Kruppa's Equations Derived from the Fundamental Matrix
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Calibration of a Moving Camera from PointCorrespondences and Fundamental Matrices
International Journal of Computer Vision
In Defense of the Eight-Point Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision - 1998 Marr Prize
A Case Against Kruppa's Equations for Camera Self-Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer vision
Multiple view geometry in computer vision
The Geometry of Multiple Images: The Laws That Govern The Formation of Images of A Scene and Some of Their Applications
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
From Projective to Euclidean Space Under any Practical Situation, a Criticism of Self-Calibration
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Video synchronization and its application to object transfer
Image and Vision Computing
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We describe a new method of achieving autocalibration that uses a stochastic optimization approach taken from the field of evolutionary computing and we perform a number of experiments on 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, i.e. they are taken from the same camera without varying the focal length. We show that for the autocalibration of focal length and aspect ratio, the evolutionary method achieves comparable results without the implementation complexity of other methods. Autocalibrating from the fundamental matrix is simply transformed into a global minimization problem utilizing a cost function based on the properties of the fundamental matrix and the essential matrix.