Future of Augmented and Virtual Reality in Our Social and Work Life

Kunal Baranwal; Hitesh Bhargav

International Journal of Image Processing and Pattern Recognition

Future of Augmented and Virtual Reality in Our Social and Work Life

Abstract

The future technology is expected to change not only industrial services, but also our social and work life. At the moment, virtual reality and augmented reality are the two biggest technological trends. In the past few years, there is a growing number of low cost augmented and virtual reality devices being available to the general public. With this, there is a huge potential of such devices being an important part of our lives. This research work contributes to explore how augmented reality and virtual reality will have an effect on our social and work life. The most common way to experience VR has been through smartphones and headsets since a few years ago. This has been the simplest option for people to begin utilizing VR. Compared to the market for augmented reality today, virtual reality is far more developed. To create an immersive VR experience, the necessary hardware platforms and software tools are now available. Increasingly sophisticated technology, like the Oculus Rift and 360-degree cameras, are making virtual reality experiences more widely applicable in our daily lives.

Full text

The full text of this article is available to subscribers.

On a subscribing campus you are let in by network address, with no login at all.

One-off purchase. Permanent access to this article, no account needed.

A year of online access to every article in International Journal of Image Processing and Pattern Recognition. Or choose several journals →

Article details

Future of Augmented and Virtual Reality in Our Social and Work Life

Abstract

Read more of it

Publish your article

Abstract

Related articles

AI-Based Deepfake Voice Detection Model for Hindi Language

DEVELOPING A DRIVER DROWSINESS DETECTION SYSTEM (DDDS)

A Study on CNN-Based Image Mosaicing

Advanced Classification of Diabetic Retinopathy Using GAN and Deep Residual CNN Model

Vani-Adapt: A Zero-Shot Accent Trans-Adaptation Framework for Robust Indic Speech Recognition

Enhancing Facial Expression Analysis Using CNNs: Insights from ML and Deep Learning Comparisons

Read more of it

Publish your article