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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.