In this paper, we propose a method for calculating the dynamic background region in a video and removing false positives in order to overcome the problems of false positives that occur due to the dynamic background and frame drop at slow speeds. Therefore, we need an efficient algorithm with a robust performance value including processing speed. The foreground is separated from the background by comparing the similarities between false positives and the foreground. In order to improve the processing speed, the median filter was optimized for the binary image. The proposed method was based on a CDnet 2012/2014 dataset and we achieved precision of 76.68%, FPR of 0.90%, FNR of 18.02%, and an F-measure of 75.35%. The average ranking across categories is 14.36, which is superior to the background subtraction method. The proposed method was operated at 45 fps (CPU), 150 fps (GPU) at 320 × 240 resolution. Therefore, we expect that the proposed method can be applied to current commercialized CCTV without any hardware upgrades.

Recently, holographic display and computer-generated holograms calculated from real existing objects have been more actively investigated to support holographic video applications. In this paper, we proposed a method of generating 360-degree color holograms of real 3D objects in an efficient manner. 360-degree 3D images are generated using the actual 3D image acquisition system consisting of a depth camera and a turntable and intermediate view generation. Then, 360-degree color holograms are calculated using a viewing-window-based computer-generated hologram. We confirmed that floating 3D objects are faithfully reconstructed around a 360-degree direction using our 360-degree tabletop color holographic display.

Surface electromyography (sEMG) sensors play a critical role in diagnosing muscle conditions and enabling prosthetic device control, especially for lower extremity robotic legs. However, challenges arise when utilizing such sensors on residual limbs within a silicon liner worn by amputees, where dynamic pressure, narrow space, and perspiration can negatively affect sensor performance. Existing commercial sEMG sensors and newly developed sensors are unsuitable due to size and thickness, or susceptible to damage in this environment. In this paper, our sEMG sensors are tailored for amputees wearing sockets, prioritizing breathability, durability, and reliable recording performance. By employing porous PDMS and Silbione substrates, our design achieves exceptional permeability and adhesive properties. The serpentine electrode pattern and design are optimized to improve stretchability, durability, and effective contact area, resulting in a higher signal-to-noise ratio (SNR) than conventional electrodes. Notably, our proposed sensors wirelessly enable to control of a robotic leg for amputees, demonstrating its practical feasibility and expecting to drive forward neuro-prosthetic control in the clinical research field near future.

Image-based three-dimensional (3D) reconstruction is a process of extracting 3D information from an object or entire scene while using low-cost vision sensors. A structure-from-motion coupled with multi-view stereo (SFM-MVS) pipeline is a widely used technique that allows 3D reconstruction from a collection of unordered images. The SFM-MVS pipeline typically comprises different processing steps, including feature extraction and feature matching, which provide the basis for automatic 3D reconstruction. However, surfaces with poor visual texture (repetitive, monotone, etc.) challenge the feature extraction and matching stage and affect the quality of reconstruction. The projection of image patterns while using a video projector during the image acquisition process is a well-known technique that has been shown to be successful for such surfaces. In this study, we evaluate the performance of different feature extraction methods on texture-less surfaces with the application of synthetically generated noise patterns (images). Seven state-of-the-art feature extraction methods (HARRIS, Shi-Tomasi, MSER, SIFT, SURF, KAZE, and BRISK) are evaluated on problematic surfaces in two experimental phases. In the first phase, the 3D reconstruction of real and virtual planar surfaces evaluates image patterns while using all feature extraction methods, where the patterns with uniform histograms have the most suitable morphological features. The best performing pattern from Phase One is used in Phase Two experiments in order to recreate a polygonal model of a 3D printed object using all of the feature extraction methods. The KAZE algorithm achieved the lowest standard deviation and mean distance values of 0.0635 mm and −0.00921 mm, respectively.

Blink rate, a major physiological response in humans, directly affects ocular diseases such as keratitis and dry eye. The blink rate in normal eyes appears at a constant frequency of 6–30 times per minute and is constant for each individual. In a previous study, the blink rate decreased when viewing content with high intensity and realism. Therefore, we tried to investigate the change in blink rate when viewing the content in VR HMD (virtual reality head-mounted display) and AR (augmented reality) glasses environments. We compared and analyzed the blink rate in four environments: natural state, viewing monitor, viewing VR HMD, and viewing AR glasses. Twenty-one participants (age, 26.87 ± 3.31 years) viewed the content for 1 min in four environments. One-way repeated ANOVA was used to analyze the blink rate changes. The study showed that the blink rate was decreased in the monitor, VR HMD, and AR glasses environments compared to that in the natural environment. Comparing the VR HMD environment with the AR glasses environment showed that the blink rate decreased in the VR HMD environment. The results of this study can be used for content use safety recommendations (guidelines for safe use of contents due to decreased blink rate) in the VR HMD and AR glasses environments, which are currently attracting attention in the metaverse.

The eye blink rate, a major human physiological response, directly affects ocular diseases, such as keratitis and dry eye syndrome. It has been shown that the eye blink rate in normal eyes has a certain frequency for individuals, from 6–30 times/min. It was suggested in a previous study that the eye blink rate can be decreased during the viewing of high-intensity and realistic content. Therefore, in this paper, we examine the change of the eye blink rate during the HMD (head-mounted display) viewing of VR (virtual reality) contents; accordingly, we propose an algorithm to measure the eye blink rate as well as compare and analyze this rate in three different environments (natural, monitor, and HMD). We confirmed that IPD (interpupillary distance) and phoria affected the eye blink rate in each environment. In this experiment, 21 subjects (28.38 ± 6.87 years) were selected, and a paired t-test was performed for changes in the eye blink rate over 1 min for each environment. The IPD and phoria effects on the eye blink rate were confirmed using the Spearman’s correlation coefficient. In this experiment, the eye blink rate was decreased in the monitor and HMD environments compared with the natural environment, while that in the HMD environment was decreased compared with the monitor environment. The results of the correlation analysis of far IPD and the eye blink rate show no statistical significance or correlation. The correlation analysis of near IPD and the eye blink rate showed a strong positive correlation of the eye blink rate in the monitor environment. The correlation analysis of distance phoria and the eye blink rate showed a strong negative correlation of the eye blink rate in the HMD environment. The correlation analysis of near-field phoria and the eye blink rate showed a strong negative correlation of the eye blink rate in the HMD environment. It is expected that the results of this study will be used as a VR-viewing recommendation.