Implicit Representation and Scene Reconstruction from Probability Density Functions
Conference Paper, Proceedings of (CVPR) Computer Vision and Pattern Recognition, Vol. 2, pp. 28 - 34, June, 1999
Abstract
A technique is presented for representing linear features as probability density functions in two or three dimensions. Three chief advantages of this approach are (1) a unified representation and algebra for manipulating points, lines, and planes, (2) seamless incorporation of uncertainty information, and (3) a very simple recursive solution for maximum likelihood shape estimation. Applications to uncalibrated affine scene reconstruction are presented, with results on images of an outdoor environment.
BibTeX
@conference{Seitz-1999-16674,author = {Steven Seitz and P. Anandan},
title = {Implicit Representation and Scene Reconstruction from Probability Density Functions},
booktitle = {Proceedings of (CVPR) Computer Vision and Pattern Recognition},
year = {1999},
month = {June},
volume = {2},
pages = {28 - 34},
}
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