Semantic Image Segmentation Using Visible and Near-Infrared Channels
Abstract
Recent progress in computational photography has shown that we can acquire physical information beyond visible (RGB) image representations. In particular, we can acquire near-infrared (NIR) cues with only slight modification to any standard digital camera. In this paper, we study whether this extra channel can improve semantic image segmentation. Based on a state-of-the-art segmentation framework and a novel manually segmented image database that contains 4-channel images (RGB+NIR), we study how to best incorporate the specific characteristics of the NIR response. We show that it leads to improved performances for 7 classes out of 10 in the proposed dataset and discuss the results with respect to the physical properties of the NIR response.
Cite
Text
Salamati et al. "Semantic Image Segmentation Using Visible and Near-Infrared Channels." European Conference on Computer Vision, 2012. doi:10.1007/978-3-642-33868-7_46Markdown
[Salamati et al. "Semantic Image Segmentation Using Visible and Near-Infrared Channels." European Conference on Computer Vision, 2012.](https://mlanthology.org/eccv/2012/salamati2012eccv-semantic/) doi:10.1007/978-3-642-33868-7_46BibTeX
@inproceedings{salamati2012eccv-semantic,
title = {{Semantic Image Segmentation Using Visible and Near-Infrared Channels}},
author = {Salamati, Neda and Larlus, Diane and Csurka, Gabriela and Süsstrunk, Sabine},
booktitle = {European Conference on Computer Vision},
year = {2012},
pages = {461-471},
doi = {10.1007/978-3-642-33868-7_46},
url = {https://mlanthology.org/eccv/2012/salamati2012eccv-semantic/}
}