Learn More
This paper has two goals. The first is to develop a variety of robust methods for the computation of the Fundamental Matrix, the calibration-free representation of camera motion. The methods are drawn from the principal categories of robust estimators, viz. case deletion diagnostics, M-estimators and random sampling, and the paper develops the theory(More)
Most global biogeochemical processes are known to respond to climate change, some of which have the capacity to produce feedbacks through the regulation of atmospheric greenhouse gases. Marine denitrification-the reduction of nitrate to gaseous nitrogen-is an important process in this regard, affecting greenhouse gas concentrations directly through the(More)
We present a new method for solving the problem of motion segmentation, identifying the objects within an image moving independently of the background. We utilise the fact that two views of a static 3D point set are linked by a 3 3 Fundamental Matrix F]. The Fundamental Matrix contains all the information on structure and motion from a given set of point(More)
MLESAC is an established algorithm for maximum-likelihood estimation by random sampling consensus, devised for computing multiview entities like the fundamental matrix from correspondences between image features. A shortcoming of the method is that it assumes that little is known about the prior probabilities of the validities of the correspondences. This(More)
A structure from motion algorithm is described which recovers structure and camera position, modulo a projective ambiguity. Camera calibration is not required, and camera parameters such as focal length can be altered freely during motion. The structure is updated sequentially over an image sequence, in contrast to schemes which employ a batch process. A(More)