Issues In Augmented Reality Research Articles

Addressing attentional issues in augmented reality with adaptive agents: Possibilities and challenges

Recent work on augmented reality (AR) has explored the use of adaptive agents to overcome attentional issues that negatively impact task performance. However, despite positive technical evaluations, adaptive agents have shown no significant improvements to user task performance in AR. Furthermore, previous works have primarily evaluated such agents using abstract tasks. In this paper, we develop an agent that observes user behaviour and performs appropriate actions to mitigate attentional issues in a realistic sense-making task in AR. We employ mixed methods to evaluate our agent in a between-subject experiment (N=60) to understand the agent’s effect on user task performance and behaviour. While we find no significant improvements in task performance, our analysis revealed that users’ preferences and trust in the agent affected their receptiveness of the agent’s recommendations. We discuss the pitfalls of autonomous agents and highlight the need to shift from designing better Human–AI interactions to better Human–AI collaborations.

Semi-parametric color reproduction method for optical see-through head-mounted displays.

The fundamental issues in Augmented Reality (AR) are on how to naturally mediate the reality with virtual content as seen by users. In AR applications with Optical See-Through Head-Mounted Displays (OST-HMD), the issues often raise the problem of rendering color on the OST-HMD consistently to input colors. However, due to various display constraints and eye properties, it is still a challenging task to indistinguishably reproduce the colors on OST-HMDs. An approach to solve this problem is to pre-process the input color so that a user perceives the output color on the display to be the same as the input. We propose a color calibration method for OST-HMDs. We start from modeling the physical optics in the rendering and perception process between the HMD and the eye. We treat the color distortion as a semi-parametric model which separates the non-linear color distortion and the linear color shift. We demonstrate that calibrated images regain their original appearance on two OST-HMD setups with both synthetic and real datasets. Furthermore, we analyze the limitations of the proposed method and remaining problems of the color reproduction in OST-HMDs. We then discuss how to realize more practical color reproduction methods for future HMD-eye system.

Invariant Features Tracking Based on Transiently Chaotic Neural Network for Markerless Augmented Reality

Tracking and registration of camera and object is one of the most important issues in Augmented Reality (AR) systems. Markerless visual tracking technologies with image feature are used in many AR applications. Feature point based neural network image matching method has attracted considerable attention in recent years. This paper proposes an approach to feature point correspondence of image sequence based on transient chaotic neural networks. Rotation and scale invariant features are extracted from images firstly, and then transient chaotic neural network is used to perform global feature matching and perform the initialization phase of the tracking. Experimental results demonstrate the efficiency and the effectiveness of the proposed method.