Displays最新文献

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.

With the rapid advancement of artificial intelligence technology, particularly within the sphere of adolescent education, a continual emergence of new challenges and opportunities is observed. The current educational system increasingly requires the automation of teaching activities detection and evaluation, offering fresh perspectives for enhancing the quality of adolescent education. Although large-scale models are receiving significant attention in educational research, their high demand for computational resources and limitations in specific applications constrain their widespread use in analyzing educational video content, especially when handling multimodal data. Current multimodal contrastive learning methods, which integrate video, audio, and text information, have achieved certain successes in video–text retrieval tasks. However, these methods typically employ simpler weighted fusion strategies and fail to avoid noise and information redundancy. Therefore, our study proposes a novel network framework, CLIP2TF, which includes an efficient audio–visual fusion encoder. It aims to dynamically interact and integrate visual and audio features, further enhancing the visual features that may be missing or insufficient in specific teaching scenarios while effectively reducing redundant information transfer during the modality fusion process. Through ablation experiments on the MSRVTT and MSVD datasets, we first demonstrate the effectiveness of CLIP2TF in video–text retrieval tasks. Subsequent tests on teaching video datasets further proves the applicability of the proposed method. This research not only showcases the potential of artificial intelligence in the automated assessment of teaching quality but also provides new directions for research in related fields studies.

Fine-grained image captioning is a focal point in the vision-to-language task and has attracted considerable attention for generating accurate and contextually relevant image captions. Effective attribute prediction and their utilization play a crucial role in enhancing image captioning performance. Despite progress in prior attribute-related methods, they either focus on predicting attributes related to the input image or concentrate on predicting linguistic context-related attributes at each time step in the language model. However, these approaches often overlook the importance of balancing visual and linguistic contexts, leading to ineffective exploitation of semantic information and a subsequent decline in performance. To address these issues, an Independent Attribute Predictor (IAP) is introduced to precisely predict attributes related to the input image by leveraging relationships between visual objects and attribute embeddings. Following this, an Enhanced Attribute Predictor (EAP) is proposed, initially predicting linguistic context-related attributes and then using prior probabilities from the IAP module to rebalance image and linguistic context-related attributes, thereby generating more robust and enhanced attribute probabilities. These refined attributes are then integrated into the language LSTM layer to ensure accurate word prediction at each time step. The integration of the IAP and EAP modules in our proposed image captioning with the enhanced attribute predictor (ICEAP) model effectively incorporates high-level semantic details, enhancing overall model performance. The ICEAP outperforms contemporary models, yielding significant average improvements of 10.62% in CIDEr-D scores for MS-COCO, 9.63% for Flickr30K and 7.74% for Flickr8K datasets using cross-entropy optimization, with qualitative analysis confirming its ability to generate fine-grained captions.

In recent years, due to the dependence on wavelength-based light absorption and scattering, underwater photographs captured by devices often exhibit characteristics such as blurriness, faded color tones, and low contrast. To address these challenges, convolutional neural networks (CNNs) with their robust feature-capturing capabilities and adaptable structures have been employed for underwater image enhancement. However, most CNN-based studies on underwater image enhancement have not taken into account color space kernel convolution adaptability, which can significantly enhance the model’s expressive capacity. Building upon current academic research on adjusting the color space size for each perceptual field, this paper introduces a Double-Branch Multi-Perception Kernel Adaptive (DBMKA) model. A DBMKA module is constructed through two perceptual branches that adapt the kernels: channel features and local image entropy. Additionally, considering the pronounced attenuation of the red channel in underwater images, a Dependency-Capturing Feature Jump Connection module (DCFJC) has been designed to capture the red channel’s dependence on the blue and green channels for compensation. Its skip mechanism effectively preserves color contextual information. To better utilize the extracted features for enhancing underwater images, a Cross-Level Attention Feature Fusion (CLAFF) module has been designed. With the Double-Branch Multi-Perception Kernel Adaptive model, Dependency-Capturing Skip Connection module, and Cross-Level Adaptive Feature Fusion module, this network can effectively enhance various types of underwater images. Qualitative and quantitative evaluations were conducted on the UIEB and EUVP datasets. In the color correction comparison experiments, our method demonstrated a more uniform red channel distribution across all gray levels, maintaining color consistency and naturalness. Regarding image information entropy (IIE) and average gradient (AG), the data confirmed our method’s superiority in preserving image details. Furthermore, our proposed method showed performance improvements exceeding 10% on other metrics like MSE and UCIQE, further validating its effectiveness and accuracy.

Lupus Nephritis (LN) has been considered as the most prevalent form of systemic lupus erythematosus. Medical imaging plays an important role in diagnosing and treating LN, which can help doctors accurately assess the extent and extent of the lesion. However, relying solely on visual observation and judgment can introduce subjectivity and errors, especially for complex pathological images. Image segmentation techniques are used to differentiate various tissues and structures in medical images to assist doctors in diagnosis. Multi-threshold Image Segmentation (MIS) has gained widespread recognition for its direct and practical application. However, existing MIS methods still have some issues. Therefore, this study combines non-local means, 2D histogram, and 2D Renyi’s entropy to improve the performance of MIS methods. Additionally, this study introduces an improved variant of the Whale Optimization Algorithm (GTMWOA) to optimize the aforementioned MIS methods and reduce algorithm complexity. The GTMWOA fusions Gaussian Exploration (GE), Topology Mapping (TM), and Magnetic Liquid Climbing (MLC). The GE effectively amplifies the algorithm’s proficiency in local exploration and quickens the convergence rate. The TM facilitates the algorithm in escaping local optima, while the MLC mechanism emulates the physical phenomenon of MLC, refining the algorithm’s convergence precision. This study conducted an extensive series of tests using the IEEE CEC 2017 benchmark functions to demonstrate the superior performance of GTMWOA in addressing intricate optimization problems. Furthermore, this study executed an experiment using Berkeley images and LN images to verify the superiority of GTMWOA in MIS. The ultimate outcomes of the MIS experiments substantiate the algorithm’s advanced capabilities and robustness in handling complex optimization problems.