  Animals have evolved to restrict their sensing capabilities to certain region
of electromagnetic spectrum. This is surprisingly a very narrow band on a vast
scale which makes one think if there is a systematic bias underlying such
selective filtration. The situation becomes even more intriguing when we find a
sharp cutoff point at Near-infrared point whereby almost all animal vision
systems seem to have a lower bound. This brings us to an interesting question:
did evolution "intentionally" performed such a restriction in order to evolve
higher visual cognition? In this work this question is addressed by
experimenting with Near-infrared images for their potential applicability in
higher visual processing such as semantic segmentation. A modified version of
Fully Convolutional Networks are trained on NIR images and RGB images
respectively and compared for their respective effectiveness in the wake of
semantic segmentation. The results from the experiments show that visible part
of the spectrum alone is sufficient for the robust semantic segmentation of the
indoor as well as outdoor scenes.
