caa a22 4500 469036036 CHVBK 20180323132756.0 cr unu---uuuuu 170328e19921201xx s 000 0 eng 10.1007/BF00133702 doi (NATIONALLICENCE)springer-10.1007/BF00133702 Rohr Karl Arbeitsbereich Kognitive Systeme, FB Informatik, Universität Hamburg, Bodenstedtstr 16, 2000, Hamburg 50, FRG aut Recognizing corners by fitting parametric models [Elektronische Daten] [Karl Rohr] 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. Kluwer Academic Publishers, 1992 International Journal of Computer Vision Kluwer Academic Publishers 9/3(1992-12-01), 213-230 0920-5691 9:3<213 1992 9 11263 https://doi.org/10.1007/BF00133702 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/BF00133702 text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- Rohr Karl Arbeitsbereich Kognitive Systeme, FB Informatik, Universität Hamburg, Bodenstedtstr 16, 2000, Hamburg 50, FRG aut NATIONALLICENCE 773 0- International Journal of Computer Vision Kluwer Academic Publishers 9/3(1992-12-01), 213-230 0920-5691 9:3<213 1992 9 11263 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer