• Imaging Science in Dentistry
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• v.45 no.1
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• pp.7-13
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• 2015

Purpose: We have developed a new method of segmenting the areas of absorbable implants and bone using region-based segmentation of micro-computed tomography (micro-CT) images, which allowed us to quantify volumetric bone-implant contact (VBIC) and volumetric absorption (VA). Materials and Methods: The simple threshold technique generally used in micro-CT analysis cannot be used to segment the areas of absorbable implants and bone. Instead, a region-based segmentation method, a region-labeling method, and subsequent morphological operations were successively applied to micro-CT images. The three-dimensional VBIC and VA of the absorbable implant were then calculated over the entire volume of the implant. Two-dimensional (2D) bone-implant contact (BIC) and bone area (BA) were also measured based on the conventional histomorphometric method. Results: VA and VBIC increased significantly with as the healing period increased (p<0.05). VBIC values were significantly correlated with VA values (p<0.05) and with 2D BIC values (p<0.05). Conclusion: It is possible to quantify VBIC and VA for absorbable implants using micro-CT analysis using a region-based segmentation method.

• The Journal of the Acoustical Society of Korea
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• v.19 no.4
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• pp.58-68
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• 2000

This paper proposes a sinusoidal modeling of polyphonic audio signals. Sinusoidal modeling which has been applied well to speech and monophonic signals cannot be applied directly to polyphonic signals because a window size for sinusoidal analysis cannot be determined over the entire signal. In addition, for high quality synthesized signal transient parts like attacks should be preserved which determines timbre of musical instrument. In this paper, a multiresolution filter bank is designed which splits the input signal into six octave-spaced subbands without aliasing and sinusoidal modeling is applied to each subband signal. To alleviate smearing of transients in sinusoidal modeling a dynamic segmentation method is applied to subbands which determines the analysis-synthesis frame size adaptively to fit time-frequency characteristics of the subband signal. The improved dynamic segmentation is proposed which shows better performance about transients and reduced computation. For various polyphonic audio signals the result of simulation shows the suggested sinusoidal modeling can model polyphonic audio signals without loss of perceptual quality.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.6
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• pp.1100-1105
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• 2006

Severe distortions of colors in the obtained images have made it difficult for color codes to expand their applications. To reduce the effect of color distortions on reading colors, it will be more desirable to statistically process as many pixels in the individual color region as possible, than relying on some regularly sampled pixels. This process may require segmentation, which usually requires edge detection. However, edges in color codes can be disconnected due tovarious distortions such as zipper effect and reflection, to name a few, making segmentation incomplete. Edge linking is also a difficult process. In this paper, a more efficient approach to reducing the effect of color distortions on reading colors, one that excludes precise edge detection for segmentation, was obtained by employing the k-means clustering algorithm. And, in detecting color codes, the properties of both six safe colors and grays were utilized. Experiments were conducted on 144, 4M-pixel, outdoor images. The proposed method resulted in a color-code detection rate of 100% fur the test images, and an average color-reading accuracy of over 99% for the detected codes, while the highest accuracy that could be achieved with an approach employing Canny edge detection was 91.28%.

• Journal of the Korea Institute of Building Construction
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• v.23 no.2
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• pp.197-207
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• 2023

Concrete is a widely used material due to its excellent compressive strength and durability. However, depending on the surrounding environment and the characteristics of the materials used in the construction, various defects may occur, such as cracks on the surface and subsidence of the structure. The detects on the surface of the concrete structure occur after completion or over time. Neglecting these cracks may lead to severe structural damage, necessitating regular safety inspections. Traditional visual inspections of concrete walls are labor-intensive and expensive. This research presents a deep learning-based semantic segmentation model designed to detect cracks in concrete walls. The model addresses surface defects that arise from aging, and an image augmentation technique is employed to enhance feature extraction and generalization performance. A dataset for semantic segmentation was created by combining publicly available and self-generated datasets, and notable semantic segmentation models were evaluated and tested. The model, specifically trained for concrete wall fracture detection, achieved an extraction performance of 81.4%. Moreover, a 3% performance improvement was observed when applying the developed augmentation technique.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.792-799
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• 2022

Building Information Modeling (BIM) technology is a key component of modern construction engineering and project management workflows. As-is BIM models that represent the spatial reality of a project site can offer crucial information to stakeholders for construction progress monitoring, error checking, and building maintenance purposes. Geometric methods for automatically converting raw scan data into BIM models (Scan-to-BIM) often fail to make use of higher-level semantic information in the data. Whereas, semantic segmentation methods only output labels at the point level without creating object level models that is necessary for BIM. To address these issues, this research proposes a hybrid semantic-geometric approach for clutter-resistant floorplan generation from laser-scanned building point clouds. The input point clouds are first pre-processed by normalizing the coordinate system and removing outliers. Then, a semantic segmentation network based on PointNet++ is used to label each point as ceiling, floor, wall, door, stair, and clutter. The clutter points are removed whereas the wall, door, and stair points are used for 2D floorplan generation. A region-growing segmentation algorithm paired with geometric reasoning rules is applied to group the points together into individual building elements. Finally, a 2-fold Random Sample Consensus (RANSAC) algorithm is applied to parameterize the building elements into 2D lines which are used to create the output floorplan. The proposed method is evaluated using the metrics of precision, recall, Intersection-over-Union (IOU), Betti error, and warping error.

• The Journal of Fisheries Business Administration
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• v.35 no.1
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• pp.23-56
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• 2004

The offshore trawl fishery is seeking its survival way to overcome current management conditions in red, resulted from the bilateral agreement with China and Japan. However, this movement magnifies conflicts between fisheries on the contrary and it is thought to be impossible to get over current situations. For all that, this study is aimed at investigating how this current situations have occurred. The management freedom as response to the change in fishing conditions of a certain fishery, in case of Korea, is affected by institutional regulations. The example of this is controls on fishing gears, fishing vessels, and fishing grounds. The most exposure of this control is a segmentation of institutional fisheries. The initial segmentation of the offshore trawl fishery in Korea was occurred in the period of Japan's colonization when the degree of use of fishing grounds was limited geographically. At that time, fisheries were divided by fishing areas, but it did not divide the fishery itself. The large - sized fishing vessels were developed politically to be more competative to Japanese fishing vessels since 1950s. During this time, the trawl fishery was merged into current Eastern trawl fishery and South - Western trawl fishery. It was also inevitable to divide into the pair trawl and single trawl fishery as a result of the physical mergency between Western trawl and Southern trawl fishery. In order to develop the trawl fishery, new licenses were issued on the shrimp trawl fishery, through which it was expected to boost the trawl fishery. As opposed, the shrimp trawl fishery was changed into the mid - sized trawl fishery, targeting on the eastern fishing areas and the large - sized trawl fishery was developed since the late of 1970s with the development of filefish processing industry. The large trawl fishery that led in development of offshore trawl fishery since the late of 1950s was started to divide into a pair trawl and single trawl according to the fishing method and capital power. It finally became an institutionally independent fishery in 1980s, respectively. Looking into these historical process, the segmentation of the trawl fishery is thought as a result of the lack of long - term perspective and as a production of trial and error resulted by unprepared policy. As a result, these segmentation of fisheries roles as critical obstacles in harmonization of fisheries and in overcoming of current situations. Therefore, the review of this institutional segmentation of the offshore trawl fishery should be taken for an optimal redistribution of fishing grounds suits with business and fishing technology. For this, the fishery must be divided into large capitalized fishery and small - mid fishery with consideration of capital, fishing method, and the condition of use of fishing grounds. In addition to this, by limiting outline of fishing ground that the large fishery can harvest, it must allow for the small - mid fishery to catch with its own boundary. Furthermore, by launching buyback programs on the trawl, eastern trawl, pair trawl, it can provide broader fishing grounds where the fishery can harvest with management freedom.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1693-1705
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• 2023

Rural areas, which account for about 90% of the country's land area, are increasing in importance and value as a space that performs various public functions. However, facilities that adversely affect residents' lives, such as livestock facilities, factories, and solar panels, are being built indiscriminately near residential areas, damaging the rural environment and landscape and lowering the quality of residents' lives. In order to prevent disorderly development in rural areas and manage rural space in a planned manner, detection and monitoring of hazardous facilities in rural areas is necessary. Data can be acquired through satellite imagery, which can be acquired periodically and provide information on the entire region. Effective detection is possible by utilizing image-based deep learning techniques using convolutional neural networks. Therefore, U-Net model, which shows high performance in semantic segmentation, was used to classify potentially hazardous facilities in rural areas. In this study, KOMPSAT ortho-mosaic optical imagery provided by the Korea Aerospace Research Institute in 2020 with a spatial resolution of 0.7 meters was used, and AI training data for livestock facilities, factories, and solar panels were produced by hand for training and inference. After training with U-Net, pixel accuracy of 0.9739 and mean Intersection over Union (mIoU) of 0.7025 were achieved. The results of this study can be used for monitoring hazardous facilities in rural areas and are expected to be used as basis for rural planning.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.508-518
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• 2023

This paper presents a new approach for the automatic mapping of discontinuities in a tunnel face based on its 3D digital model reconstructed by LiDAR scan or photogrammetry techniques. The main idea revolves around the identification of discontinuity areas in the 3D digital model of a tunnel face by segmenting its 2D projected images using a deep-learning semantic segmentation model called U-Net. The proposed deep learning model integrates various features including the projected RGB image, depth map image, and local surface properties-based images i.e., normal vector and curvature images to effectively segment areas of discontinuity in the images. Subsequently, the segmentation results are projected back onto the 3D model using depth maps and projection matrices to obtain an accurate representation of the location and extent of discontinuities within the 3D space. The performance of the segmentation model is evaluated by comparing the segmented results with their corresponding ground truths, which demonstrates the high accuracy of segmentation results with the intersection-over-union metric of approximately 0.8. Despite still being limited in training data, this method exhibits promising potential to address the limitations of conventional approaches, which only rely on normal vectors and unsupervised machine learning algorithms for grouping points in the 3D model into distinct sets of discontinuities.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.14 no.7
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• pp.769-776
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• 2003

This paper presents analysis and design of microstrip Rotman lens operating over wide band and wide steering angle by the contour integral method along with the segmentation method. All mutual coupling, internal reflections between ports and the discontinuity at every junction are taken into account. Equally spaced ports are designed and realized, which make suppress output ripple through the array ports. Impedance matching and mutual coupling between ports are analyzed and optimized using 12 input and 12 output exponential tapers. The measured results of fabricated lens show ${\pm}$ 1.8 dB insertion loss deviation over 6∼18 GHz wide frequency range and beam steering accuracy less than 1$^{\circ}$over ${\pm}$53$^{\circ}$angle and agrees well with the analysis results.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.18-21
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• 2008

Lee (2008) proposed the pansharpening method to reconstruct at the higher resolution the multispectral images which agree with the spectral values observed from the sensor of the lower resolution values. It outperformed over several current techniques for the statistical analysis with quantitative measures, and generated the imagery of good quality for visual interpretation. However, if a small object stretches over two adjacent pixels with different spectral characteristics at the lower resolution, the pixels of the object at the higher resolution may have different multispectral values according to their location even though they have a same intensity in the panchromatic image of higher resolution. To correct this problem, this study employed an iterative technique similar to the image restoration scheme of Point-Jacobian iterative MAP estimation. The effect of pansharpening on image segmentation/classification was assessed for various techniques. The method was applied to the IKONOS image acquired over the area around Anyang City of Korea.