Automatic and adaptable registration of live RGBD video streams sharing partially overlapping views

Rafighi, Afsaneh (2015) Automatic and adaptable registration of live RGBD video streams sharing partially overlapping views. Masters thesis, Memorial University of Newfoundland.

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Abstract

In this thesis, we introduce DeReEs-4v, an algorithm for unsupervised and automatic registration of two video frames captured depth-sensing cameras. DeReEs-4V receives two RGBD video streams from two depth-sensing cameras arbitrary located in an indoor space that share a minimum amount of 25% overlap between their captured scenes. The motivation of this research is to employ multiple depth-sensing cameras to enlarge the field of view and acquire a more complete and accurate 3D information of the environment. A typical way to combine multiple views from different cameras is through manual calibration. However, this process is time-consuming and may require some technical knowledge. Moreover, calibration has to be repeated when the location or position of the cameras change. In this research, we demonstrate how DeReEs-4V registration can be used to find the transformation of the view of one camera with respect to the other at interactive rates. Our algorithm automatically finds the 3D transformation to match the views from two cameras, requires no human interference, and is robust to camera movements while capturing. To validate this approach, a thorough examination of the system performance under different scenarios is presented. The system presented here supports any application that might benefit from the wider field-of-view provided by the combined scene from both cameras, including applications in 3D telepresence, gaming, people tracking, videoconferencing and computer vision.

Item Type: Thesis (Masters)
URI: http://research.library.mun.ca/id/eprint/11739
Item ID: 11739
Additional Information: Includes bibliographical references (pages 70-80).
Keywords: wide-screen 3D video, registration of multiple RGBD cameras, field of view exten
Department(s): Science, Faculty of > Computer Science
Date: October 2015
Date Type: Submission
Library of Congress Subject Heading: Computer vision; Depth perception; 3-D video (Three-dimensional imaging); Image registration

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