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Climate Code Foundation
Poster presented at the VSMF Symposium held in the Unilever Centre on 2011-01-17Climate Code Foundation - who are we? A non-profit organisation founded in August 2010; our goal is to promote the public understanding of climate science, by increasing the visibility and clarity of the software used in climate science and by encouraging climate scientists to do the same, by encouraging good software development and management practices among climate scientists and by encouraging the publication of climate science software as Open Source. [http://www.climatecode.org/
Learning RGB-D Salient Object Detection using background enclosure, depth contrast, and top-down features
Recently, deep Convolutional Neural Networks (CNN) have demonstrated strong
performance on RGB salient object detection. Although, depth information can
help improve detection results, the exploration of CNNs for RGB-D salient
object detection remains limited. Here we propose a novel deep CNN architecture
for RGB-D salient object detection that exploits high-level, mid-level, and low
level features. Further, we present novel depth features that capture the ideas
of background enclosure and depth contrast that are suitable for a learned
approach. We show improved results compared to state-of-the-art RGB-D salient
object detection methods. We also show that the low-level and mid-level depth
features both contribute to improvements in the results. Especially, F-Score of
our method is 0.848 on RGBD1000 dataset, which is 10.7% better than the second
place
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