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

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

ISBN-10: 3319086502

ISBN-13: 9783319086507

ISBN-10: 3319086510

ISBN-13: 9783319086514

This booklet provides an interdisciplinary number of state-of-the-art study on RGB-D established machine imaginative and prescient. positive aspects: 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 range of purposes which use RGB-D details to reconstruct human figures, evaluation power intake and procure exact motion type; provides an strategy for 3D item retrieval and for the reconstruction of gasoline circulation 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 aid aged and disabled humans; examines the potent 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 reputation and a unique hand segmentation and gesture reputation system.

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For the camera pose at a given frame [45, 46]. A sophisticated approach transforms the labeling in another space: instead of letting the user annotate in image space, the static scene captured with a moving Kinect is reconstructed in 3D and 2 A State of the Art Report. . g. [21]. The annotated point clouds are then simply reprojected into the input stream using the camera pose for the Kinect sensor at each frame. 9 SLAM Highly accurate depth data are necessary for 3D reconstruction and simultaneous reconstruction and SLAM applications, although the requirements for mapping or localization can differ within the applicational context.

We use the popular checkerboard pattern adopted in color camera calibration (Fig. 1); thus, no extra hardware is needed. We utilize the fact that points on the checkerboard shall lie on a common plane; thus, their distance to the plane shall be minimized. Point correspondences between the depth and color images may be further added to help improve calibration accuracy. A maximum likelihood framework is presented with careful modeling of the sensor noise, particularly in the depth image. Extensive experimental results are presented to validate the proposed calibration method.

The detected face region is used to guide the contour detection in the other three views. The detected contours are then finally merged to to a 3D estimate of the pose of the anatomy. The authors claim a precision of 3 mm at the expected 30 Hz. Wilson and Benko [53] use three PrimeSense depth cameras which stream at 320 × 240 px resolution and 30 Hz for human interaction with an augmented reality table. They compare input depth image streams against background depth images for each depth camera captured when the room is empty to segment out the human user.

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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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