  In recent years, the deep learning techniques have been applied to the
estimation of saliency maps, which represent probability density functions of
fixations when people look at the images. Although the methods of saliency-map
estimation have been actively studied for 2-dimensional planer images, the
methods for omni-directional images to be utilized in virtual environments had
not been studied, until a competition of saliency-map estimation for the
omni-directional images was held in ICME2017. In this paper, novel methods for
estimating saliency maps for the omni-directional images are proposed
considering the properties of prior distributions for fixations in the planar
images and the omni-directional images.
