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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Self-Calibration with Two Views Using the Scale-Invariant Feature Transform
verfasst von : Jae-Ho Yun, Rae-Hong Park
Erschienen in: Advances in Visual Computing
Verlag: Springer Berlin Heidelberg
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In this paper, we present a self-calibration strategy to estimate camera intrinsic and extrinsic parameters using the scale-invariant feature transform (SIFT). The accuracy of the estimated parameters depends on how reliably a set of image correspondences is established. The SIFT employed in the self-calibration algorithms plays an important role in accurate estimation of camera parameters, because of its robustness to changes in viewing conditions. Under the assumption that the camera intrinsic parameters are constant, experimental results show that the SIFT-based approach using two images yields more competitive results than the existing Harris corner detector-based approach using two images.