• Journal of Broadcast Engineering
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• v.24 no.1
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• pp.67-76
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• 2019

In this paper, we propose an efficient object classification method based on semantic segmentation for multi-labeled image data. In addition to various pixel unit information and processing techniques such as color information, contour, contrast, and saturation included in image data, a detailed region in which each object is located is extracted as a meaningful unit and the experiment is conducted to reflect the result in the inference. We use a neural network that has been proven to perform well in image classification to understand which object is located where image data containing various class objects are located. Based on these researches, we aim to provide artificial intelligence services that can classify real-time detailed areas of complex images containing various objects in the future.

• Journal of The Geomorphological Association of Korea
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• v.25 no.1
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• pp.1-17
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• 2018

The characteristics and burial ages of sand sediments on the Jiduri coast in Daechung-myeon, Ongjin-gun, Incheon were investigated. Daecheong Island is the area where the characteristics of the rocky coast and sand coast are shown. Various studies have been conducted on the Okjukdong sand dune that appears in the north of the island. However, there has been no study on the sandy sedimentary topography of the Jiduri and Moraewul area in the south. The sandy sedimentary terrain of Jiduri is divided into sandy beaches, sand dunes and sand deposits along the slope including climbing dune. Overall, the depth of sandy sediments in Jiduri is not deep. The characteristics of sandy sediments and burial ages were investigated at an elevation of about 23 m above sea level at the back of Jiduli Beach and 46 m above sea level at the ridge line between Jiduri and Moraewol. From the Jiduri coast to the hillside behind, the average grain size decreases and the sorting becomes better as it moves from the intertidal zone to the beach and the foredune. This indicates the selective sand transport by the wind and can be judged by the terrain formed under the current sedimentation environment. The average grain size at the upper part of the section of JD-1 (elevation of about 23m MSL) was $1.6918{\varphi}$ of medium sand. The sorting was $0.4584{\varphi}$, skewness was -1.0491 and kurtosis was -1.2411, respectively. Particularly, the average particle size of the crosssection issomewhat uniform, but the color of the constituent material changes from brown to black. In the case of JD-2 (about 46 m MSL), the mean grain size of the section was $1.7943{\varphi}$, the sorting was 0.4931, the skewness was -1.1163, and the kurtosis was 1.2133. On the other hand, the brown and black layers of JD-1 exhibited a burial age of $0.1{\pm}0.0ka$ and the JD-2 had a burial age of $0.7{\pm}0.0ka$.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.213-218
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• 2021

In this paper, we detect and track vehicles by type based on highway daytime driving videos taken with black boxes for vehicles. In addition, we design a real-time MR contents production method that can be newly created by placing substitute videos of each type of detected vehicles in the same location as the new background video. To detect and track vehicles by type, we use the YOLO algorithm. And we also use the mask technique based on RGB color for substitute videos of each type of vehicles detected. The size of the vehicle substitute videos to be used for MR content are substituted by the same size as the area size of the detected vehicles. In this paper, we confirm that real-time MR contents design is possible as a result of experiments and simulations and believe that It will be usefully utilized in the field of VR contents.

• Journal of the Korean Orthopaedic Association
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• v.56 no.3
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• pp.277-281
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• 2021

Iliac vein compression syndrome, which results in thrombosis of the left iliac veins, was first described by May and Thurner in 1957. May-Thurner syndrome should be considered when deep vein thrombosis-like symptoms appear, especially in the left lower extremities without an invasive procedure. The authors encountered an interesting case of a middle-aged female patient, who presented with sudden pain, swelling and skin color changes to the left lower extremity after right total knee arthroplasty and was diagnosed May-Thurner syndrome by computed tomography venography. This case is of clinical significance in that the early diagnosis of May-Thurner syndrome in the left lower extremity was made, which might have been overlooked after right total knee arthroplasty. This case is reported with a review of the literature review.

• Journal of Aerospace System Engineering
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• v.16 no.5
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• pp.70-77
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• 2022

This paper proposes database generation and management system for small-pixelized airborne target recognition. The proposed system has five main features: 1) image extraction from in-flight test video frames, 2) automatic image archiving, 3) image data labeling and Meta data annotation, 4) virtual image data generation based on color channel convert conversion and seamless cloning and 5) HOG/LBP-based tiny-pixelized target augmented image data. The proposed framework is Python-based PyQt5 and has an interface that includes OpenCV. Using video files collected from flight tests, an image dataset for airborne target recognition on generates by using the proposed system and system input.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4420-4438
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• 2021

Image dehazing is an ill-posed problem which is far from being solved. Traditional image dehazing methods often yield mediocre effects and possess substandard processing speed, while modern deep learning methods perform best only in certain datasets. The haze removal effect when processed by said methods is unsatisfactory, meaning the generalization performance fails to meet the requirements. Concurrently, due to the limited processing speed, most dehazing algorithms cannot be employed in the industry. To alleviate said problems, a lightweight fast dehazing network based on a multiple scale-patch framework (MSP) is proposed in the present paper. Firstly, the multi-scale structure is employed as the backbone network and the multi-patch structure as the supplementary network. Dehazing through a single network causes problems, such as loss of object details and color in some image areas, the multi-patch structure was employed for MSP as an information supplement. In the algorithm image processing module, the image is segmented up and down for processed separately. Secondly, MSP generates a clear dehazing effect and significant robustness when targeting real-world homogeneous and nonhomogeneous hazy maps and different datasets. Compared with existing dehazing methods, MSP demonstrated a fast inference speed and the feasibility of real-time processing. The overall size and model parameters of the entire dehazing model are 20.75M and 6.8M, and the processing time for the single image is 0.026s. Experiments on NTIRE 2018 and NTIRE 2020 demonstrate that MSP can achieve superior performance among the state-of-the-art methods, such as PSNR, SSIM, LPIPS, and individual subjective evaluation.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.87-93
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• 2023

In this paper, we propose a system that accurately and efficiently determines forward head posture based on network learning by analyzing the user's skeletal posture. Forward head posture syndrome is a condition in which the forward head posture is changed by keeping the neck in a bent forward position for a long time, causing pain in the back, shoulders, and lower back, and it is known that daily posture habits are more effective than surgery or drug treatment. Existing methods use convolutional neural networks using webcams, and these approaches are affected by the brightness, lighting, skin color, etc. of the image, so there is a problem that they are only performed for a specific person. To alleviate this problem, this paper extracts the skeleton from the image and learns the data corresponding to the side rather than the frontal view to find the forward head posture more efficiently and accurately than the previous method. The results show that the accuracy is improved in various experimental scenes compared to the previous method.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.12
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• pp.509-516
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• 2022

Accurate pose prediction of objects in 3D space is an important visual recognition technique widely used in many applications such as scene understanding in both indoor and outdoor environments, robotic object manipulation, autonomous driving, and augmented reality. Most previous works for object pose estimation have the limitation that they require an exact 3D CAD model for each object. Unlike such previous works, this paper proposes a novel neural network model that can predict the poses of unknown objects based on only their RGB color images without the corresponding 3D CAD models. The proposed model can obtain depth maps required for unknown object pose prediction by using an adaptive depth estimator, AdaBins,. In this paper, we evaluate the usefulness and the performance of the proposed model through experiments using benchmark datasets.

• The Research Journal of the Costume Culture
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• v.31 no.6
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• pp.776-792
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• 2023

This study aims to analyze the traditional attire of four ethnic minority groups in Northeastern China: Daur, Ewenki, Oroqen, and Hezhen, considering their natural environment, lifestyles, and cultural influences. A literature review of academic papers, books, and online resources was conducted, along with indirect investigations through artifacts. The Daur people, being equestrian, have garments with deep slits, vibrant colors, and elaborate decorations. The Ewenki people wear clothes made of fabric in the summer and primarily deer skin in the winter, and their clothing is simple and not flashy. The Oroqen people's clothing typically has slits at the front, back, or on both sides, and they wear a waist belt. The Hezhen people, an ethnic group that primarily hunts and fishes, wear two-piece clothing with a hip-length top and pants or other fur-trimmed garments. All groups incorporate symbolic patterns influenced by Shamanism, along with animal headgear and leather shoes. We observed that the traditional costumes of ethnic minority groups in Northeastern China share many commonalities in form, but there are detailed differences in material, shape, color, and decoration due to unique geographical and climatic characteristics as well as differences in livelihood. Additionally, the structure of clothing varies depending on each tribe's shamanistic practices and lifestyle.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.1-7
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• 2023

The advent of deep learning technologies has led to the development of various medical applications, making healthcare services more convenient and effective. Among these applications, heart rate estimation is considered a vital method for assessing an individual's health. Traditional methods, such as photoplethysmography through smart watches, have been widely used but are invasive and require additional hardware. Recent advancements allow for contactless heart rate estimation through facial image analysis, providing a more hygienic and convenient approach. In this paper, we propose a lightweight methodology capable of accurately estimating heart rate in mobile environments, using a specialized 2-channel network structure based on 2D convolution. Our method considers both subtle facial movements and color changes resulting from blood flow and muscle contractions. The approach comprises two major components: an Encoder for analyzing image features and a regression layer for evaluating Blood Volume Pulse. By incorporating both features simultaneously our methodology delivers more accurate results even in computing environments with limited resources. The proposed approach is expected to offer a more efficient way to monitor heart rate without invasive technology, particularly well-suited for mobile devices.