• Journal of IKEEE
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• v.26 no.3
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• pp.430-436
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• 2022

In this paper, we propose an intelligent camera for multiple body temperature detection. The proposed camera is composed of optical(4056*3040) and thermal(640*480), which detects abnormal symptoms by analyzing a person's facial expression and body temperature from the acquired image. The optical and thermal imaging cameras are operated simultaneously and detect an object in the optical image, in which the facial region and expression analysis are calculated from the object. Additionally, the calculated coordinate values from the optical image facial region are applied to the thermal image, also the maximum temperature is measured from the region and displayed on the screen. Abnormal symptom detection is determined by using the analyzed three facial expressions(neutral, happy, sadness) and body temperature values. In order to evaluate the performance of the proposed camera, the optical image processing part is tested on Caltech, WIDER FACE, and CK+ datasets for three algorithms(object detection, facial region detection, and expression analysis). Experimental results have shown 91%, 91%, and 84% accuracy scores each.

• Journal of Creative Information Culture
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• v.5 no.3
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• pp.245-253
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• 2019

To evaluate the achievements of the Solar School Project that has been implemented in twelve African countries since 2013, a case study was implemented in Kenya and in Uganda to investigate networking activities, student accessibility to computers, the frequency of student computer use, the extent to which teaching quality was improved by the enhanced accessibility to ICT-based teaching and learning practices. The results showed the followings. First, Solar Schools have significantly improved the rates of enrollment, transferring, and school attendance. Second, Solar Schools have organized local and invitational training programs to build the capacities of teachers. Third, Solar Schools have facilitated change in neighboring schools and local communities. Fourth, the participants are required to have a clear vision, take ownership of the project, and make a commitment to continuing their individual efforts toward empowerment.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.3
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• pp.150-159
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• 2022

This paper presents an implementation of detecting whether a helmet is worn and there is a fall accident through individual image analysis in real-time from extracting the image objects of several workers active in the industrial field. In order to detect image objects of workers, YOLO, a deep learning-based computer vision model, was used, and for whether a helmet is worn or not, the extracted images with 5,000 different helmet learning data images were applied. For whether a fall accident occurred, the position of the head was checked using the Pose real-time body tracking algorithm of Mediapipe, and the movement speed was calculated to determine whether the person fell. In addition, to give reliability to the result of a falling accident, a method to infer the posture of an object by obtaining the size of YOLO's bounding box was proposed and implemented. Finally, Telegram API Bot and Firebase DB server were implemented for notification service to administrators.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.1-6
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• 2023

In the realm of dental prosthesis fabrication, obtaining accurate impressions has historically been a challenging and inefficient process, often hindered by hygiene concerns and patient discomfort. Addressing these limitations, Company D recently introduced a cutting-edge solution by harnessing the potential of intraoral scan images to create 3D dental models. However, the complexity of these scan images, encompassing not only teeth and gums but also the palate, tongue, and other structures, posed a new set of challenges. In response, we propose a sophisticated real-time image segmentation algorithm that selectively extracts pertinent data, specifically focusing on teeth and gums, from oral scan images obtained through Company D's oral scanner for 3D model generation. A key challenge we tackled was the detection of the intricate molar regions, common in dental imaging, which we effectively addressed through intelligent data augmentation for enhanced training. By placing significant emphasis on both accuracy and speed, critical factors for real-time intraoral scanning, our proposed algorithm demonstrated exceptional performance, boasting an impressive accuracy rate of 0.91 and an unrivaled FPS of 92.4. Compared to existing algorithms, our solution exhibited superior outcomes when integrated into Company D's oral scanner. This algorithm is scheduled for deployment and commercialization within Company D's intraoral scanner.

• Journal of Korea Water Resources Association
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• v.49 no.6
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• pp.469-480
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• 2016

The present study aims to develop a real-time surface image velocimeter (SIV) using an Android smartphone. It can measure river surface velocity by using its built-in sensors and processors. At first the SIV system figures out the location of the site using the GPS of the phone. It also measures the angles (pitch and roll) of the device by using its orientation sensors to determine the coordinate transform from the real world coordinates to image coordinates. The only parameter to be entered is the height of the phone from the water surface. After setting, the camera of the phone takes a series of images. With the help of OpenCV, and open source computer vision library, we split the frames of the video and analyzed the image frames to get the water surface velocity field. The image processing algorithm, similar to the traditional STIV (Spatio-Temporal Image Velocimeter), was based on a correlation analysis of spatio-temporal images. The SIV system can measure instantaneous velocity field (1 second averaged velocity field) once every 11 seconds. Averaging this instantaneous velocity measurement for sufficient amount of time, we can get an average velocity field. A series of tests performed in an experimental flume showed that the measurement system developed was greatly effective and convenient. The measured results by the system showed a maximum error of 13.9 % and average error less than 10 %, when we compared with the measurements by a traditional propeller velocimeter.

• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.7
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• pp.87-94
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• 2019

Recently, the advance of accurate dynamic displacement measurement devices, such as GPS, computer vision, and optic laser sensor, has enhanced the structural monitoring technology. In this study, the dynamic displacement data was used to verify the applicability of the structural physical parameter estimation method through subspace system identification. The subspace system identification theory for estimating state-space model from measured data and physics-based interpretation for deriving the physical parameter of the estimated system are presented. Three-degree-freedom steel structures were fabricated for the experimental verification of the theory in this study. Laser displacement sensor and accelerometer were used to measure the displacement data of each floor and the acceleration data of the shaking table. Discrete state-space model generated from measured data was verified for precision. The discrete state-space model generated from the measured data extracted the floor stiffness of the building after accuracy verification. In addition, based on the story stiffness extracted from the state space model, five column stiffening and damage samples were set up to extract the change rate of story stiffness for each sample. As a result, in case of reinforcement and damage under the same condition, the stiffness change showed a high matching rate.

• Journal of KIISE:Software and Applications
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• v.36 no.10
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• pp.836-850
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• 2009

Human posture recognition has attracted tremendous attention in ubiquitous environment, performing arts and robot control so that, recently, many researchers in pattern recognition and computer vision are working to make efficient posture recognition system. However the most of existing studies is very sensitive to human variations such as the rotation or the translation of body. This is why the feature, which is extracted from the feature extraction part as the first step of general posture recognition system, is influenced by these variations. To alleviate these human variations and improve the posture recognition result, this paper presents 3D Star Skeleton and Principle Component Analysis (PCA) based feature extraction methods in the multi-view environment. The proposed system use the 8 projection maps, a kind of depth map, as an input data. And the projection maps are extracted from the visual hull generation process. Though these data, the system constructs 3D Star Skeleton and extracts the rotation invariant feature using PCA. In experimental result, we extract the feature from the 3D Star Skeleton and recognize the human posture using the feature. Finally we prove that the proposed method is robust to human variations.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.4
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• pp.90-101
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• 2012

The landmark selection is crucial to successful perform in SLAM(Simultaneous Localization and Mapping) with a mono camera. Especially, in unknown environment, automatic landmark selection is needed since there is no advance information about landmark. In this paper, proposed visual attention system which modeled human's vision system will be used in order to select landmark automatically. The edge feature is one of the most important element for attention in previous visual attention system. However, when the edge feature is used in complicated indoor area, the response of complicated area disappears, and between flat surfaces are getting higher. Also, computation cost increases occurs due to the growth of the dimensionality since it uses the responses for 4 directions. This paper suggests to use a corner feature in order to solve or prevent the problems mentioned above. Using a corner feature can also increase the accuracy of data association by concentrating on area which is more complicated and informative in indoor environments. Finally, this paper will prove that visual attention system based on corner feature can be more effective in SLAM compared to previous method by experiment.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.189-196
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• 2023

As the manufacturing paradigm shifts, various collaborative robots are creating new markets. Demand for collaborative robots is increasing in all industries for the purpose of easy operation, productivity improvement, and replacement of manpower who do simple tasks compared to existing industrial robots. However, accidents frequently occur during work caused by collaborative robots in industrial sites, threatening the safety of workers. In order to construct an industrial site through robots in a human-centered environment, the safety of workers must be guaranteed, and there is a need to develop a collaborative robot guard system that provides reliable communication without the possibility of dispatch. It is necessary to double prevent accidents that occur within the working radius of cobots and reduce the risk of safety accidents through sensors and computer vision. We build a system based on OPC UA, an international protocol for communication with various industrial equipment, and propose a collaborative robot guard system through image analysis using ultrasonic sensors and CNN (Convolution Neural Network). The proposed system evaluates the possibility of robot control in an unsafe situation for a worker.

• Journal of Internet Computing and Services
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• v.15 no.4
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• pp.33-42
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• 2014

Hand-signal is an effective communication means in the situation where voice cannot be used for expression especially for soldiers. Vision-based approaches using cameras as input devices are widely suggested in the literature. However, these approaches are not suitable for soldiers that have unseen visions in many cases. in addition, existing special-glove approaches utilize the information of fingers only. Thus, they are still lack for soldiers' hand-signal recognition that involves not only finger motions, but also additional information such as the rotation of a hand. In this paper, we have designed and implemented a new recognition system for six military hand-signal motions, i. e., 'ready', 'move', quick move', 'crawl', 'stop', and 'lying-down'. For this purpose, we have proposed a finger-recognition method and motion-recognition methods. The finger-recognition method discriminate how much each finger is bended, i. e., 'completely flattened', 'slightly flattened', 'slightly bended', and 'completely bended'. The motion-recognition algorithms are based on the characterization of each hand-signal motion in terms of the three axes. Through repetitive experiments, our system have shown 91.2% of correct recognition.