• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.460-462
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• 2021

Food basically must have nutrition and safety. Recently, a number of symptoms of food poisoning occurred in a kindergarten in Ansan, where food safety was suspected. Therefore, the safety of food is more demanding. In this paper, we propose a method to inprove the detector to secure food safety. The proposed method is to learn through the network of convolution neural network (CNN) and Faster region-CNN (Faster R-CNN) and test the images of normal and foreign products. As a result of testing through a deep learning model, the method that used Faster R-CNN in parallel with the existing foreign body detector algorithm showed better detection rate than other methods.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.313-316
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• 2015

Construction is one of the most dangerous job sectors, which reports tens of thousands of time-loss injuries and deaths every year. These disasters incur delays and additional costs to the projects. The safety management needs to be on the top primary tasks throughout the construction to avoid fatal accidents and to foster safe working environments. One of the safety regulations that are frequently violated is the wearing of personal protection equipment (PPE). In order to facilitate monitoring of the compliance of the PPE wearing regulations, this paper proposes a vision based method that automatically identifies whether workers wear hard hats and safety vests. The method involves three modules - human body detection, identification of safety vest wearing, and hard hat detection. First, human bodies are detected in the video frames captured by real-time on-site construction cameras. The detected human bodies are classified into with/without wearing safety vests based on the color features of their upper parts. Finally, hard hats are detected on the nearby regions of the detected human bodies and the locations of the detected hard hats and human bodies are correlated to reveal their corresponding matches. In this way, the proposed method provides any appearance of the workers without wearing hard hats or safety vests. The method has been tested on onsite videos and the results signify its potential to facilitate site safety monitoring.

• Journal of Fashion Business
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• v.27 no.5
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• pp.78-92
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• 2023

Fashion trends are actively disseminated through social media, which influences both their propagation and consumption. This study explored how users perceive subversive basic fashion in social media videos, by examining the associated concepts and characteristics. In addition, the factors contributing to the style's social media dissemination were identified and its distinctive features were analyzed. Through text mining analysis, 80 keywords were selected for semantic network and CONCOR analysis. TF-IDF and N-gram results indicate that subversive basic fashion involves transformative design techniques such as cutting or layering garments, emphasizing the body with thin fabrics, and creating bold visual effects. Topic modeling suggests that this fashion forms a subculture that resists mainstream norms, seeking individuality by creatively transforming the existing garments. CONCOR analysis categorized the style into six groups: forward-thinking unconventional fashion, bold and unique style, creative reworking, item utilization and combination, pursuit of easy and convenient fashion, and contemporary sensibility. Consumer actions, linked to social media, were shown to involve easily transforming and pursuing personalized styles. Furthermore, creating new styles through the existing clothing is seen as an economic and creative activity that fosters network formation and interaction. This study is significant as it addresses language expression limitations and subjectivity issues in fashion image analysis, revealing factors contributing to content reproduction through user-perceived design concepts and social media-conveyed fashion characteristics.

• Journal of Electrical Engineering and information Science
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• v.1 no.2
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• pp.77-86
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• 1996

In this paper we deal with the problem of recovering 3-D motion and structure from a time-varying 2-D velocity vector field. A great deal has been done on this topic, most of which has concentrated on finding necessary and sufficient conditions for there to be a unique 3-D solution corresponding to a given 2-D motion. While previous work provides useful theoretical insight, in most situations the known algorithms have turned out to be too sensitive to be of much practical use. It appears that any robust algorithm must improve the 3-D solutions over time. As a step toward such algorithm, we present a method for recovering 3-D motion and structure from a given time-varying 2-D velocity vector field. The surface of the object in the scene is assumed to be locally planar. It is also assumed that 3-D velocity vectors are piecewise constant over three consecutive frames (or two snapshots of flow field). Our formulation relates 3-D motion and object geometry with the optical flow vector as well as its spatial and temporal derivatives. The linearization parameters, or equivalently, the first-order flow approximation (in space and time) is sufficient to recover rigid body motion and local surface structure from the local instantaneous flow field. We also demonstrate, through a sensitivity analysis carried out for synthetic and natural motions in space, that 3-D motion can be recovered reliably.

• Journal of the Korean Society of Radiology
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• v.17 no.7
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• pp.1017-1023
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• 2023

The purpose of this study was to improve the accuracy of effective atomic number (EAN) and relative electron density (RED) using a polynomial-based calibration method using dual-energy CT images. A phantom composed of 11 tissue-equivalent materials was acquired with dual-energy CT to obtain low- and high-energy images. Using the acquired dual-energy images, the ratio of attenuation of low- and high-energy images for EAN was calibrated based on Stoichiometric, Quadratic, Cubic, Quartic polynomials. EAN and RED were extracted using each calibration method. As a result of the experiment, the average error of EAN using Cubic polynomial-based calibration was minimum. Even in the RED image extracted using EAN, the error of the Cubic polynomial-based RED was minimum. Cubic polynomial-based calibration contributes to improving the accuracy of EAN and RED, and would like to contribute to accurate diagnosis of lesions in CT examinations or quantification of various materials in the human body.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.142-148
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• 2023

In Modern days, Self-driving for modern people is an absolute necessity for transportation and many other reasons. Additionally, after the outbreak of COVID-19, driving by oneself is preferred over other means of transportation for the prevention of infection. However, due to the constant exposure to stressful situations and chronic fatigue one experiences from the work or the traffic to and from it, modern drivers often drive under drowsiness which can lead to serious accidents and fatality. To address this problem, we propose a drowsy driving prevention learning model which detects a driver's state of drowsiness. Furthermore, a method to sound a warning message after drowsiness detection is also presented. This is to use MoveNet to quickly and accurately extract the keypoints of the body of the driver and Dense Neural Network(DNN) to train on real-time driving behaviors, which then immediately warns if an abnormal drowsy posture is detected. With this method, we expect reduction in traffic accident and enhancement in overall traffic safety.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.730-744
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• 2023

The topic of this paper is the recognition of human activities using egocentric vision, particularly captured by body-worn cameras, which could be helpful for video surveillance, automatic search and video indexing. This being the case, it could also be helpful in assistance to elderly and frail persons for revolutionizing and improving their lives. The process throws up the task of human activities recognition remaining problematic, because of the important variations, where it is realized through the use of an external device, similar to a robot, as a personal assistant. The inferred information is used both online to assist the person, and offline to support the personal assistant. With our proposed method being robust against the various factors of variability problem in action executions, the major purpose of this paper is to perform an efficient and simple recognition method from egocentric camera data only using convolutional neural network and deep learning. In terms of accuracy improvement, simulation results outperform the current state of the art by a significant margin of 61% when using egocentric camera data only, more than 44% when using egocentric camera and several stationary cameras data and more than 12% when using both inertial measurement unit (IMU) and egocentric camera data.

• Journal of Veterinary Clinics
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• v.41 no.2
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• pp.139-142
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• 2024

A wild Whooper swan (Cygnus Cygnus) with limping due to an injured left pelvic limb in an accident was rescued on the seashore and transferred to the Jeju Wildlife Rescue Center on November 23rd, 2020. On physical examination, its body condition score was 1 out of 5 due to starvation and dehydration. The left coxofemoral joint was also examined by careful palpating and estimating the damage. Moderated soft tissue swelling and crepitus surrounding the hip joint were confirmed. Radiography and computed tomography (CT) were used together for an accurate diagnosis of the joint. By radiographs readings, it was difficult to accurately confirm the condition of the proximal femur due to superimposition of the synsacrum and internal organs. However, signs such as avulsion fracture of the femoral head and a few fragments around the joint were revealed by CT imaging. Besides, through three-dimensional (3D) image analysis of CT, the dislocated area and condition of the left hip joint could be accurately and easily confirmed. The diagnostic process showing in this paper could be used as a good reference for diagnosing coxofemoral joint luxation in wild swan.

• Journal of the Korean Society of Radiology
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• v.82 no.6
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• pp.1441-1476
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• 2021

The tracheobronchial tree is a system of airways that allows the passage of air to aerate the lungs and entire body. Several pathological conditions can affect this anatomical region. Multidetector CT (MDCT) helps identify and characterize various large airway diseases. Post-processing tools, such as virtual bronchoscopy and automatic lung analysis, can help enhance the performance of imaging studies. In this pictorial essay review, we provide imaging findings of various bronchial lesions manifested as wall thickening and endoluminal nodules on conventional MDCT and advanced image visualization and analysis.

• Journal of the Korean Society of Radiology
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• v.84 no.6
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• pp.1233-1243
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• 2023

Percutaneous ultrasound-guided biopsy is useful for the pathologic confirmation of variable body lesions to establish diagnostic and therapeutic approaches. However, deep pelvic lesions are a challenge for pathologic diagnoses because of the presence of the bowel, bladder, major vessels, and pelvic bones which make a percutaneous approach difficult and dangerous. In female, the vagina is elastic and near the pelvic internal organs. Therefore, transvaginal ultrasound may serve as an effective and safe guide for the pathologic diagnosis of pelvis lesions. This review aimed to introduce the indications for, and the method of transvaginal ultrasoundguided biopsy, and to describe the reported diagnostic accuracy and safety.