  Recent progress in computational photography has shown that we can acquire
near-infrared (NIR) information in addition to the normal visible (RGB) band,
with only slight modifications to standard digital cameras. Due to the
proximity of the NIR band to visible radiation, NIR images share many
properties with visible images. However, as a result of the material dependent
reflection in the NIR part of the spectrum, such images reveal different
characteristics of the scene. We investigate how to effectively exploit these
differences to improve performance on the semantic image segmentation task.
Based on a state-of-the-art segmentation framework and a novel manually
segmented image database (both indoor and outdoor scenes) that contain
4-channel images (RGB+NIR), we study how to best incorporate the specific
characteristics of the NIR response. We show that adding NIR leads to improved
performance for classes that correspond to a specific type of material in both
outdoor and indoor scenes. We also discuss the results with respect to the
physical properties of the NIR response.
