Neda Salamati, Diane Larlus, Gabriela Csurka, Sabine Süsstrunk
4th Workshop on Color and Photometry in Computer Vision at ECCV12, Florence, Italy, October 7-13, 2012.
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.
Report number: