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  • 1
    Online Resource
    Online Resource
    An Extended Method for Saccadic Eye Movement Measurements Using a Head-Mounted Display
    MDPI AG ; 2020
    In:  Healthcare Vol. 8, No. 2 ( 2020-04-21), p. 104-
    Details
    In: Healthcare, MDPI AG, Vol. 8, No. 2 ( 2020-04-21), p. 104-
    Abstract: Saccadic eye movement is an important ability in our daily life and is especially important in driving and sports. Traditionally, the Developmental Eye Movement (DEM) test and the King–Devick (K-D) test have been used to measure saccadic eye movement, but these only involve measurements with “adjusted time”. Therefore, a different approach is required to obtain the eye movement speed and reaction rate in detail, as some are rapid eye movements, while others are slow actions, and vice versa. This study proposed an extended method that can acquire the “rest time” and “transfer time”, as well as the “adjusted time”, by implementing a virtual reality-based DEM test, using a FOVE virtual reality (VR) head-mounted display (HMD), equipped with an eye-tracking module. This approach was tested in 30 subjects with normal vision and no ophthalmologic disease by using a 2-diopter (50-cm) distance. This allowed for measurements of the “adjusted time” and the “rest time” for focusing on each target number character, the “transfer time” for moving to the next target number character, and recording of the gaze-tracking log. The results of this experiment showed that it was possible to analyze more parameters of the saccadic eye movement with the proposed method than with the traditional methods.
    Type of Medium: Online Resource
    ISSN: 2227-9032
    URL: Article
    DOI: 10.3390/healthcare8020104
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2721009-1
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  • 2
    Online Resource
    Online Resource
    WisenetMD: Motion Detection Using Dynamic Background Region Analysis
    MDPI AG ; 2019
    In:  Symmetry Vol. 11, No. 5 ( 2019-05-03), p. 621-
    Details
    In: Symmetry, MDPI AG, Vol. 11, No. 5 ( 2019-05-03), p. 621-
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 2073-8994
    URL: Article
    DOI: 10.3390/sym11050621
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2518382-5
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  • 3
    Online Resource
    Online Resource
    3-D People Counting with a Stereo Camera on GPU Embedded Board
    MDPI AG ; 2018
    In:  Applied Sciences Vol. 8, No. 11 ( 2018-10-23), p. 2017-
    Details
    In: Applied Sciences, MDPI AG, Vol. 8, No. 11 ( 2018-10-23), p. 2017-
    Abstract: People counting in surveillance cameras is a key technology for understanding the flow population and generating heat maps. In recent years, people detection performance has been greatly improved with the development of object detection algorithms using deep learning. However, in places where people are crowded, the detection rate is low as people are often occluded by other people. We proposed a people-counting method using a stereo camera to resolve the non-detection problem due to the occlusion. We applied stereo matching to extract the depth image and convert the camera view to top view using depth information. People were detected using a height map and an occupancy map, and people were tracked and counted using a Kalman filter-based tracker. We operated the proposed method on the NVIDIA Jetson TX2 to check the real-time operation possibility on the embedded board. Experimental results showed that the proposed method had higher accuracy than the existing methods and that real-time processing is possible.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app8112017
    Language: English
    Publisher: MDPI AG
    Publication Date: 2018
    detail.hit.zdb_id: 2704225-X
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  • 4
    Online Resource
    Online Resource
    Efficient Single-Shot Multi-Object Tracking for Vehicles in Traffic Scenarios
    MDPI AG ; 2021
    In:  Sensors Vol. 21, No. 19 ( 2021-09-23), p. 6358-
    Details
    In: Sensors, MDPI AG, Vol. 21, No. 19 ( 2021-09-23), p. 6358-
    Abstract: Multi-object tracking is a significant field in computer vision since it provides essential information for video surveillance and analysis. Several different deep learning-based approaches have been developed to improve the performance of multi-object tracking by applying the most accurate and efficient combinations of object detection models and appearance embedding extraction models. However, two-stage methods show a low inference speed since the embedding extraction can only be performed at the end of the object detection. To alleviate this problem, single-shot methods, which simultaneously perform object detection and embedding extraction, have been developed and have drastically improved the inference speed. However, there is a trade-off between accuracy and efficiency. Therefore, this study proposes an enhanced single-shot multi-object tracking system that displays improved accuracy while maintaining a high inference speed. With a strong feature extraction and fusion, the object detection of our model achieves an AP score of 69.93% on the UA-DETRAC dataset and outperforms previous state-of-the-art methods, such as FairMOT and JDE. Based on the improved object detection performance, our multi-object tracking system achieves a MOTA score of 68.5% and a PR-MOTA score of 24.5% on the same dataset, also surpassing the previous state-of-the-art trackers.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s21196358
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2052857-7
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  • 5
    Online Resource
    Online Resource
    Super-Fast Computation for the Three-Asset Equity-Linked Securities Using the Finite Difference Method
    MDPI AG ; 2020
    In:  Mathematics Vol. 8, No. 3 ( 2020-02-26), p. 307-
    Details
    In: Mathematics, MDPI AG, Vol. 8, No. 3 ( 2020-02-26), p. 307-
    Abstract: In this article, we propose a super-fast computational algorithm for three-asset equity-linked securities (ELS) using the finite difference method (FDM). ELS is a very popular investment product in South Korea. There are one-, two-, and three-asset ELS. The three-asset ELS is the most popular financial product among them. FDM has been used for pricing the one- and two-asset ELS because it is accurate. However, the three-asset ELS is still priced using the Monte Carlo simulation (MCS) due to the curse of dimensionality for FDM. To overcome the limitation of dimension for FDM, we propose a systematic non-uniform grid with an explicit Euler scheme and an optimal implementation of the algorithm. The computational time is less than 6 s. We perform standard ELS option pricing and compare the results from the fast FDM with the ones from MCS. The computational results confirm the superiority and practicality of the proposed algorithm.
    Type of Medium: Online Resource
    ISSN: 2227-7390
    URL: Article
    DOI: 10.3390/math8030307
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2704244-3
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  • 6
    Online Resource
    Online Resource
    Chronic and Recurrent Herpes Zoster Ophthalmicus
    MDPI AG ; 2021
    In:  Medicina Vol. 57, No. 10 ( 2021-09-22), p. 999-
    Details
    In: Medicina, MDPI AG, Vol. 57, No. 10 ( 2021-09-22), p. 999-
    Abstract: Background and Objectives: This study sought to investigate the natural course, the chronicity and recurrence rate, and the risk factors of chronic and recurrent herpes zoster ophthalmicus (HZO). We also evaluated the effects of long-term treatment for HZO. Materials and Methods: Patients diagnosed and treated for HZO were included in the retrospective medical chart review. Multivariable-adjusted logistic and Cox regression models were used to show risk factors for chronic and recurrent HZO along with hazard ratios (HRs) and 95% confidence intervals (CIs). Results: Among a total 130 of HZO patients, 31 patients (23.85%) had chronic disease and 19 patients (14.62%) had recurrent disease. The rate of chronic disease was higher in HZO with conjunctivitis, epithelial keratitis, and stromal keratitis. The recurrence rate increased in patients with chronic HZO (HR: 34.4, 95% CI: 3.6–324.6), epithelial keratitis (HR: 5.5, 95% CI: 1.3–30.0), stromal keratitis (HR: 18.8, 95% CI: 3.0–120.8), and increased intraocular pressure (IOP) (HR: 7.3, 95% CI: 1.6–33.2). Length of systemic antiviral therapy and anti-inflammatory eyedrop treatment were not associated with recurrent HZO (p = 0.847 and p = 0.660, respectively). The most common ocular manifestation for recurrent HZO was stromal keratitis. Conclusions: This study demonstrated a considerable frequency of chronic and recurrent HZO. Chronic HZO in the form of epithelial or stromal keratitis with increased IOP provoked a significant rise in the risk of recurrence.
    Type of Medium: Online Resource
    ISSN: 1648-9144
    URL: Article
    DOI: 10.3390/medicina57100999
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2088820-X
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  • 7
    Online Resource
    Online Resource
    Design and Implementation of Defect Detection System Based on YOLOv5-CBAM for Lead Tabs in Secondary Battery Manufacturing
    MDPI AG ; 2023
    In:  Processes Vol. 11, No. 9 ( 2023-09-14), p. 2751-
    Details
    In: Processes, MDPI AG, Vol. 11, No. 9 ( 2023-09-14), p. 2751-
    Abstract: According to QYResearch, a global market research firm, the global market size of secondary batteries is growing at an average annual rate of 8.1%, but fires and casualties continue to occur due to the lack of quality and reliability of secondary batteries. Therefore, improving the quality of secondary batteries is a major factor in determining a company’s competitive advantage. In particular, lead taps, which electrically connect the negative and positive electrodes of secondary batteries, are a key factor in determining the stability of the battery. Currently, the quality inspection of secondary battery lead tab manufacturers mostly consists of visual inspection after vision inspection with a rule-based algorithm, which has limitations on the types of defects that can be detected, and the inspection time is increasing due to overlapping inspections, which is directly related to productivity. Therefore, this study aims to automate the quality inspection of lead tabs of secondary batteries by applying deep-learning-based algorithms to improve inspection accuracy, improve reliability, and improve productivity. We selected the YOLOv5 model, which, among deep-learning algorithms, has a benefit for object detection, and used the YOLOv5_CBAM model, which replaces the bottleneck part in the C3 layer of YOLOv5 with the Convolutional Block Attention Module (CBAM) based on the attention mechanism, to improve the accuracy and speed of the model. As a result of applying the YOLOv5_CBAM model, we found that the parameter was reduced by more than 50% and the performance was improved by 2%. In addition, image processing was applied to help segment the defective area to apply the SPEC value for each defective object after detection.
    Type of Medium: Online Resource
    ISSN: 2227-9717
    URL: Article
    DOI: 10.3390/pr11092751
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2720994-5
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  • 8
    Online Resource
    Online Resource
    Automatic Generation of Aerial Orthoimages Using Sentinel-2 Satellite Imagery with a Context-Based Deep Learning Approach
    MDPI AG ; 2021
    In:  Applied Sciences Vol. 11, No. 3 ( 2021-01-25), p. 1089-
    Details
    In: Applied Sciences, MDPI AG, Vol. 11, No. 3 ( 2021-01-25), p. 1089-
    Abstract: Aerial images are an outstanding option for observing terrain with their high-resolution (HR) capability. The high operational cost of aerial images makes it difficult to acquire periodic observation of the region of interest. Satellite imagery is an alternative for the problem, but low-resolution is an obstacle. In this study, we proposed a context-based approach to simulate the 10 m resolution of Sentinel-2 imagery to produce 2.5 and 5.0 m prediction images using the aerial orthoimage acquired over the same period. The proposed model was compared with an enhanced deep super-resolution network (EDSR), which has excellent performance among the existing super-resolution (SR) deep learning algorithms, using the peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and root-mean-squared error (RMSE). Our context-based ResU-Net outperformed the EDSR in all three metrics. The inclusion of the 60 m resolution of Sentinel-2 imagery performs better through fine-tuning. When 60 m images were included, RMSE decreased, and PSNR and SSIM increased. The result also validated that the denser the neural network, the higher the quality. Moreover, the accuracy is much higher when both denser feature dimensions and the 60 m images were used.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app11031089
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2704225-X
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  • 9
    Online Resource
    Online Resource
    Automated Algorithm for Removing Clutter Objects in MMS Point Cloud for 3D Road Mapping
    MDPI AG ; 2020
    In:  Sensors Vol. 20, No. 15 ( 2020-07-22), p. 4076-
    Details
    In: Sensors, MDPI AG, Vol. 20, No. 15 ( 2020-07-22), p. 4076-
    Abstract: Road information high definition maps (HD map) contain information about the facilities around the roads and are often constructed through a mobile mapping system (MMS). Although constructing an HD map is essential for road maintenance and the application of autonomous driving in the future, it is problematic to acquire the data of objects other than the facilities in an unstructured form while operating the MMS. In this study, the researchers define this object data as clutter objects and present a method of automatic removal using characteristics of the MMS and image segmentation techniques. By applying the method to 10 KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago) datasets, clutter objects were removed with an average overall accuracy of 91% with 0% (0.448%) error of commission for the complete point cloud map.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s20154076
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2052857-7
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  • 10
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    Online Resource
    Robust Extrinsic Calibration of Multiple RGB-D Cameras with Body Tracking and Feature Matching
    MDPI AG ; 2021
    In:  Sensors Vol. 21, No. 3 ( 2021-02-02), p. 1013-
    Details
    In: Sensors, MDPI AG, Vol. 21, No. 3 ( 2021-02-02), p. 1013-
    Abstract: RGB-D cameras have been commercialized, and many applications using them have been proposed. In this paper, we propose a robust registration method of multiple RGB-D cameras. We use a human body tracking system provided by Azure Kinect SDK to estimate a coarse global registration between cameras. As this coarse global registration has some error, we refine it using feature matching. However, the matched feature pairs include mismatches, hindering good performance. Therefore, we propose a registration refinement procedure that removes these mismatches and uses the global registration. In an experiment, the ratio of inliers among the matched features is greater than 95% for all tested feature matchers. Thus, we experimentally confirm that mismatches can be eliminated via the proposed method even in difficult situations and that a more precise global registration of RGB-D cameras can be obtained.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s21031013
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
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