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Implicit Representation and Scene Reconstruction from Probability Density Functions
S. Seitz and P. Anandan
Proc. Computer Vision and Pattern Recognition Conference (CVPR '99), 1999, pp. 28 - 34.

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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.

Text Reference

S. Seitz and P. Anandan, "Implicit Representation and Scene Reconstruction from Probability Density Functions," Proc. Computer Vision and Pattern Recognition Conference (CVPR '99), 1999, pp. 28 - 34.

BibTeX Reference

@inproceedings{Seitz_1999_2842,
   author = "Steven Seitz and P. Anandan",
   title = "Implicit Representation and Scene Reconstruction from Probability Density Functions",
   booktitle = "Proc. Computer Vision and Pattern Recognition Conference (CVPR '99)",
   year = "1999",
   pages = "28 - 34"
}