Multiple feature integration for robust object localization
Shishir K. Shah, J.K. Aggarwal · 2002
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 classi#ers, each of which solves the problem independently based on its input observations. Each classi- #er module is trained to detect manmade object regions and a higher order decision integrator collects evidencefrom each of the modules to delineate a #nal 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. 1 Introduction This paper addresses the problem of object localization in complex scenes imaged by a single sensor or registered multiple sensors. The general topic of determining region of interest #ROI# and object detection is a critical step in all existing paradigms for object recognition. In...