Title :
Implicit representation and scene reconstruction from probability density functions
Author :
Seitz, Steven M. ; Anandan, P.
Author_Institution :
Robotics Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA
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
Keywords :
image reconstruction; image representation; maximum likelihood estimation; probability; implicit representation; linear features; maximum likelihood shape estimation; probability density functions; recursive solution; scene reconstruction; uncalibrated affine scene reconstruction; uncertainty information; Algebra; Equations; Image reconstruction; Layout; Maximum likelihood estimation; Probability density function; Recursive estimation; Robots; Shape; Uncertainty;
Conference_Titel :
Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on.
Conference_Location :
Fort Collins, CO
Print_ISBN :
0-7695-0149-4
DOI :
10.1109/CVPR.1999.784604
