ML Anthology

Multiple Feature Integration for Robust Object Localization

Shishir Shah, J. K. Aggarwal
CVPR 1998 pp. 765-771
doi:10.1109/CVPR.1998.698690 /cvpr/1998/shah1998cvpr-multiple/

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.

CVPR Semantic Scholar

Cite

Text

Shah and Aggarwal. "Multiple Feature Integration for Robust Object Localization." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1998. doi:10.1109/CVPR.1998.698690

Markdown

[Shah and Aggarwal. "Multiple Feature Integration for Robust Object Localization." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1998.](https://mlanthology.org/cvpr/1998/shah1998cvpr-multiple/) doi:10.1109/CVPR.1998.698690

BibTeX

@inproceedings{shah1998cvpr-multiple,
  title     = {{Multiple Feature Integration for Robust Object Localization}},
  author    = {Shah, Shishir and Aggarwal, J. K.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1998},
  pages     = {765-771},
  doi       = {10.1109/CVPR.1998.698690},
  url       = {https://mlanthology.org/cvpr/1998/shah1998cvpr-multiple/}
}