3D Euclidean reconstruction from multiple uncalibrated views using particle swarm optimization

The process of creating 3D models from data captured from real objects is called 3D reconstruction. Given images of a set of 3D points, with known correspondence among them, we would like to reconstruct the coordinates of the 3D points and discover the perspective projection matrices associated with...

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Main Author:	Wong, Ya Ping
Format:	Thesis
Published:	2012
Subjects:	QA Mathematics
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id	my-mmu-ep.5987
record_format	uketd_dc
spelling	my-mmu-ep.59872015-04-09T09:01:33Z 3D Euclidean reconstruction from multiple uncalibrated views using particle swarm optimization 2012-04 Wong, Ya Ping QA Mathematics The process of creating 3D models from data captured from real objects is called 3D reconstruction. Given images of a set of 3D points, with known correspondence among them, we would like to reconstruct the coordinates of the 3D points and discover the perspective projection matrices associated with the cameras used to capture these images. Once this information is obtained, we would be able to compute any novel view of these 3D points from a virtual camera placed at any position and orientation. The analytical method which estimates the perspective projection matrices has the limitation that the reconstructed models are ambiguous up to a projective transformation. To overcome this, we estimate the camera intrinsic and extrinsic parameters directly. We solve this problem as an optimization problem using the Particle Swarm Optimization (PSO) algorithm. 2012-04 Thesis http://shdl.mmu.edu.my/5987/ http://library.mmu.edu.my/diglib/onlinedb/dig_lib.php phd doctoral Multimedia University Faculty of Computing and Informatics
institution	Multimedia University
collection	MMU Institutional Repository
topic	QA Mathematics
spellingShingle	QA Mathematics Wong, Ya Ping 3D Euclidean reconstruction from multiple uncalibrated views using particle swarm optimization
description	The process of creating 3D models from data captured from real objects is called 3D reconstruction. Given images of a set of 3D points, with known correspondence among them, we would like to reconstruct the coordinates of the 3D points and discover the perspective projection matrices associated with the cameras used to capture these images. Once this information is obtained, we would be able to compute any novel view of these 3D points from a virtual camera placed at any position and orientation. The analytical method which estimates the perspective projection matrices has the limitation that the reconstructed models are ambiguous up to a projective transformation. To overcome this, we estimate the camera intrinsic and extrinsic parameters directly. We solve this problem as an optimization problem using the Particle Swarm Optimization (PSO) algorithm.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Wong, Ya Ping
author_facet	Wong, Ya Ping
author_sort	Wong, Ya Ping
title	3D Euclidean reconstruction from multiple uncalibrated views using particle swarm optimization
title_short	3D Euclidean reconstruction from multiple uncalibrated views using particle swarm optimization
title_full	3D Euclidean reconstruction from multiple uncalibrated views using particle swarm optimization
title_fullStr	3D Euclidean reconstruction from multiple uncalibrated views using particle swarm optimization
title_full_unstemmed	3D Euclidean reconstruction from multiple uncalibrated views using particle swarm optimization
title_sort	3d euclidean reconstruction from multiple uncalibrated views using particle swarm optimization
granting_institution	Multimedia University
granting_department	Faculty of Computing and Informatics
publishDate	2012
_version_	1747829603106291712

3D Euclidean reconstruction from multiple uncalibrated views using particle swarm optimization

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