Camera Self-Calibration Using the Singular Value Decomposition of the Fundamental Matrix: From Point Correspondences to 3D Measurements (1999)
| Venue: | IN PROCEEDINGS OF ACCV’00 |
| Citations: | 12 - 7 self |
BibTeX
@INPROCEEDINGS{Lourakis99cameraself-calibration,
author = {Manolis I. A. Lourakis and Rachid Deriche},
title = {Camera Self-Calibration Using the Singular Value Decomposition of the Fundamental Matrix: From Point Correspondences to 3D Measurements},
booktitle = {IN PROCEEDINGS OF ACCV’00},
year = {1999},
pages = {403--408},
publisher = {}
}
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OpenURL
Abstract
This paper deals with a fundamental problem in motion and stereo analysis, namely that of determining the camera intrinsic calibration parameters. A novel method is proposed that follows the autocalibration paradigm, according to which calibration is achieved not with the aid of a calibration pattern but by observing a number of image features in a set of successive images. The proposed method relies upon the Singular Value Decomposition of the fundamental matrix, which leads to a particularly simple form of the Kruppa equations. In contrast to the classical formulation that yields an over-determined system of constraints, the derivation proposed here provides a straightforward answer to the problem of determining which constraints to employ among the set of available ones. Moreover, the derivation is a purely algebraic one, without a need for resorting to the somewhat non-intuitive geometric concept of the absolute conic. Apart from the fundamental matrix itself, no other quantities...







