A Bayesian Framework for Multifeature / Multisensor Integration - Automatic Target Detection and Recognition

This paper describes a new methodology for Automatic Target Recognition in visual images and second generation Forward Looking Infrared (FLIR) images. A system that uses information from multiple features or sensors can employ redundancy, diversity and complementarity to overcome the shortcomings of single-sensor systems and improve performance. In this paper, a general multifeature/multisensor framework is proposed which does not simply expand the dimensionality of the feature space, but which can discern new features to provide greater discrimination. Using this framework, a more focused methodology is described for localization of targets in complex scenes by learning multiple feature models in images. The methodology is based on a modular structure consisting of multiple classi ers, each of which solves the problem independently based on its input observations. A higher level decision integration is obtained through a supra-Bayesian scheme. Finally, a recognition methodology is proposed to classify segmented target regions by extending the multifeature/multisensor framework. Recognition is performed hierarchically by using geometric and photometric features for object representation.