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Abstract

This paper describes a segmentation method for extracting salient regions in outdoor scenes using both 3-D laser scans and imagery information. Our approach is a bottom-up attentive process without any high-level priors, models, or learning. As a mid-level vision task, it is not only robust against noise and outliers but it also provides valuable information for other high-level tasks in the form of optimal segments and their ranked saliency. In this paper, we propose a new saliency definition for 3-D point clouds and we incorporate it with saliency features from color information.

Keywords

Computer Vision, LADAR, Saliency, Segmentation

Notes

Sponsor: U.S Army Research Laboratory

Grant ID: DAAD19-01-2-0012

Associated Center(s) / Consortia: Vision and Autonomous Systems Center and Field Robotics Center

Associated Project(s): CTA Robotics

Number of pages: 8

Text Reference

Gunhee Kim, Daniel Huber, and Martial Hebert, "Segmentation of Salient Regions in Outdoor Scenes Using Imagery and 3-D Data," IEEE Workshop on Applications of Computer VIsion (WACV08), January, 2008.

BibTeX Reference

@inproceedings{Kim_2008_5929,
   author = "Gunhee Kim and Daniel Huber and Martial Hebert",
   title = "Segmentation of Salient Regions in Outdoor Scenes Using Imagery and 3-D Data",
   booktitle = "IEEE Workshop on Applications of Computer VIsion (WACV08)",
   publisher = "IEEE Computer Society",
   month = "January",
   year = "2008",
}

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