Predicting Eye Fixations Using Computer Vision Techniques The goal of this work is to study mechanisms of visual attention to assist visual perception for patients suffering from agerelated macular degeneration (AMD) or retinitis pigmentosa (RP) through artificial retina devices. We present a method to predict where humans look; we extend a visual saliency model by incorporating additional features and use this model to obtain saliency maps. These are thresholded at different scales to estimate the points of an image upon which the human eye fixates as well as the exact sequence of these fixations. The sequence of fixations extracted is further used to identify the part of the image that will mostly attract visual attention. Contrary to most existing approaches our method can indicate specific coordinates for the fixation points rather than generic areas that may attract visual attention and is thus more appropriate to imitate human fixations. Our method performs marginally better than the wellknown method for saliency prediction we compare against (xe2x89x8876% accuracy) and very satisfactorily in terms of estimating the sequence of fixations upon any given image (up to 98% accuracy).

Predicting the visual saliency of the people with VIMS As is known to us, visually induced motion sickness (VIMS) is often experienced in a virtual environment. Learning the visual attention of people with VIMS contributes to related research in the field of virtual reality (VR) content design and psychology. In this paper, we first construct a saliency prediction for people with VIMS (SPPV) database, which is the first of its kind. The database consists of 80 omnidirectional images and the corresponding eye tracking data collected from 30 individuals. We analyze the performance of five stateoftheart deep neural networks (DNN)based saliency prediction algorithms with their original networks and the finetuned networks on our database. We predict the atypical visual attention of people with VIMS for the first time and obtain relatively good saliency prediction results for VIMS controls so far.