Multiple knowledge sources and evidential reasoning for shape recognition

Abstract

A shape recognition approach is presented. Uncertainty handling, combining, and propagation form the heart of the method. Multiple knowledge sources extract information from the segmented image and increase knowledge about undefined shapes. Knowledge sources have to be tuned to discriminate shape classes, and a critical number of independent knowledge sources guarantees the classification. Information provided by the knowledge sources is stored in the Shafer form of probability mass assignment. Dempster's rule is used to update belief in classes. A brief theoretical overview is given. Combined with a heuristic, this method achieves interesting results as well as a short execution time. An example derived from an application in the PROMETHEUS project, consisting of traffic sign recognition on a motorway, illustrates this method