Approaches to Object/Background Segmentation and Object Dimension Estimation
2006 (English)Report (Other academic)
In this paper, optimization approaches for object/background segmentation and object dimension/orientation estimation are studied. The data sets are collected with a laser radar or are simulated laser radar data. Three cases are defined: 1) Segmentation of the data set into object and background data. When there are several objects present in the scene, data from each object is also separated into different clusters. Bayesian hypothesis testing of two classes is studied. 2) Estimation of the object’s dimensions and orientation using object data only. 3) Estimation of the object’s dimensions and orientation using both object and background data. The dimension and orientation estimation problem is formulated using non-convex optimization, least squares and, convex optimization expressions. The performance of the methods are investigated in simulations.
Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2006. , 25 p.
LiTH-ISY-R, ISSN 1400-3902 ; 2746
Segmentation, Bayes, Rectangle estimation, Least squares, Optimization
IdentifiersURN: urn:nbn:se:liu:diva-14125ISRN: LiTH-ISY-R-2746OAI: oai:DiVA.org:liu-14125DiVA: diva2:22678