Color Contrast Enhanced Rendering for Optical See-Through Head-Mounted Displays

Personalized Dual-Level Color Grading for 360-degree Images in Virtual Reality

The rising popularity of 360-degree images and virtual reality (VR) has spurred a growing interest among creators in producing visually appealing content through effective color grading processes. Although existing computational approaches have simplified the global color adjustment for entire images with Preferential Bayesian Optimization (PBO), they neglect local colors for points of interest and are not optimized for the immersive nature of VR. In response, we propose a dual-level PBO framework that integrates global and local color adjustments tailored for VR environments. We design and evaluate a novel context-aware preferential Gaussian Process (GP) to learn contextual preferences for local colors, taking into account the dynamic contexts of previously established global colors. Additionally, recognizing the limitations of desktop-based interfaces for comparing 360-degree images, we design three VR interfaces for color comparison. We conduct a controlled user study to investigate the effectiveness of the three VR interface designs and find that users prefer to be enveloped by one 360-degree image at a time and to compare two rather than four color-graded options.

VoxAR: Adaptive Visualization of Volume Rendered Objects in Optical See-Through Augmented Reality

We present VoxAR, a method to facilitate an effective visualization of volume-rendered objects in optical see-through head-mounted displays (OST-HMDs). The potential of augmented reality (AR) to integrate digital information into the physical world provides new opportunities for visualizing and interpreting scientific data. However, a limitation of OST-HMD technology is that rendered pixels of a virtual object can interfere with the colors of the real-world, making it challenging to perceive the augmented virtual information accurately. We address this challenge in a two-step approach. First, VoxAR determines an appropriate placement of the volume-rendered object in the real-world scene by evaluating a set of spatial and environmental objectives, managed as user-selected preferences and pre-defined constraints. We achieve a real-time solution by implementing the objectives using a GPU shader language. Next, VoxAR adjusts the colors of the input transfer function (TF) based on the real-world placement region. Specifically, we introduce a novel optimization method that adjusts the TF colors such that the resulting volume-rendered pixels are discernible against the background and the TF maintains the perceptual mapping between the colors and data intensity values. Finally, we present an assessment of our approach through objective evaluations and subjective user studies.

User's image perception improved strategy and application of augmented reality systems in smart medical care: A review

BACKGROUND

Augmented reality (AR) is a new human-computer interaction technology that combines virtual reality, computer vision, and computer networks. With the rapid advancement of the medical field towards intelligence and data visualisation, AR systems are becoming increasingly popular in the medical field because they can provide doctors with clear enough medical images and accurate image navigation in practical applications. However, it has been discovered that different display types of AR systems have different effects on doctors' perception of the image after virtual-real fusion during the actual medical application. If doctors cannot correctly perceive the image, they may be unable to correctly match the virtual information with the real world, which will have a significant impact on their ability to recognise complex structures.

METHODS

This paper uses Citespace, a literature analysis tool, to visualise and analyse the research hotspots when AR systems are used in the medical field.

RESULTS

A visual analysis of the 1163 articles retrieved from the Web of Science Core Collection database reveals that display technology and visualisation technology are the key research directions of AR systems at the moment.

CONCLUSION

This paper categorises AR systems based on their display principles, reviews current image perception optimisation schemes for various types of systems, and analyses and compares different display types of AR systems based on their practical applications in the field of smart medical care so that doctors can select the appropriate display types based on different application scenarios. Finally, the future development direction of AR display technology is anticipated in order for AR technology to be more effectively applied in the field of smart medical care. The advancement of display technology for AR systems is critical for their use in the medical field, and the advantages and disadvantages of various display types should be considered in different application scenarios to select the best AR system.