Ling Shao, Jungong Han, Pushmeet Kohli, Zhengyou Zhang's Computer Vision and Machine Learning with RGB-D Sensors PDF

By Ling Shao, Jungong Han, Pushmeet Kohli, Zhengyou Zhang

ISBN-10: 3319086502

ISBN-13: 9783319086507

ISBN-10: 3319086510

ISBN-13: 9783319086514

This e-book offers an interdisciplinary number of state-of-the-art examine on RGB-D dependent computing device imaginative and prescient. gains: discusses the calibration of colour and intensity cameras, the aid of noise on intensity maps and strategies for taking pictures human functionality in 3D; experiences a variety of functions which use RGB-D details to reconstruct human figures, overview strength intake and acquire exact motion class; offers an technique for 3D item retrieval and for the reconstruction of fuel movement from a number of Kinect cameras; describes an RGB-D computing device imaginative and prescient procedure designed to help the visually impaired and one other for smart-environment sensing to help aged and disabled humans; examines the powerful positive aspects that symbolize static hand poses and introduces a unified framework to implement either temporal and spatial constraints for hand parsing; proposes a brand new classifier structure for real-time hand pose acceptance and a singular hand segmentation and gesture reputation system.

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Extra resources for Computer Vision and Machine Learning with RGB-D Sensors

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In a rather invasive approach Faion et al. [11] manage to toggle the projector subsystem to perform measurements similar to Schroeder et al. [44]. They use Bayesian state estimator to intelligently schedule which sensor is to be selected for the next time frame. Their maximal reconstruction error denotes 21 mm. Kainz et al. [19] describe an elaborate setup for eight Kinects mounted on vibrating rods and one freely moving Kinect suitable for various applications, such as motion capturing and reconstruction.

In: IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, pp 3993–3999 12. Fuhrmann AL, Kretz J, Burwik P (2013) Multi sensor tracking for live sound transformation 13. Glocker B, Izadi S, Shotton J, Criminisi A (2013) Real-time RGB-D camera relocalization. In: International symposium on mixed and augmented reality. Springer 14. Han J, Shao L, Xu D, Shotton J (2013) Enhanced computer vision with microsoft kinect sensor: a review 15. Hossny M, Filippidis D, Abdelrahman W, Zhou H, Fielding M, Mullins J, Wei L, Creighton D, Puri V, Nahavandi S (2012) Low cost multimodal facial recognition via kinect sensors.

In: IEEE international conference on computer vision workshops (ICCV workshops). IEEE, pp 1108–1113 8. Berger K, Ruhl K, Brümmer C, Schröder Y, Scholz A, Magnor M (2011) Markerless motion capture using multiple color-depth sensors. In Proceedings of vision, modeling and visualization (VMV), vol 2011, p 3 9. Butler DA, Izadi S, Hilliges O, Molyneaux D, Hodges S, Kim D (2012) Shake‘n’sense: reducing interference for overlapping structured light depth cameras. In: Proceedings of the 2012 ACM annual conference on human factors in computing systems.

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Computer Vision and Machine Learning with RGB-D Sensors by Ling Shao, Jungong Han, Pushmeet Kohli, Zhengyou Zhang


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