Augmented reality is one of the fastest growing fields, receiving increased funding for the last few years as people realise the potential benefits of rendering virtual information in the real world. Most of today’s augmented reality marker-based applications use local feature detection and tracking techniques. The disadvantage of applying these techniques is that the markers must be modified to match the unique classified algorithms or they suffer from low detection accuracy. Machine learning is an ideal solution to overcome the current drawbacks of image processing in augmented reality applications. However, traditional data annotation requires extensive time and labour, as it is usually done manually. This study incorporates machine learning to detect and track augmented reality marker targets in an application using deep neural networks. We firstly implement the auto-generated dataset tool, which is used for the machine learning dataset preparation. The final iOS prototype application incorporates object detection, object tracking and augmented reality. The machine learning model is trained to recognise the differences between targets using one of YOLO’s most well-known object detection methods. The final product makes use of a valuable toolkit for developing augmented reality applications called ARKit.

This article describes technical basics and applications of graphically interactive and online Virtual Reality (VR) frameworks. It automatically extracts and displays left and right stereo images from the Internet search engines, e.g., Google Image Search. Within a short waiting time, many 3D related results are returned to the users regarding aligned left and right stereo photos; these results are viewable through VR glasses. The system automatically filters different types of available 3D data from redundant pictorial datasets on the public networks (the Internet). To reduce possible copyright issues, only the search for images that are “labelled for reuse” is performed; meaning that the obtained pictures can be used for any purpose, in any area, without being modified. The system then automatically specifies if the picture is a side-by-side stereo pair, an anaglyph, a stereogram, or just a “normal” 2D image (not optically 3D viewable). The system then generates a stereo pair from the collected dataset, to seamlessly display 3D visualisation on State-of-the-art VR devices such as the low-cost Google Cardboard, Samsung Gear VR or Google Daydream. These devices are used to provide an immediate, controllable 3D display. In this article, we propose an image type classification technique that dynamically extracts co-aligned stereo pairs with rich 3D visualisation to VR viewers. This system is portable, simple to set up and operate. From some initial experiment results; our system is shown to be relatively fast, accurate, and easy to implement. With such system, Internet users all over theWorld could easily visualise millions of real life stereo datasets publicly available on the Internet; which are believed to be useful for VR testing and learning purposes.

For decades, researchers have been trying to create intuitive virtual environments by blending reality and virtual reality, thus enabling general users to interact with the digital domain as easily as with the real world. The result is “augmented reality” (AR). AR seamlessly superimposes virtual objects on to a real environment in three dimensions (3D) and in real time. One of the most important parts that helps close the gap between virtuality and reality is the marker used in the AR system. While pictorial marker and bar-code marker are the two most commonly used marker types in the market, they have some disadvantages in visual and processing performance. In this paper, we present a novelty method that combines the bar-code with the original feature of a colour picture (e.g., photos, trading cards, advertisement’s figure). Our method decorates on top of the original pictorial images additional features with a single stereogram image that optically conceals a multi-level (3D) bar-code. Thus, it has a larger capability of storing data compared to the general 1D barcode. This new type of marker has the potential of addressing the issues that the current types of marker are facing. It not only keeps the original information of the picture but also contains encoded numeric information. In our limited evaluation, this pictorial bar-code shows a relatively robust performance under various conditions and scaling; thus, it provides a promising AR approach to be used in many applications such as trading card games, educations, and advertisements.