Semantic Image Segmentation Using Visible and Near-Infrared Channels
Neda Salamati, Diane Larlus, Gabriela Csurka, Sabine Süsstrunk
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.
4th Workshop on Color and Photometry in Computer Vision at ECCV12, Florence, Italy, October 7-13, 2012.