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Animals have evolved to restrict their sensing capabilities to certain regionof electromagnetic spectrum. This is surprisingly a very narrow band on a vastscale which makes one think if there is a systematic bias underlying suchselective filtration. The situation becomes even more intriguing when we find asharp cutoff point at Near-infrared point whereby almost all animal visionsystems seem to have a lower bound. This brings us to an interesting question:did evolution "intentionally" performed such a restriction in order to evolvehigher visual cognition? In this work this question is addressed byexperimenting with Near-infrared images for their potential applicability inhigher visual processing such as semantic segmentation. A modified version ofFully Convolutional Networks are trained on NIR images and RGB imagesrespectively and compared for their respective effectiveness in the wake ofsemantic segmentation. The results from the experiments show that visible partof the spectrum alone is sufficient for the robust semantic segmentation of theindoor as well as outdoor scenes.

Did Evolution get it right? An evaluation of Near-Infrared imaging in semantic scene segmentation using deep learning