Salamati, NedaLarlus, DianeCsurka, GabrielaSüsstrunk, Sabine2012-10-152012-10-152012-10-15201210.1007/978-3-642-33868-7_46https://infoscience.epfl.ch/handle/20.500.14299/86121Recent 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.Semantic segmentationNear-infrared imagingHigh-level classificationtext::conference output::conference proceedings::conference paper