• Journal of the Korean Society for Aviation and Aeronautics
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• v.22 no.2
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• pp.9-15
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• 2014

'Visual segment surface' means a surface that extends from the missed approach point of non precision approaches (or the decision altitude location for approaches with vertical guidance and precision approaches) to the threshold to facilitate the identification of and protection from obstacles in this visual segment of the approach. Validation is the necessary final quality assurance step in the procedure design process, prior to publication. The purpose of validation is the verification of all obstacle and navigation data, and assessment of flyability of the procedure. This paper shows how to apply the protection for the visual segment of the approach procedure, and the results of the validation for visual segment surface conducted at an airport.

• Korean Journal of Applied Biomechanics
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• v.15 no.1
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• pp.45-61
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• 2005

Stance is defined as any state in which the total mass of the body is supported by the feet. In order to maintain stance, the sum of gravito-inertial forces acting on the body must be registered by equal and opposite forces at the region of contact between the organism and the support surface. Balance is controlled by applying forces to the surface of support so as to maintain the body's center of mass vertically above the feet. for a muIti-segment organism, there can be a variety of ways in which balance can be controlled, since movements of different body segments can have similar effects on the control of balance. In general, the organism tends to have a body configuration that is aligned with gravito-inertial force when there are no external forces acting on it. If any segments of the body are not aligned with gravito-inertial force vector, a torque on that segment would tend to move the body's center of mass. The maintenance of postural stability is accomplished in humans by a complex neural control system. This requires organizing integrating and acting upon visual, vestibular, and somatosensory input, providing orientation information to the postural control system. The information necessary to control and coordinate movement is provided by the visual sense of eye position with respect to the surrounding surface layout, the vestibular sense of head orientation in the gravito-inertial space, and the somatic sense of body segment position relative to one another and to the support surface. In this study, perception and action capability was examined from various points of view. The underlying assumption of the study was that the change of postural configuration could be effected by organism, environment and task goal.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4763-4775
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• 2020

This research aims to develop a deep learning-based method that automatically detects and segments the defects on railroad surfaces to reduce the cost of visual inspection of the railroad. We developed our segmentation model by modifying a fully convolutional network model [1], a well-known segmentation model used for machine learning, to detect and segment railroad surface defects. The data used in this research are images of the railroad surface with one or more defect regions. Railroad images were cropped to a suitable size, considering the long height and relatively narrow width of the images. They were also normalized based on the variance and mean of the data images. Using these images, the suggested model was trained to segment the defect regions. The proposed method showed promising results in the segmentation of defects. We consider that the proposed method can facilitate decision-making about railroad maintenance, and potentially be applied for other analyses.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.5
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• pp.1996-2001
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• 2012

Visual inspection systems are widely proposed for the well formed surface materials like electronics parts. But the materials with ill reflection ability have many troubles when visual inspection system is introduced. We have developed a robust visual inspection system that can work well in spite of low reflection ratio and with much noise when truth model is not known in the mixed production line. A workpiece identification technique using k-means has been proposed to identify the type. Based on the identified type, a robust-to-noise segmentation method, called active contour, has been applied to segment the features from the image. Finally, Kalman filter has been applied to adapt the error variation. Experiment shows that performance is about to match the accuracy of manual measurement using projectors.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.9
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• pp.758-765
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• 2001

This paper proposes a method to automatically extract stable visual landmarks from sensory data. Given a 2D occupancy map, a mobile robot first extracts vertical line features which are distinct and on vertical planar surfaces, because they are expected to be observed reliably from various viewpoints. Since the feature information such as position and length includes uncertainty due to errors of vision and motion, the robot then reduces the uncertainty by matching the planar surface containing the features to the map. As a result, the robot obtains modeled stable visual landmarks from extracted features. This extraction process is performed on-line to adapt to an actual changes of lighting and scene depending on the robot’s view. Experimental results in various real scenes show the validity of the proposed method.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.259-260
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• 1998

In this paper, we present a 3D visualization method of medical image on PC. Using morphological method, we used to segment 2D medical images (X-ray CT, MRI). Presented method is treating in some detail two operations : dilation and erosion. Also known as an isosurface, using a constant density surface make a target organ in 3D. In the whole procedure for visualization. The medical images are implemented by using Visual C++ 5.0 in activeX and IDL(interactive data language) under PC environment.

• Korean Journal of Remote Sensing
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• v.39 no.4
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• pp.425-440
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• 2023

Monitoring water surface has become one of the most prominent areas of research in addressing environmental challenges.Accurate and automated detection of watersurface in remote sensing imagesis crucial for disaster prevention, urban planning, and water resource management, particularly for a country where water plays a vital role in human life. However, achieving precise detection poses challenges. Previous studies have explored different approaches,such as analyzing water indexes, like normalized difference water index (NDWI) derived from satellite imagery's visible or infrared bands and using k-means clustering analysis to identify land cover patterns and segment regions based on similar attributes. Nonetheless, challenges persist, notably distinguishing between waterspectralsignatures and cloud shadow or terrain shadow. In thisstudy, our objective is to enhance the precision of water surface detection by constructing a comprehensive water database (DB) using existing digital and land cover maps. This database serves as an initial assumption for automated water index analysis. We utilized 1:5,000 and 1:25,000 digital maps of Korea to extract water surface, specifically rivers, lakes, and reservoirs. Additionally, the 1:50,000 and 1:5,000 land cover maps of Korea aided in the extraction process. Our research demonstrates the effectiveness of utilizing a water DB product as our first approach for efficient water surface extraction from satellite images, complemented by our second and third approachesinvolving NDWI analysis and k-means analysis. The image segmentation and binary mask methods were employed for image analysis during the water extraction process. To evaluate the accuracy of our approach, we conducted two assessments using reference and ground truth data that we made during this research. Visual interpretation involved comparing our results with the global surface water (GSW) mask 60 m resolution, revealing significant improvements in quality and resolution. Additionally, accuracy assessment measures, including an overall accuracy of 90% and kappa values exceeding 0.8, further support the efficacy of our methodology. In conclusion, thisstudy'sresults demonstrate enhanced extraction quality and resolution. Through comprehensive assessment, our approach proves effective in achieving high accuracy in delineating watersurfaces from satellite images.

• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.4
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• pp.501-509
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• 2014

In NCW(Network Centric Warfare) environment, mobile nodes communicate through wireless link. But wireless link provides limited networking performance due to signal interferences or mobility of nodes. So it is quite challenge to acquire enough networking resources and use the resources efficiently. In this paper, we have proposed a P2P based tactical information sharing system which provides satisfactory visual information playout for mobile nodes(i.e., military personnel, vehicle,..) in NCW environment. Our proposed system consists of two components. One is caching-enabled switch which stores tactical information segments at its internal storage and then transports them to mobile nodes when require. Another is centralized scheduling algorithm which exploits networking resources more efficiently. To validate performance of proposed system, we performed series of experiments in wireless network testbed. Results show improved performance in terms of segment-missing ratio, networking resources usage, sharing time, and number of simultaneous playout mobile nodes with acceptable playout continuity(i.e., over 95%).

• The KIPS Transactions:PartA
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• v.9A no.1
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• pp.83-92
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• 2002

The segmentation of target objects from three dimensional volume images is an essential step for visualization and volume measurement. In this paper, we present a method to detect the surface of objects by improving the widely used levoy filtering for volume visualization. Using morphological operators we generate completely closed surfaces and selectively segment objects using the volume growing algorithm. The presented method was applied to 3-D artificial sphere images and angiocardiograms. We quantitatively compared this method with the conventional levoy filtering using artificial sphereimages, and the results showed that our method is better in the aspect of voxel errors. The results of visual comparison using angiocardiograms also showed that our method is more accurate. The presented method in this paper is very effective for segmentation of volume data because segmentation, visualization and measurement are frequently used together for 3-D image processing and they can be easily related in our method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.5
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• pp.619-628
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• 2009

LiDAR has capability of rapid data acquisition and provides useful information for reconstructing surface of the Earth. However, Extracting information from LiDAR data is not easy task because LiDAR data consist of irregularly distributed point clouds of 3D coordinates and lack of semantic and visual information. This thesis proposed methods for automatic extraction of buildings and 3D detail modeling using airborne LiDAR data. As for preprocessing, noise and unnecessary data were removed by iterative surface fitting and then classification of ground and non-ground data was performed by analyzing histogram. Footprints of the buildings were extracted by tracing points on the building boundaries. The refined footprints were obtained by regularization based on the building hypothesis. The accuracy of building footprints were evaluated by comparing with 1:1,000 digital vector maps. The horizontal RMSE was 0.56m for test areas. Finally, a method of 3D modeling of roof superstructure was developed. Statistical and geometric information of the LiDAR data on building roof were analyzed to segment data and to determine roof shape. The superstructures on the roof were modeled by 3D analytical functions that were derived by least square method. The accuracy of the 3D modeling was estimated using simulation data. The RMSEs were 0.91m, 1.43m, 1.85m and 1.97m for flat, sloped, arch and dome shapes, respectively. The methods developed in study show that the automation of 3D building modeling process was effectively performed.