• Journal of Fashion Business
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• v.10 no.6
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• pp.38-51
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• 2006

In Scythian art the multitude of animal representations well illustrates the preoccupation of this nomadic people with animals in their environment. Usually only wild animals are represented. The purpose and meaning of the animal motifs used in Scythian ornaments appears that in some cases the work was intended to be purely ornamental, while many times the motifs had symbolic meaning (such as the successful dominance of the aggressor over the victim portrayed in the attack scenes). Following earlier Scythian migrations, Sarmatian animal-style art is distinguished by complex compositions in which stylized animals are depicted twisted or turned back upon themselves or in combat with other animals. Without copying nature, they accurately conveyed the essence of every beast depicted. Scythian bound the leather belts that was hanged a hook that shaped of different kinds at the end on the upper garment. Through the antique records and tombs bequests the styles of Scythian Buckles was divided into six groups, animal-shaped, animal's head shaped, animal fight-shaped, rectangle-shaped, rectangle openwork-shaped, genre scene shaped Buckle. In Korea, through the antique records and tombs bequests the styles of Buckles was horse-shaped and tiger-shaped Buckles that were influenced by scythe style.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2042-2059
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• 2019

Digital shadow puppet has traditionally relied on expensive motion capture equipments and complex design. In this paper, a low-cost driven technique is presented, that captures human pose estimation data with simple camera from real scenarios, and use them to drive virtual Chinese shadow play in a 2.5D scene. We propose a special method for extracting human pose data for driving virtual Chinese shadow play, which is called 2.5D human pose estimation. Firstly, we use the 3D human pose estimation method to obtain the initial data. In the process of the following transformation, we treat the depth feature as an implicit feature, and map body joints to the range of constraints. We call the obtain pose data as 2.5D pose data. However, the 2.5D pose data can not better control the shadow puppet directly, due to the difference in motion pattern and composition structure between real pose and shadow puppet. To this end, the 2.5D pose data transformation is carried out in the implicit pose mapping space based on self-network and the final 2.5D pose expression data is produced for animating shadow puppets. Experimental results have demonstrated the effectiveness of our new method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.771-789
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• 2019

Text detection has been a popular research topic in the field of computer vision. It is difficult for prevalent text detection algorithms to avoid the dependence on datasets. To overcome this problem, we proposed a novel unsupervised text detection algorithm inspired by bootstrap learning. Firstly, the text candidate in a novel form of superpixel is proposed to improve the text recall rate by image segmentation. Secondly, we propose a unique text sample selection model (TSSM) to extract text samples from the current image and eliminate database dependency. Specifically, to improve the precision of samples, we combine maximally stable extremal regions (MSERs) and the saliency map to generate sample reference maps with a double threshold scheme. Finally, a multiple kernel boosting method is developed to generate a strong text classifier by combining multiple single kernel SVMs based on the samples selected from TSSM. Experimental results on standard datasets demonstrate that our text detection method is robust to complex backgrounds and multilingual text and shows stable performance on different standard datasets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1728-1743
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• 2021

Three-dimensional (3D) video scenes are complex and difficult to control, especially when scene switching occurs. In this paper, we propose two algorithms based on an incremental proportional-integral-derivative (PID) algorithm and a similarity analysis between views to improve the method of bit allocation for multi-view high efficiency video coding (MV-HEVC). Firstly, an incremental PID algorithm is introduced to control the buffer "liquid level" to reduce the negative impact on the target bit allocation of the view layer and frame layer owing to the fluctuation of the buffer "liquid level". Then, using the image similarity between views is used to establish, a bit allocation calculation model for the multi-view video main viewpoint and non-main viewpoint is established. Then, a bit allocation calculation method based on hierarchical B frames is proposed. Experimental simulation results verify that the algorithm ensures a smooth transition of image quality while increasing the coding efficiency, and the PSNR increases by 0.03 to 0.82dB while not significantly increasing the calculation complexity.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.1
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• pp.41-49
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• 2022

This paper proposes a novel lane detection algorithm based on inverse perspective transformation and machine learning in lightweight embedded system. The inverse perspective transformation method is presented for obtaining a bird's-eye view of the scene from a perspective image to remove perspective effects. This method requires only the internal and external parameters of the camera without a homography matrix with 8 degrees of freedom (DoF) that maps the points in one image to the corresponding points in the other image. To improve the accuracy and speed of lane detection in complex road environments, machine learning algorithm that has passed the first classifier is used. Before using machine learning, we apply a meaningful first classifier to the lane detection to improve the detection speed. The first classifier is applied in the bird's-eye view image to determine lane regions. A lane region passed the first classifier is detected more accurately through machine learning. The system has been tested through the driving video of the vehicle in embedded system. The experimental results show that the proposed method works well in various road environments and meet the real-time requirements. As a result, its lane detection speed is about 3.85 times faster than edge-based lane detection, and its detection accuracy is better than edge-based lane detection.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.637-640
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• 2023

This paper presents multimodal image fusion with human pose for detecting abnormal human behaviors in low illumination conditions. Detecting human behaviors in low illumination conditions is challenging due to its limited visibility of the objects of interest in the scene. Multimodal image fusion simultaneously combines visual information in the visible spectrum and thermal radiation information in the long-wave infrared spectrum. We propose an abnormal event detection scheme based on the multimodal fused image and the human poses using the keypoints to characterize the action of the human body. Our method assumes that human behaviors are well correlated to body keypoints such as shoulders, elbows, wrists, hips. In detail, we extracted the human keypoint coordinates from human targets in multimodal fused videos. The coordinate values are used as inputs to train a multilayer perceptron network to classify human behaviors as normal or abnormal. Our experiment demonstrates a significant result on multimodal imaging dataset. The proposed model can capture the complex distribution pattern for both normal and abnormal behaviors.

• Fire Science and Engineering
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• v.28 no.1
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• pp.71-78
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• 2014

Complex ethical issues of Emergency Medical Techinician (EMT) out-of hospital emergency medical scene and the ER (Emergency Room) behaviors were studied. The survey was conducted by 500 EMT group members working in the field of ambulance work and general hospital and it was about their work ethics, discussions and solutions about the transferred patients, and ethics regarding Do Not Attempt Resuscitate (DNAR). The survey includes work ethics, awareness about the target job, a discussion on the transfer of patients, measures, and deathbed. Discussions about the patient's condition and diagnosis results were majorly absent during patient transportation at the emergency care scene. More than 90% of emergency care transfer were inappropriate. Sometimes, EMT working in the field facing morally unethical problems beyond their responsibility. When EMT, who can not make death diagnosis, received deathbed related DNAR issues, they gone through severe ethical conflicts. The institutional support and therapy for EMT was weak. In Korea, especially in the accident site, ethical issues education is more needed than DNAR prevalence of education and guidance. If ethics training and guidance are given to EMT, a lot of moral errors in the field can be resolved.

• Journal of Broadcast Engineering
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• v.5 no.1
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• pp.113-122
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• 2000

It is sometimes necessary to remove the captions and recover original images from video images already broadcast, When the number of images requiring such recovery is small, manual processing is possible, but as the number grows it would be very difficult to do it manually. Therefore, a method for recovering original image for the caption areas in needed. Traditional research on image restoration has focused on restoring blurred images to sharp images using frequency filtering or video coding for transferring video images. This paper proposes a method for automatically recovering original image using BMA(Block Matching Algorithm). We extract information on caption regions and scene change that is used as a prior-knowledge for recovering original image. From the result of caption information detection, we know the start and end frames of captions in video and the character areas in the caption regions. The direction for the recovery is decided using information on the scene change and caption region(the start and end frame for captions). According to the direction, we recover the original image by performing block matching for character components in extracted caption region. Experimental results show that the case of stationary images with little camera or object motion is well recovered. We see that the case of images with motion in complex background is also recovered.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.30-31
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• 2009

Early detection of schedule delay in field construction activities is vital to project management. It provides the opportunity to initiate remedial actions and increases the chance of controlling such overruns or minimizing their impacts. This entails project managers to design, implement, and maintain a systematic approach for progress monitoring to promptly identify, process and communicate discrepancies between actual and as-planned performances as early as possible. Despite importance, systematic implementation of progress monitoring is challenging: (1) Current progress monitoring is time-consuming as it needs extensive as-planned and as-built data collection; (2) The excessive amount of work required to be performed may cause human-errors and reduce the quality of manually collected data and since only an approximate visual inspection is usually performed, makes the collected data subjective; (3) Existing methods of progress monitoring are also non-systematic and may also create a time-lag between the time progress is reported and the time progress is actually accomplished; (4) Progress reports are visually complex, and do not reflect spatial aspects of construction; and (5) Current reporting methods increase the time required to describe and explain progress in coordination meetings and in turn could delay the decision making process. In summary, with current methods, it may be not be easy to understand the progress situation clearly and quickly. To overcome such inefficiencies, this research focuses on exploring application of unsorted daily progress photograph logs - available on any construction site - as well as IFC-based 4D models for progress monitoring. Our approach is based on computing, from the images themselves, the photographer's locations and orientations, along with a sparse 3D geometric representation of the as-built scene using daily progress photographs and superimposition of the reconstructed scene over the as-planned 4D model. Within such an environment, progress photographs are registered in the virtual as-planned environment, allowing a large unstructured collection of daily construction images to be interactively explored. In addition, sparse reconstructed scenes superimposed over 4D models allow site images to be geo-registered with the as-planned components and consequently, a location-based image processing technique to be implemented and progress data to be extracted automatically. The result of progress comparison study between as-planned and as-built performances can subsequently be visualized in the D4AR - 4D Augmented Reality - environment using a traffic light metaphor. In such an environment, project participants would be able to: 1) use the 4D as-planned model as a baseline for progress monitoring, compare it to daily construction photographs and study workspace logistics; 2) interactively and remotely explore registered construction photographs in a 3D environment; 3) analyze registered images and quantify as-built progress; 4) measure discrepancies between as-planned and as-built performances; and 5) visually represent progress discrepancies through superimposition of 4D as-planned models over progress photographs, make control decisions and effectively communicate those with project participants. We present our preliminary results on two ongoing construction projects and discuss implementation, perceived benefits and future potential enhancement of this new technology in construction, in all fronts of automatic data collection, processing and communication.

• Korean Journal of Environment and Ecology
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• v.18 no.1
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• pp.53-60
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• 2004

This study was carried out to investigate the effect of green area in apartment complexes to variation of temperature. The inside temperature of each site was estimated by analyzing Landsat ETM＋ image data. The factors on variation of temperature were landcover type, building density, and Normalised Difference Vegetation Index(NDVI). The results of correlation between inside temperature of apartment complex and land cover type showed that the green area ratio had negative(－) correlation and impermeable pavement ratio had positive(＋) correlation. Building-to-land ratio was not significant with inside temperature. A coefficient of correlation between the temperature value and the value of permeable pavement ratio added up green area ratio was higher than a coefficient of correlation between the temperature value and the value of permeable pavement ratio added up impermeable pavement ratio. Thus we may define that permeable pavement area decrease urban temperature with green area in apartment complex. Floor area ratio had no significant correlation with inside temperature. Inside temperature was decreased as the NDVI was increased. To establish the temperature distribution model in a development apartment complex, As the result of regression analysis between inside temperature as dependent variable and permeable pave ratio+green area ratio, green area ratio, building-to-land ratio and NDIT as independent variables, only permeable pavement ratio added up green area ratio of the independent variables was accepted fur regression equation in both two seasons and adjusted coefficient of determination was 41.4 on September, 2000 and 40.4 on June,2001.