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Segmentation of Salient Regions in Outdoor Scenes Using Imagery and 3-D Data

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3 Author(s)
Gunhee Kim ; Robot. Inst., Carnegie Mellon Univ., Pittsburgh, PA ; Huber, D. ; Hebert, M.

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.

Published in:

Applications of Computer Vision, 2008. WACV 2008. IEEE Workshop on

Date of Conference:

7-9 Jan. 2008

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