DocumentCode
321241
Title
Dynamic vision and estimation on spheres
Author
Picci, Giorgio
Author_Institution
Dipt. di Elettronica e Inf., Padova Univ., Italy
Volume
2
fYear
1997
fDate
10-12 Dec 1997
Firstpage
1140
Abstract
In this paper we analyze the simplest kind of estimation problems encountered in dynamic vision, namely tracking an unknown direction from noisy perspective projections on the image plane. We formulate this as an estimation problem on the unit sphere. Assuming a suitable class of probability density functions, we give explicit formulas to compute the steady-state MAP estimate. These formulas look in certain cases like nonlinear recursions of the Kalman filtering type
Keywords
Bayes methods; computer vision; image reconstruction; optical tracking; probability; recursive estimation; Bayesian perspective estimation; Kalman filtering; computer vision; directional reconstruction; dynamic vision; probability density functions; recursive estimation; tracking; Additive noise; Cameras; Computer vision; Image analysis; Kalman filters; Machine vision; Optical distortion; Optical noise; Probability density function; Steady-state;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control, 1997., Proceedings of the 36th IEEE Conference on
Conference_Location
San Diego, CA
ISSN
0191-2216
Print_ISBN
0-7803-4187-2
Type
conf
DOI
10.1109/CDC.1997.657601
Filename
657601
Link To Document