Title :
Parameter estimation in MRF line process models
Author :
Nadabar, Sateesha G. ; Jain, Anil K.
Author_Institution :
Dept. of Comput. Sci., Michigan State Univ., East Lansing, MI, USA
Abstract :
A scheme for the estimation of the Markov random field (MRF) line process parameters that uses geometric CAD models of the objects in the scene is presented. The models are used to generate synthetic images of the objects from random viewpoints. The edge maps computed from the synthesized images are used as training samples to estimate the line process parameters using a least squares method. It is shown that this parameter estimation method is useful for detecting edges in range as well as intensity images
Keywords :
Markov processes; computational geometry; computer vision; least squares approximations; parameter estimation; Markov random field line process models; edges detection; geometric CAD models; intensity images; least squares method; line process parameters; parameter estimation; synthesized images; synthetic images; Computer science; Context modeling; Frequency estimation; Image edge detection; Labeling; Lattices; Layout; Markov random fields; Parameter estimation; Solid modeling;
Conference_Titel :
Computer Vision and Pattern Recognition, 1992. Proceedings CVPR '92., 1992 IEEE Computer Society Conference on
Conference_Location :
Champaign, IL
Print_ISBN :
0-8186-2855-3
DOI :
10.1109/CVPR.1992.223140
