A structural knowledge-based vehicle recognition method is modified yielding a new probabilistic foundation for the decisions. The method uses a pre-calculated set of hidden line projected views of articulated polyhedral models of the vehicles. Model view structures are set into correspondence with structures composed from edge lines in the image. The correspondence space is searched utilizing a 4D Houghtype accumulator. Probabilistic models of the background and the error in the measurements of the image structures lead to likelihood estimations that are used for the decision. The likelihood is propagated along the structure of the articulated model. The system is tested on a cluttered outdoor scene. To ensure any-time performance the recognition process is implemented in a data-driven production system.
CITATION STYLE
Michaelsen, E., & Stilla, U. (2002). Probabilistic decisions in production nets: An example from vehicle recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2396, pp. 225–233). Springer Verlag. https://doi.org/10.1007/3-540-70659-3_23
Mendeley helps you to discover research relevant for your work.