Rule-Based Evidence Accrual System for Image Understanding

摘要

The main function of an evidence accrual system for image understanding is to sequentially update information on scene objects based on new sensor data or on non-sensory information such as intelligence. This paper presents a concept for sequentially updating information on scene objects. Scene objects and background (clutter) are represented by attributed relational graphs in which nodes represent objects of interest and arcs represent inter-object relations. Dynamic recognition/identification of nodes is accomplished by a belief/disbelief measure. Our experimental results with infrared images show improvements in natural scene object recognition over traditional image processing methods. (Author)