Authors
Andrew W Fitzgibbon, Robert B Fisher
Description
In this paper, we are concerned with the problem of deciding whether a tted model accurately describes the data to which it has been tted. We have developed an e ective method of testing the lack-of-t of a parametric model to data, with applications to the computer vision problems of robust estimation, model selection, and curve and surface segmentation. The bene ts of this technique are high sensitivity (large response to small outliers) and very low dependence on the noise distribution of the input data. Our test is new to the computer vision community in several ways:
Scholar articles