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Permanent link (DOI): https://doi.org/10.7939/R3TQ1X

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Real time spatio temporal segmentation of RGBD cloud and applications Open Access

Descriptions

Other title
Subject/Keyword
Segmentation
Object discovery
Object detection
GPU
Point cloud
RGBD
Micorsoft Kinect
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Saini, Amritpal S
Supervisor and department
Zhang, Hong (Computing Science)
Ray, Nilanjan (Computing Science)
Examining committee member and department
Zhang, Hong (Computing Science)
Boulanger, Pierre (Computing Science)
Ray, Nilanjan (Computing Science)
Department
Department of Computing Science
Specialization

Date accepted
2015-01-19T14:42:28Z
Graduation date
2015-06
Degree
Master of Science
Degree level
Master's
Abstract
There is considerable research work going on segmentation of RGB-D clouds due its applications in tasks like scene understanding, robotics etc. The availability of inexpensive and easy to use RGB-D cameras and computational capabilities of GPUs has lead to development of numerous applications in this area. Recently proposed cloud segmentation methods are either slow in operation or do not operate in an online fashion making them unsuitable for applications in robotics. In this work we deal with the aforementioned problem. We propose a method to perform online segmentation of RGB-D scene. Our framework is built on dense scene mapping methods like Kinect fusion. It allows us to generate accurate and dense depth maps and provide camera pose information. Instead of directly operating on a large 3D point cloud we process individual RGB and depth frames which are assembled in a dense cloud in an incremental fashion. Pose information is used to integrate the segmentation maps into the global label cloud using GPU. We perform multi-view integration of segments as the camera is moved around in the scene by formulating the problem as weighted graph. We will discuss applications of our segmentation framework to perform real time and scalable object discovery and object detection.
Language
English
DOI
doi:10.7939/R3TQ1X
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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