBN

978-3-319-08650-7

Beschreibung

Portrait


Dr. Ling Shao
is a Senior Lecturer (Associate Professor) in the Department of Electronic and Electrical Engineering at the University of Sheffield, UK. His publications include the Springer title
Multimedia Interaction and Intelligent User Interfaces
.

Dr. Jungong Han
is a Senior Scientist at Civolution Technology, Eindhoven, and a Guest Researcher at the Eindhoven University of Technology, Netherlands.

Dr. Pushmeet Kohli
is a Senior Researcher in the Machine Learning and Perception Group at Microsoft Research Cambridge and an Associate in the Psychometrics Centre at the University of Cambridge, UK.

Dr. Zhengyou Zhang
, IEEE Fellow and ACM Fellow, is a Principal Researcher and Research Manager of the Multimedia, Interaction, and Communication Group at Microsoft Research Redmond, WA, USA.

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

05.08.2014

Abbildungen

X, 316 p. 163 illus., 148 illus. in color.

Herausgeber

Verlag

Springer

Seitenzahl

316

Maße (L/B/H)

24,1/16/2,4 cm

Gewicht

658 g

Sprache

Englisch

ISBN

978-3-319-08650-7

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Computer Vision and Machine Learning with RGB-D Sensors
  • Produktbild: Computer Vision and Machine Learning with RGB-D Sensors

  • 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.