Semantic Image Segmentation Using Visible and Near-Infrared Channels

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.


Published in:
Lecture Notes in Computer Science, 7584, 461-471
Presented at:
4th Workshop on Color and Photometry in Computer Vision at ECCV12, Florence, Italy, October 7-13, 2012
Year:
2012
Publisher:
Springer Berlin Heidelberg
ISSN:
0302-9743
Keywords:
Note:
The RGB+NIR outdoor dataset and corresponding annotation (721M): https://drive.google.com/file/d/0B-GdymjYPNgPZFJ1UEM4dEo1Tkk/edit?usp=sharing
Laboratories:




 Record created 2012-10-15, last modified 2018-03-17

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