posted on 01.01.2008, 00:00by Gunhee Kim, Daniel F. Huber, Martial Hebert
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.