In optical see-through (OST) augmented reality (AR), displayed colors blend with the real-world scene, affecting perceived color. Studies showed that AR's color appearance depends not only on the additive chromaticity of the display and the real scene but also on ambient illumination. However, these studies often overlook changes in observer's adaptation state under varying illumination. To address this, a series of color-matching experiments between AR and display devices was conducted in an immersive lighting environment. The first experiment under a D65 illuminant found a slightly higher correlated color temperature (CCT) level of the internal white point in OST AR than in the display. Considering media's white point differences, this study proposes a three-step chromatic adaptation transform (CAT) framework to improve color appearance reproduction accuracy in AR. The second experiment used three Planckian radiators varying in CCT and two offPlanckian colorful ones to validate the proposed CAT under varying illumination, indicating that AR reached a more complete adaptation state than the display, especially under high luminance. A third validation experiment had observers rate color differences between reference and reproduced stimuli in AR. Results showed the effectiveness of our three-step CAT for cross-media color reproduction of OST AR under diverse illumination conditions.

In This paper, the information regarding the corresponding authors is missing. The corresponding authors of the paper should be Shining Ma and Weitao Song.

Current virtual reality (VR) system takes gesture interaction based on camera, handle and touch screen as one of the mainstream interaction methods, which can provide accurate gesture input for it. However, limited by application forms and the volume of devices, these methods cannot extend the interaction area to such surfaces as walls and tables. To address the above challenge, we propose AudioGest, a portable, plug-and-play system that detects the audio signal generated by finger tapping and sliding on the surface through a set of microphone devices without extensive calibration. First, an audio synthesis-recognition pipeline based on micro-contact dynamics simulation is constructed to generate modal audio synthesis from different materials and physical properties. Then the accuracy and effectiveness of the synthetic audio are verified by mixing the synthetic audio with real audio proportionally as the training sets. Finally, a series of desktop office applications are developed to demonstrate the application potential of AudioGest's scalability and versatility in VR scenarios.

Augmented reality (AR) technology has great potential in the applications of training, exhibition, and visual guidance, all of which demand precise virtual-real registration in perceived depth. Many AR applications such as navigation and tourism guidance are usually implemented in outdoor environments. However, prior research on depth perception in AR predominantly focused on the indoor environment, characterized by a lower illumination level and more confined space compared to outdoor settings. To address this gap, this paper presented a systematic investigation into the depth perception in outdoor environments. Two experiments were conducted in this study: the first one aimed to explore how to eliminate the bias induced by the floating object and how the knowledge of object height influences the perceived depth. The second experiment examined how ambient luminance affects depth estimation in AR. Our findings revealed an overestimation of perceived depth when participants were unaware of the actual height of the floating object, but an underestimation when they were informed of this information prior to the experiment. Additionally, shadows effectively reduced depth errors regardless of whether participants were informed of the object's height. The second experiment further indicated that, in outdoor environments, reducing ambient luminance significantly improves the accuracy of depth perception in AR.

Intrinsic decomposition separates an image into reflectance and shading, which contributes to image editing, augmented reality, etc. Despite recent efforts dedicated to this field, effectively separating colored illumination from reflectance and correctly restoring it into shading remains an challenge. We propose a deep intrinsic decomposition method to address this issue. Specifically, by transforming intrinsic decomposition process in RGB image domains into the combination of intensity and chromaticity domains, we propose a novel macro intrinsic decomposition network framework. This framework enables the generation of finer intrinsic components through more relevant features propagation and more detailed sub-constraints guidance. In order to expand the macro network, we integrate multiple attention mechanism modules in key positions of encoders, which enhances the extraction of distinct features. We also propose a skip connection module based on specific deep features guidance, which can filter out features that are physically irrelevant to each intrinsic component. Our method not only outperforms state-of-the-art methods across multiple datasets, but also robustly separates illumination from reflectance and restores it into shading in various types of images. By leveraging our intrinsic images, we achieve visually superior image editing effects compared to other methods, while also being able to manipulate the inherent lighting of the original scene.

The immersive augmented reality (AR) system necessitates precise depth registration between virtual objects and the real scene. Prior studies have emphasized the efficacy of surface texture in providing depth cues to enhance depth perception across various media, including the real scene, virtual reality, and AR. However, these studies predominantly focus on black-and-white textures, leaving a gap in understanding the effectiveness of colored textures. To address this gap and further explore texture-related factors in AR, a series of experiments were conducted to investigate the effects of different texture cues on depth perception using the perceptual matching method. Findings indicate that the absolute depth error increases with decreasing contrast under black-and-white texture. Moreover, textures with higher color contrast also contribute to enhanced accuracy of depth judgments in AR. However, no significant effect of texture density on depth perception was observed. The findings serve as a theoretical reference for texture design in AR, aiding in the optimization of virtual-real registration processes.

Gesture interaction is currently a main interaction technology in the field of mixed reality. However, long-term and large-scale gesture in mid-air will lead to muscle fatigue and privacy problems, which cannot meet the comfort requirements of continuous interaction and inevitably hinder the development of mixed reality systems. To solve this problem, we propose NailRing, an intelligent ring to recognize fingertip micro-gestures using a micro-close-focus camera on a fingertip bracket. Such fingertip physiological characteristics as the changes in fingertip color distribution and muscle shape changes caused by fingertip pressure have been studied. According to the recognition principle, ten types of micro-gestures have been designed and used for contact interaction and one-hand interaction respectively. The accuracy of gesture recognition (cross-session $ F_{Macro}=98.3\%$; cross-person $ F_{Macro}=86.4\%$) in user studies verifies the performances of NailRing under different interaction conditions. Finally, the capability of NailRing in a series of potential application scenarios has also been discussed and analyzed.

MOTIVATION: Although many gene set analysis methods have been proposed to explore associations between a phenotype and a group of genes sharing common biological functions or involved in the same biological process, the underlying biological mechanisms of identified gene sets are typically unexplained. RESULTS: We propose a method called Differential Regulation-based enrichment Analysis for GENe sets (DRAGEN) to identify gene sets in which a significant proportion of genes have their transcriptional regulatory patterns changed in a perturbed phenotype. We conduct comprehensive simulation studies to demonstrate the capability of our method in identifying differentially regulated gene sets. We further apply our method to three human microarray expression datasets, two with hormone treated and control samples and one concerning different cell cycle phases. Results indicate that the capability of DRAGEN in identifying phenotype-associated gene sets is significantly superior to those of four existing methods for analyzing differentially expressed gene sets. We conclude that the proposed differential regulation enrichment analysis method, though exploratory in nature, complements the existing gene set analysis methods and provides a promising new direction for the interpretation of gene expression data. AVAILABILITY AND IMPLEMENTATION: The program of DRAGEN is freely available at http://bioinfo.au.tsinghua.edu.cn/dragen/. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.