Multiple feature integration for robust object localization

This paper presents a methodology for localization of manmade objects in complex scenes by learning multiple feature models in images. The methodology is based on a modular structure consisting of multiple classifiers, each of which solves the problem independently based on its input observations. Each classifier module is trained to detect manmade object regions and a higher order decision integrator collects evidence from each of the modules to delineate a final region of interest. The proposed framework is applied to the problem of Automatic Manmade Object Localization/Detection. Results obtained on the detection of vehicles in color visual and infrared imagery are presented in this paper