Augmented displays (ADs), where AR extends the workspace of a physical display, are gaining momentum across productivity, visual analytics, and collaborative scenarios; yet, the field lacks a unified conceptual foundation. We present the first scoping survey on ADs, synthesizing 62 papers (2010-2024), including one earlier paper from 2005. From the corpus, we derive the AR-Display Spatial Integration Framework, capturing recurring patterns across AD systems along four core dimensions: extension type, AR content type, placement, and layout. We map existing systems across the framework to identify design patterns and translate them into recommendations. We further consolidate insights on development and evaluation practices, followed by a discussion on using the framework, AD applications, cross-cutting factors, and managing the complexity of AD systems. Our survey also outlines research gaps for advancing the field, particularly in the design space of AR-Display integration and the broader support and use of AD.

Hand actions classification is an imperative field for acquiring smart functionality in modern electronic devices because hand actions classification offers interactive and innovative methods to communicate and interact. Therefore, we develop a novel architecture based on you only looking at coefficients (YOLACT), a real-time instance segmentation approach, and a temporal relation network (TRN) for hand actions understanding. In addition, our framework consists of a face recognition-based security network (FRB-SN) for user identification. We trained the YOLACT and the TRN models using the segmented version of the 20BN jester dataset composed of hand actions images and ground truths while the FRB-SN is trained using the VGGFace2 dataset. For testing, the YOLACT is used to segment the object from the given image sequence and then passed to the TRN-trained model to predict the corresponding action. Our experimental results showed that the accuracy and frame rate of the proposed framework are competitive.

Human action recognition (HAR) is a popular topic in developing a visual analysis system because of its tremendous potential in autonomous visual analysis. However, visual analysis is a sophisticated field in computer vision because an image sequence consists of various features that do not belong to a specific action. Therefore, we present a novel architecture approach for human action recognition and localization. We dubbed it DTA, an abbreviation of the detect, track, and analyze. It is inspired by yolov3, deep-sort, and 3D convolutional neural networks. Our framework is compact in analyzing human action, and the results showed that the proposed method outperforms previous state-of-the-art methods in various aspects. Moreover, the action recognition model is developed, trained, and tested using the ROI version of the KTH dataset. The experimental results showed the accuracy of the proposed model is superior compared to other traditional methods.

Abstract Deep learning has assisted the field of single‐image super‐resolution (SR) in achieving new heights. However, the task of restoring a high‐resolution (HR) image from a highly degraded low‐resolution (LR) image is sophisticated due to poor image restoration quality. A novel and effective lightweight SR method is presented as super‐resolution via an enhanced feature block network (SREFBN) that successfully reconstructs an HR image using a corresponding LR image with a purposed deep residual block. In addition, a novel shared parameters approach in the top‐down pathway among low‐level feature maps is introduced. The experimental results prove that SREFBN achieves remarkable performance. The presented framework requires lower computational cost and outperforms many state‐of‐the‐art methods. It is also highly adaptable with low‐end devices, requiring lower multiplication and adding operations. A trade‐off comparison between the number of parameters, execution time, and accuracies is given while also showing different variations of our approach to prove the effectiveness and reliability of the shared parameters. Most importantly, the results indicate that our framework has gained state‐of‐the‐art performance on larger scales 3 and 4. Code is available at https://github.com/curzii23/SREFBN .