In the education process, students face problems with understanding due to the complexity, necessity of abstract thinking and concepts. More and more educational centres around the world have started to introduce powerful new technology-based tools that help meet the needs of the diverse student population. Over the last several years, virtual reality (VR) has moved from being the purview of gaming to professional development. It plays an important role in teaching process, providing an interesting and engaging way of acquiring information. What follows is an overview of the big trend, opportunities and concerns associated with VR in education. We present new opportunities in VR and put together the most interesting, recent virtual reality applications used in education in relation to several education areas such as general, engineering and health-related education. Additionally, this survey contributes by presenting methods for creating scenarios and different approaches for testing and validation. Lastly, we conclude and discuss future directions of VR and its potential to improve the learning experience.

In this letter, a new satellite image contrast enhancement technique based on the discrete wavelet transform (DWT) and singular value decomposition has been proposed. The technique decomposes the input image into the four frequency subbands by using DWT and estimates the singular value matrix of the low-low subband image, and, then, it reconstructs the enhanced image by applying inverse DWT. The technique is compared with conventional image equalization techniques such as standard general histogram equalization and local histogram equalization, as well as state-of-the-art techniques such as brightness preserving dynamic histogram equalization and singular value equalization. The experimental results show the superiority of the proposed method over conventional and state-of-the-art techniques.

Understanding visual attention has always been a topic of great interest in the graphics, image/video processing, robotics and human-computer interaction communities. By understanding salient image regions, the compression, transmission and rendering algorithms can be optimized. This is particularly important in omnidirectional images (ODIs) viewed with a head-mounted display (HMD), where only a fraction of the captured scene is displayed at a time, namely viewport. In order to predict salient image regions, saliency maps are estimated either by using an eye tracker to collect eye fixations during subjective tests or by using computational models of visual attention. However, eye tracking developments for ODIs are still in the early stages and although a large list of saliency models are available, no particular attention has been dedicated to ODIs. Therefore, in this paper, we consider the problem of estimating saliency maps for ODIs viewed with HMDs, when the use of an eye tracker device is not possible. We collected viewport center trajectories (VCTs) of 32 participants for 21 ODIs and propose a method to transform the gathered data into saliency maps. The obtained saliency maps are compared in terms of image exposition time used to display each ODI in the subjective tests. Then, motivated by the equator bias tendency in ODIs, we propose a post-processing method, namely fused saliency maps (FSM), to adapt current saliency models to ODIs requirements. We show that the use of FSM on current models improves their performance by up to 20%. The developed database and testbed are publicly available with this paper.

"Fast track article for IS&T International Symposium on Electronic Imaging 2019: Image Quality and System Performance XVI proceedings."

Understanding of visual attention is crucial for omnidirectional video (ODV) viewed for instance with a head-mounted display (HMD), where only a fraction of an ODV is rendered at a time. Transmission and rendering of ODV can be optimized by understanding how viewers consume a given ODV in virtual reality (VR) applications. In order to predict video regions that might draw the attention of viewers, saliency maps can be estimated by using computational visual attention models. As no such model currently exists for ODV, but given the importance for emerging ODV applications, we create a new visual attention user dataset for ODV, investigate behavior of viewers when consuming the content, and analyze the prediction performance of state-of-the-art visual attention models. Our developed test-bed and dataset will be publicly available with this paper, to stimulate and support research on ODV.