Displays最新文献

Although there have been many previous studies on icon visual search and recognition performance, only a few have considered the effects of both the internal and external characteristics of icons. In this behavioral study, we employed a visual search task and a semantic recognition task to explore the effects of icon style, semantic distance (SD), and task difficulty on users’ performance in perceiving and identifying icons. First, we created and filtered 64 new icons, which were divided into four different groups (flat design & close SD, flat design & far SD, skeuomorphic design & close SD, skeuomorphic design & far SD) through expert evaluation. A total of 40 participants (13 men and 27 women, ages ranging from 19 to 25 years, mean age = 21.9 years, SD=1.93) were asked to perform an icon visual search task and an icon recognition task after a round of learning. Participants’ accuracy and response time were measured as a function of the following independent variables: two icon styles (flat or skeuomorphic style), two levels of SD (close or far), and two levels of task difficulty (easy or difficult). The results showed that flat icons had better visual search performance than skeuomorphic icons; this beneficial effect increased as the task difficulty increased. However, in the icon recognition task, participants’ performance in recalling skeuomorphic icons was significantly better than that in recalling flat icons. Furthermore, a strong interaction effect between icon style and task difficulty was observed for response time. As the task difficulty decreased, the difference in recognition performance between these two different icon styles increased significantly. These findings provide valuable guidance for the design of icons in human–computer interaction interfaces.

Neural Radiance Fields (NeRF) have recently emerged as a promising approach for synthesizing highly realistic images from 3D scenes. This technology has shown impressive results in capturing intricate details and producing photorealistic renderings. However, one of the limitations of traditional NeRF approaches is the difficulty in editing and manipulating the geometry of the scene once it has been captured. This restriction hinders creative freedom and practical applicability.

In this paper, we propose a method that enables interactive geometry editing for neural radiance fields manipulation. We use two proxy cages (inner cage and outer cage) to edit a scene. The inner cage defines the operation target, and the outer cage defines the adjustment space. Various operations apply to the two cages. After cage selection, operations on the inner cage lead to the desired transformation of the inner cage and adjustment of the outer cage. Users can edit the scene with translation, rotation, scaling, or combinations. The operations on the corners and edges of the cage are also supported. Our method does not need any explicit 3D geometry representations. The interactive geometry editing applies directly to the implicit neural radiance fields. Extensive experimental results demonstrate the effectiveness of our approach.

Virtual Reality (VR) sickness remains a significant challenge in the widespread adoption of VR technologies. The absence of a standardized benchmark system hinders progress in understanding and effectively countering VR sickness. This paper proposes an initial step towards a benchmark system, utilizing a novel methodological framework to serve as a common platform for evaluating contributing VR sickness factors and mitigation strategies. Our benchmark, grounded in established theories and leveraging existing research, features both small and large environments. In two research studies, we validated our system by demonstrating its capability to (1) quickly, reliably, and controllably induce VR sickness in both environments, followed by a rapid decline post-stimulus, facilitating cost and time-effective within-subject studies and increased statistical power, (2) integrate and evaluate established VR sickness mitigation methods — static and dynamic field of view reduction, blur, and virtual nose — demonstrating their effectiveness in reducing symptoms in the benchmark and their direct comparison within a standardized setting. Our proposed benchmark also enables broader, more comparative research into different technical, setup, and participant variables influencing VR sickness and overall user experience, ultimately paving the way for building a comprehensive database to identify the most effective strategies for specific VR applications.

Automated segmentation algorithms are a crucial component of medical image analysis, playing an essential role in assisting professionals to achieve accurate diagnoses. Traditional convolutional neural networks (CNNs) face challenges when dealing with complex and variable lesions: limited by the receptive field of convolutional operators, CNNs often struggle to capture long-range dependencies of complex lesions. The transformer’s outstanding ability to capture long-range dependencies offers a new perspective on addressing these challenges. Inspired by this, our research aims to combine the precise spatial detail extraction capabilities of CNNs with the global semantic understanding abilities of transformers. Unlike traditional fusion methods, we propose a fine-grained feature fusion strategy based on complementary attention, deeply exploring and complementarily fusing the feature representations of the encoder. Moreover, considering that merely relying on feature fusion might overlook critical texture details and key edge features in the segmentation task, we designed a feature enhancement module based on information entropy. This module emphasizes shallow texture features and edge information, enabling the model to more accurately capture and enhance multi-level details of the image, further optimizing segmentation results. Our method was tested on multiple public segmentation datasets of polyps and skin lesions,and performed better than state-of-the-art methods. Extensive qualitative experimental results indicate that our method maintains robust performance even when faced with challenging cases of narrow or blurry-boundary lesions.

Long-term use of desktop displays may increase the burden of the visual system and users can use low blue light mode for eye protection in terms of circadian effect. In this work, we investigated its influence from two aspects of brightness-visual effect, namely efficacy and circadian effect, and color quality, namely color difference Δu’v’ (chromaticity coordinate offset of two colors), and D_uv (deviation from blackbody locus). The decrease of brightness is accompanied by the increase of efficacy while diminishing circadian effect. The blue, cyan, and magenta have the largest Δu’v’, and the lower the saturation, the greater the Δu’v’. The lower the correlated color temperature (CCT), the greater the D_uv and the farther it deviates from the Planckian locus. We summarize three low blue light mode adjustment strategies based on red, green, and blue three-channel ratio of spectrum, and propose an optimized mode using genetic algorithm, which has two optional CCT ranges of 3500–5000 K and 2700–3000 K. Furthermore, we establish the relationship between brightness and gamut coverage to refine the screen brightness range for low blue light mode. This research provides valuable insights into low blue light mode application and their implications for human-centric healthy displays.

Human posture estimation is still a hot research topic. Previous algorithms based on traditional machine learning have difficulties in feature extraction and low fusion efficiency. To address these problems, we proposed a Transformer-based method. We combined three techniques, namely the Transformer-based feature extraction module, the multi-scale feature fusion module, and the occlusion processing mechanism, to capture the human pose. The Transformer-based feature extraction module uses the self-attention mechanism to extract key features from the input sequence, the multi-scale feature fusion module fuses feature information of different scales to enhance the perception ability of the model, and the occlusion processing mechanism can effectively handle occlusion in the data and effectively remove background interference. Our method has shown excellent performance through verification on the standard dataset Human3.6M and the wild video dataset, achieving accurate pose prediction in both complex actions and challenging samples.

In this work, we systematically investigate low-temperature polycrystalline silicon (LTPS)-based driving circuits of electronic paper for the aim of adopting small width/length ratio (W/L) of LTPS-based thin film transistors (TFTs) to reduce switch error and thus improve image sticking. Firstly, LTPS-TFTs with extremely low off-state leakage current (I_OFF) even at a large source-drain voltage (V_DS) of 30 V were obtained through detailed explorations of LTPS process technology. Meanwhile, the high on-state current (I_ON) of LTPS-TFTs also meet the requirements of fast signal writing to the storage capacitor due to their extremely high field-effect mobility (approximately 100 cm²/V⋅s), making it possible to fabricate TFTs with relatively small W/L, thereby minimizing switch error. The I_D-V_D test results reveal that the produced LTPS-TFTs can effectively withstand the maximum voltage difference of 30 V during product operation. Subsequently, the optimal W/L of the LTPS-TFT was determined through experimental results. Then, reliability test was conducted on the obtained LTPS-TFTs, revealing that the threshold voltage (V_TH) of the LTPS-TFTs shifted by 0.08 V after 7200 s under negative bias temperature stress (NBTS), and only by 0.19 V under positive bias temperature stress (PBTS). The aging test results of the aforementioned LTPS-TFTs exhibits a new physical phenomenon, that is, the I_OFF of the LTPS-TFTs has a strict matching characteristic with the aging direction. Next, we proposed a novel 2T2C driving circuit for the e-paper, which can effectively avoid the adverse effects of I_OFF on the frame holding period, and plotted it into an array layout. Finally, we combined the optimal fabricating process of the LTPS-TFTs with the 2T2C driving circuit design scheme to produce an e-paper with outstanding image sticking performance.

Augmented Reality (AR) has gained significant attention in recent years as a technology that enhances the user’s perception and interaction with the real world by overlaying virtual objects. Simultaneous Localization and Mapping (SLAM) algorithm plays a crucial role in enabling AR applications by allowing the device to understand its position and orientation in the real world while mapping the environment. This paper first summarizes AR products and SLAM algorithms in recent years, and presents a comprehensive overview of SLAM algorithms including feature-based method, direct method, and deep learning-based method, highlighting their advantages and limitations. Then provides an in-depth exploration of classical SLAM algorithms for AR, with a focus on visual SLAM and visual-inertial SLAM. Lastly, sensor configuration, datasets, and performance evaluation for AR SLAM are also discussed. The review concludes with a summary of the current state of SLAM algorithms for AR and provides insights into future directions for research and development in this field. Overall, this review serves as a valuable resource for researchers and engineers who are interested in understanding the advancements and challenges in SLAM algorithms for AR.

Light field (LF) images offer abundant spatial and angular information, therefore, the combination of which is beneficial in the performance of LF image superresolution (LF image SR). Currently, existing methods often decompose the 4D LF data into low-dimensional subspaces for individual feature extraction and fusion for LF image SR. However, the performance of these methods is restricted because of lacking effective correlations between subspaces and missing out on crucial complementary information for capturing rich texture details. To address this, we propose a cross-subspace fusion network for LF spatial SR (i.e., CSFNet). Specifically, we design the progressive cross-subspace fusion module (PCSFM), which can progressively establish cross-subspace correlations based on a cross-attention mechanism to comprehensively enrich LF information. Additionally, we propose a high-resolution adaptive enhancement group (HR-AEG), which preserves the texture and edge details in the high resolution feature domain by employing a multibranch enhancement method and an adaptive weight strategy. The experimental results demonstrate that our approach achieves highly competitive performance on multiple LF datasets compared to state-of-the-art (SOTA) methods.

Dense video captioning automatically locates events in untrimmed videos and describes event contents through natural language. This task has many potential applications, including security, assisting people who are visually impaired, and video retrieval. The related datasets constitute an important foundation for research on data-driven methods. However, the existing models for building dense video caption datasets were designed for the universal domain, often ignoring the characteristics and requirements of a specific domain. In addition, the one-way dataset construction process cannot form a closed-loop iterative scheme to improve the quality of the dataset. Therefore, this paper proposes a novel dataset construction model that is suitable for classroom-specific scenarios. On this basis, the Dense Video Caption Dataset of Student Classroom Behaviors (SCB-DVC) is constructed. Additionally, the existing dense video captioning methods typically utilize only temporal event boundaries as direct supervisory information during localization and fail to consider semantic information. This results in a limited correlation between the localization and captioning stages. This defect makes it more difficult to locate events in videos with oversmooth boundaries (due to the excessive similarity between the foregrounds and backgrounds (temporal domains) of events). Therefore, we propose a fine-grained semantic-aware assisted boundary localization-based dense video captioning method. This method enhances the ability to effectively learn the differential features between the foreground and background of an event by introducing semantic-aware information. It can provide increased boundary perception and achieve more accurate captions. Experimental results show that the proposed method performs well on both the SCB-DVC dataset and public datasets (ActivityNet Captions, YouCook2 and TACoS). We will release the SCB-DVC dataset soon.