Estimating Intrinsic Camera Parameters from the Fundamental Matrix Using an Evolutionary Approach

<p/> <p>Calibration is the process of computing the intrinsic (internal) camera parameters from a series of images. Normally calibration is done by placing predefined targets in the scene or by having special camera motions, such as rotations. If these two restrictions do not hold, then...

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Main Authors: Roth Gerhard, Whitehead Anthony
Format: Article
Language:English
Published: SpringerOpen 2004-01-01
Series:EURASIP Journal on Advances in Signal Processing
Subjects:
Online Access:http://dx.doi.org/10.1155/S1110865704401024
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spelling doaj-bfb73903a4f245158535d4b6467db43d2020-11-24T21:35:56ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802004-01-0120048412751Estimating Intrinsic Camera Parameters from the Fundamental Matrix Using an Evolutionary ApproachRoth GerhardWhitehead Anthony<p/> <p>Calibration is the process of computing the intrinsic (internal) camera parameters from a series of images. Normally calibration is done by placing predefined targets in the scene or by having special camera motions, such as rotations. If these two restrictions do not hold, then this calibration process is called autocalibration because it is done automatically, without user intervention. Using autocalibration, it is possible to create 3D reconstructions from a sequence of uncalibrated images without having to rely on a formal camera calibration process. The fundamental matrix describes the epipolar geometry between a pair of images, and it can be calculated directly from 2D image correspondences. We show that autocalibration from a set of fundamental matrices can simply be transformed into a global minimization problem utilizing a cost function. We use a stochastic optimization approach taken from the field of evolutionary computing to solve this problem. A number of experiments are performed on published and standardized data sets that show the effectiveness of the approach. The basic assumption of this method is that the internal (intrinsic) camera parameters remain constant throughout the image sequence, that is, the images are taken from the same camera without varying such quantities as the focal length. We show that for the autocalibration of the focal length and aspect ratio, the evolutionary method achieves results comparable to published methods but is simpler to implement and is efficient enough to handle larger image sequences.</p>http://dx.doi.org/10.1155/S1110865704401024autocalibrationdynamic hill climbingfundamental matrixevolutionary computingepipolar geometry3D reconstruction
collection DOAJ
language English
format Article
sources DOAJ
author Roth Gerhard
Whitehead Anthony
spellingShingle Roth Gerhard
Whitehead Anthony
Estimating Intrinsic Camera Parameters from the Fundamental Matrix Using an Evolutionary Approach
EURASIP Journal on Advances in Signal Processing
autocalibration
dynamic hill climbing
fundamental matrix
evolutionary computing
epipolar geometry
3D reconstruction
author_facet Roth Gerhard
Whitehead Anthony
author_sort Roth Gerhard
title Estimating Intrinsic Camera Parameters from the Fundamental Matrix Using an Evolutionary Approach
title_short Estimating Intrinsic Camera Parameters from the Fundamental Matrix Using an Evolutionary Approach
title_full Estimating Intrinsic Camera Parameters from the Fundamental Matrix Using an Evolutionary Approach
title_fullStr Estimating Intrinsic Camera Parameters from the Fundamental Matrix Using an Evolutionary Approach
title_full_unstemmed Estimating Intrinsic Camera Parameters from the Fundamental Matrix Using an Evolutionary Approach
title_sort estimating intrinsic camera parameters from the fundamental matrix using an evolutionary approach
publisher SpringerOpen
series EURASIP Journal on Advances in Signal Processing
issn 1687-6172
1687-6180
publishDate 2004-01-01
description <p/> <p>Calibration is the process of computing the intrinsic (internal) camera parameters from a series of images. Normally calibration is done by placing predefined targets in the scene or by having special camera motions, such as rotations. If these two restrictions do not hold, then this calibration process is called autocalibration because it is done automatically, without user intervention. Using autocalibration, it is possible to create 3D reconstructions from a sequence of uncalibrated images without having to rely on a formal camera calibration process. The fundamental matrix describes the epipolar geometry between a pair of images, and it can be calculated directly from 2D image correspondences. We show that autocalibration from a set of fundamental matrices can simply be transformed into a global minimization problem utilizing a cost function. We use a stochastic optimization approach taken from the field of evolutionary computing to solve this problem. A number of experiments are performed on published and standardized data sets that show the effectiveness of the approach. The basic assumption of this method is that the internal (intrinsic) camera parameters remain constant throughout the image sequence, that is, the images are taken from the same camera without varying such quantities as the focal length. We show that for the autocalibration of the focal length and aspect ratio, the evolutionary method achieves results comparable to published methods but is simpler to implement and is efficient enough to handle larger image sequences.</p>
topic autocalibration
dynamic hill climbing
fundamental matrix
evolutionary computing
epipolar geometry
3D reconstruction
url http://dx.doi.org/10.1155/S1110865704401024
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