Information fusion in computer vision using the fuzzy integral

Intelligent systems must be capable of integrating information from a variety of sources. This information can be used to increase object classification confidence, remove ambiguity inherent in a single representation, and resolve conflict in separate decisions. Methods for combining evidence produced by multiple information sources include Bayesian reasoning, Dempster-Shafer belief theory, and heuristic measures of belief and disbelief. A method of evidence fusion, based on the fuzzy integral, is developed. This technique nonlinearly combines objective evidence, in the form of a fuzzy membership function, with subjective evaluation of the worth of the sources with respect to the decision. Various new theoretical properties of this technique are developed and its applicability to information fusion in computer vision is demonstrated through simulation and with object recognition data from forward looking infrared imagery.