Multiple Feature Integration for Robust Object Localization

Authors:
S. Shah;J. K. Aggarwal
Affiliations:
-;-
Venue:
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Year:
1998

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Abstract

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.