DocumentCode :
3542560
Title :
Generalized Kalman filter using fully and partially occluded models
Author :
Barzohar, Meir ; Cohen, Moshe ; Ziskind, Ilan ; Cooper, David B.
Author_Institution :
Comput. Vision Group, RAFAEL, Haifa, Israel
Volume :
3
fYear :
1997
fDate :
26-29 Oct 1997
Firstpage :
134
Abstract :
A heretofore unsolved challenge is the completely automatic and accurate estimation of road boundaries in aerial images when the roads may be partially or completely locally occluded and clutter may be prevalent. We introduce a roadfinder that is effective in meeting this challenge. The roadfinder begins with one or more seeds on each long road, and then accurately estimates the remaining boundaries, which can be found completely automatically by the algorithm described by Barzohar and Cooper (see IEEE PAMI, vol.18, no.7, p.407-21, 1996). The algorithm is robust to missing boundary edges on one side of the road and on both sides of the road simultaneously. (These arise from shadows and occlusion by trees, poles, small structures, etc.) It is also robust to clutter within the road caused by cars or trucks, and to clutter resulting from intersecting or close parallel roads. The algorithm is based on simple clutter and occlusion models and a combined multihypothesis generalized Kalman filter (MGKF)
Keywords :
Kalman filters; clutter; edge detection; filtering theory; accurate estimation; aerial images; automatic road boundary estimation; cars; clutter; fully occluded models; generalized Kalman filter; missing boundary edges; parallel roads; partially occluded models; roadfinder; shadows; trucks; Bayesian methods; Computer vision; Differential equations; Laboratories; Roads; Robustness; Satellites; Stochastic processes; Velocity measurement;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing, 1997. Proceedings., International Conference on
Conference_Location :
Santa Barbara, CA
Print_ISBN :
0-8186-8183-7
Type :
conf
DOI :
10.1109/ICIP.1997.632019
Filename :
632019
Link To Document :
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