Recognizing corners by fitting parametric models

摘要

The parametric model of a certain class of characteristic intensity variations in Rohr (1990, 1992), which is the superposition of elementary model functions, is employed to identify corners in images. Estimates of the searched model parameters characterizing completely single grey-value structures are determined by a least-squares fit of the model to the observed image intensities applying the minimization method of Levenberg-Marquardt. In particular, we develop an analytical approximation of our model in such a way that function values can be calculated without numerical integration. Assuming the blur of the imaging system to be describable by Gaussian convolution our approach permits subpixel localization of the corner position of the unblurred grey-value structures, that is, to reverse the blur of the imaging system. By fitting our model to the original as well as to the smoothed original-image cues can be obtained for finding out whether the underlying model is an adequate description or not. Results are shown for real image data.