• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.74-74
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• 2002

This paper describes a corner matching technique for automatic relative orientation. Automatically matched corner points from stereo aerial images are used to a data set and help to improve automation of relative orientation process. A general corner matching process of overall image to image has very heavy operation and repetitive computation, so very time-consuming. But aerial stereo images are approximately seventy percent overlapped and little rotated. Based this hypothesis, we designed a sectional corner matching technique calculating correlation section by section between stereo images. Although the overlap information is not accurate, if we know it approximately, the matching process can be lighter. Since the size of aerial image is very large, corner extraction process is performed hierarchically by creating image pyramid, and corners extracted are refined at the higher level image. Extracted corners at the final step are matched section by section. Matched corners are filtered using positional information and their relation and distribution. Filtering process is applied over several steps because the thing affecting to get good result-good relative orientation parameter- is not the number of matched corner points but the accuracy of them. Filtered data is filtered one more during the process calculating relative orientation parameters. When the process is finished, we can get the well matched corner points and the refined Von-Gruber areas besides relative orientation parameters. This sectional corner matching technique is efficient by decreasing unnecessarily repetitive operations and contributes to improve automation for relative orientation.

• Journal of Information Management
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• v.41 no.3
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• pp.155-173
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• 2010

With the advanced learning and the diversity of topics, researchers on each area keenly feel the need of precise and a quick discovery of required information at any time. This study presents a way of constructing the automatic mapping system that can compare and analyze duplicated data and that describes the result by building an effective reference extraction method and another way of correcting the wrong form of used Chinese characters with Traditional Korean Medicine dictionary. With this innovation, data duplication on references and Chinese characters errors can be fixed. Under the situation that a number of references of newly published papers that can continuously be extracted.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.104-105
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• 2023

해양 관측과 위성 원격탐사를 이용하여 시공간적으로 다양하게 변하는 생태 어장 환경 및 선박 관련 자료를 획득할 수 있다. 이번 연구의 주요 목적은 야간 불빛 위성 자료를 이용하여 광범위한 해역에 대한 어선의 위치 분포를 파악하는 딥러닝 기반 모델을 제안하는 것이다. 제안한 모델의 정확성을 평가하기 위해 야간 조업 어선의 위치를 포함하고 있는 AIS(Automatic Identification System) 정보와 상호 비교 평가 하였다. 이를 위해, 먼저 AIS 자료를 획득 및 분석하는 방법을 소개한다. 해양안전종합시스템(General Information Center on Maritime Safety & Security, GICOMS)으로부터 제공받은 AIS 자료는 동적정보와 정적정보로 나뉜다. 동적 정보는 일별 자료로 구분되어있으며, 이 정보에는 해상이동업무식별번호(Maritime Mobile Service Identity, MMSI), 선박의 시간, 위도, 경도, 속력(Speed over Ground, SOG), 실침로(Course over Ground, COG), 선수방향(Heading) 등이 포함되어 있다. 정적정보는 1개의 파일로 구성되어 있으며, 선박명, 선종 코드, IMO Number, 호출부호, 제원(DimA, DimB, DimC, Dim D), 홀수, 추정 톤수 등이 포함되어 있다. 이번 연구에서는 선박의 정보에서 어선의 정보를 추출하여 비교 자료로 사용하였으며, 위성 자료는 구름의 영향이 없는 깨끗한 날짜의 영상 자료를 선별하여 사용하였다. 야간 불빛 위성 자료, 구름 정보 등을 이용하여 야간 조업 어선의 불빛을 감지하는 심층신경망(Deep Neural Network; DNN) 기반 모델을 제안하였다. 본 연구의결과는 야간 어선의 분포를 감시하고 한반도 인근 어장을 보호하는데 기여할 것으로 기대된다.

• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.837-848
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• 2018

Monitoring technology of machining has a long history since unmanned machining was introduced. Despite the long history, many researchers have presented new approaches continuously in this area. Sound based machine fault diagnosis is the process consisting of detecting automatically the damages that affect the machines by analyzing the sounds they produce during their operating time. The collected sound is corrupted by the surrounding work environment. Therefore, the most important part of the diagnosis is to find hidden elements inside the data that can represent the error pattern. This paper presents a feature extraction methodology that combines various digital signal processing and pattern recognition methods for the analysis of the sounds produced by tools. The magnitude spectrum of the sound is extracted using the Fourier analysis and the band-pass filter is applied to further characterize the data. Statistical functions are also used as input to the nonlinear classifier for the final response. The results prove that the proposed feature extraction method accurately captures the hidden patterns of the sound generated by the tool, unlike the conventional features. Therefore, it is shown that the proposed method can be applied to a sound based automatic diagnosis system.

• Journal of Korean Society for Geospatial Information Science
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• v.13 no.2 s.32
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• pp.65-69
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• 2005

Efficient 3D generation of cultural features, such as buildings in urban area is becoming increasingly important for a number of GIS applications. For reconstruction or 3D building in urban area aerial images, satellite images, LIDAR data have been used mainly. In case of automatically extracting and reconstructing of building height using single aerial images or single satellite images, there are a lot of problems, such as mismatching that result from a geometric distortion of optical images. Therefore, researches or integrating optical images and existing 2D GIS data(e.g. digital map) has been in progress. In this paper, we focused on extracting of building height by means or interest points and vortical line locus for reducing matching points. Also we used digital plotter in order to validate for the results in this study using aerial images(1/5,000) and existing digital map(1/1,000).

• Journal of Korea Spatial Information System Society
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• v.1 no.1 s.1
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• pp.5-18
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• 1999

Recently, the necessity of automatic knowledge extraction from spatial data stored in spatial databases has been increased. Spatial data mining can be defined as the extraction of implicit knowledge, spatial relationships, or other knowledge not explicitly stored in spatial databases. In order to extract useful knowledge from spatial data, an object management system that can store spatial data efficiently, provide very fast indexing & searching mechanisms, and support a distributed computing environment is needed. In this paper, we designed and implemented an open object management system for spatial data mining, that supports efficient management of spatial, aspatial, and knowledge data. In order to develop this system, we used Open OODB that is a widely used object management system. However, the lark of facilities for spatial data mining in Open OODB, we extended it to support spatial data type, dynamic class generation, object-oriented inheritance, spatial index, spatial operations, etc. In addition, for further increasement of interoperability with other spatial database management systems or data mining systems, we adopted international standards such as ODMG 2.0 for data modeling, SDTS(Spatial Data Transfer Standard) for modeling and exchanging spatial data, and OpenGIS Simple Features Specification for CORBA for connecting clients and servers efficiently.

• Tunnel and Underground Space
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• v.25 no.1
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• pp.97-106
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• 2015

We must precisely investigate the mechanical characters of rock to design rock slope safely and efficiently. But the method of clinometer has some disadvantages. So, we need a new measurement that can replace the method of clinometer. In this study, we analyze the reliability of joint orientation measurements in rock slope using the 3D laser scanner and program Split-FX that is a point cloud data analysis software. We could acquire the 495 pieces joint data through the automatic extraction of features. And we confirmed that there were some errors occurred with ${\pm}4^{\circ}$ of dip and ${\pm}5^{\circ}$ of dip direction. Generally, the method of clinometer has ${\pm}5^{\circ}$ and ${\pm}10^{\circ}$ error ranges of the joint orientation(dip/dip direction) that are the results of the advance research. Therefore, we analyzed the method of 3D laser scanner, and it is found to be efficient, reliable. This method is expected to mend the disadvantages of Clinometer method.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.1
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• pp.51-58
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• 2023

In this paper, we propose an organ segmentation technique for the automatic extraction of medical diagnostic indicators from X-ray images. In order to calculate diagnostic indicators of heart disease and spinal disease such as VHS(vertebral heart scale) and Cobb angle, it is necessary to accurately segment the thoracic spine, carina, and heart in a chest X-ray image. A deep neural network model in which the high-resolution representation of the image for each layer and the structure converted into a low-resolution feature map are connected in parallel was adopted. This structure enables the relative position information in the image to be effectively reflected in the segmentation process. It is shown that learning performance can be improved by combining the OCR module, in which pixel information and object information are mutually interacted in a multi-step process, and the channel attention module, which allows each channel of the network to be reflected as different weight values. In addition, a method of augmenting learning data is presented in order to provide robust performance against changes in the position, shape, and size of the subject in the X-ray image. The effectiveness of the proposed theory was evaluated through an experiment using 145 human chest X-ray images and 118 animal X-ray images.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.57-62
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• 2003

Among the procedure of automatic fingerprint recognition, binary code is important for the optimum thinning and singular point extraction while reserving the fingerprint image data. Binarization is to convert gray scale images into 0s and 255s values. For this conversion, you should set up the proper threshold value not to lose fingerprint image data. In this paper, we suggest the method to extract the standard threshold in real-time from fingerprint images entered by applying artificial intelligent methods in the binary code procedure. We show improved features while comparing the experiment results with the existing methods.

• Journal of Industrial Technology
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• v.19
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• pp.155-162
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• 1999

In the last few years the automatic classification of morpholgical landforms using GSIS and DEM was investigated. Particular emphasis has been put on the morphological point attribute approaches and the extraction of drainage basin variables from digital elevation models. The automated derivation of landforms has become a neccessity for quantitative analysis in geomorphology. Furthermore, the application of GSIS technologies has become an important tool for data management and numerical data analysis for purpose of geomorphological mapping. A process developed by Dikau et al, which automates Hanmond's manual process, was applied to the pyoung chang of the kangwon. Although it produced a classification that has good resemblance to the landforms in the area, it had some problems. For example, it produced a progressive zonation when landform changes from plains to mountains, it does not distinguish open valleys from a plains mountain interface, and it was affected by micro relief. Although automating existing quantitative manual processes is an important step in the evolution automation, definition may need to be calibrated since the attributes are oftem measured differently. A new process is presented that partly solves these problems.