• Journal of Korean Society for Geospatial Information Science
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• v.22 no.2
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• pp.17-24
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• 2014

This paper proposes an automatic extraction method of control point based on Geospatial Web Service. The proposed method consists of 3 steps. 1) The first step is to acquires reference data using the Geospatial Web Service. 2) The second step is to finds candidate control points in reference data and the target image by SURF algorithm. 3) By using RANSAC algorithm, the final step is to filters the correct matching points of candidate control points as final control points. By using the Geospatial Web Service, the proposed method increases operation convenience, and has the more extensible because of following the OGC Standard. The proposed method has been tested for SPOT-1, SPOT-5, IKONOS satellite images and has been used military standard data as reference data. The proposed method yielded a uniform accuracy under RMSE 5 pixel. The experimental results proved the capabilities of continuous improvement in accuracy depending on the resolution of target image, and showed the full potential of the proposed method for military purpose.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.3
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• pp.86-97
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• 2021

As mechatronic systems have various, complex functions and require high performance, automatic fault detection is necessary for secure operation in manufacturing processes. For conducting automatic and real-time fault detection in modern mechatronic systems, multiple sensor signals are collected by internet of things technologies. Since traditional statistical control charts or machine learning approaches show significant results with unified and solid density models under normal operating states but they have limitations with scattered signal models under normal states, many pattern extraction and matching approaches have been paid attention. Signal discretization-based pattern extraction methods are one of popular signal analyses, which reduce the size of the given datasets as much as possible as well as highlight significant and inherent signal behaviors. Since general pattern extraction methods are usually conducted with a fixed size of time segmentation, they can easily cut off significant behaviors, and consequently the performance of the extracted fault patterns will be reduced. In this regard, adjustable time segmentation is proposed to extract much meaningful fault patterns in multiple sensor signals. By considering inflection points of signals, we determine the optimal cut-points of time segments in each sensor signal. In addition, to clarify the inflection points, we apply Savitzky-golay filter to the original datasets. To validate and verify the performance of the proposed segmentation, the dataset collected from an aircraft engine (provided by NASA prognostics center) is used to fault pattern extraction. As a result, the proposed adjustable time segmentation shows better performance in fault pattern extraction.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.1
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• pp.7-16
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• 2010

Face recognition of face images is an active subject in the area of computer pattern recognition, which has a wide range of potential. Automatic extraction of face image of the feature points is an important step during automatic face recognition. Whether correctly extract the facial feature has a direct influence to the face recognition. In this paper, a new method of facial feature extraction based on Discrete Wavelet Transform is proposed. Firstly, get the face image by using PC Camera. Secondly, decompose the face image using discrete wavelet transform. Finally, we use the horizontal direction, vertical direction projection method to extract the features of human face. According to the results of the features of human face, we can achieve face recognition. The result show that this method could extract feature points of human face quickly and accurately. This system not only can detect the face feature points with great accuracy, but also more robust than the tradition method to locate facial feature image.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.360-362
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• 2003

This paper presents simple feature-based approaches for full- and/or semi-automatic extraction, selection, and localization (center-determination) of ground control points (GCPs) for radargrammetry using airborne synthetic aperture radar (SAR) images. Test results using airborne NASA/JPL TOPSAR images in Taiwan verify that the registration accuracy is about 0.8${\sim}$1.4 pixels. In c.a. 30 minutes, 1500${\sim}$3000 GCPs are extracted and their point centers in a SAR image of about 512 ${\times}$ 512 pixels are determined on a personal computer.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2010-2014
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• 2005

Object detection and parameter estimation in point cloud data is a relevant subject to robotics, reverse engineering, computer vision, and sport mechanics. In this paper a software is presented for fully-automatic object detection and parameter estimation in unordered, incomplete and error-contaminated point cloud with a large number of data points. The software consists of three algorithmic modules each for object identification, point segmentation, and model fitting. The newly developed algorithms for orthogonal distance fitting (ODF) play a fundamental role in each of the three modules. The ODF algorithms estimate the model parameters by minimizing the square sum of the shortest distances between the model feature and the measurement points. Curvature analysis of the local quadric surfaces fitted to small patches of point cloud provides the necessary seed information for automatic model selection, point segmentation, and model fitting. The performance of the software on a variety of point cloud data will be demonstrated live.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.264-264
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• 2000

In this paper, we propose a method to automatically extract stable visual landmarks from observed data for a mobile robot with stereo vision system. The robot selects as stable landmarks vertical line segments which are distinct and on planar surfaces, because they are expected to be observed reliably from various view-points. When the robot moves, it uses several, less uncertain landmarks for estimating its motion. Experimental results in real scenes show the validity of the proposed method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.2
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• pp.55-62
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• 2019

The stereo geometry establishment based on the precise sensor modeling is prerequisite for accurate stereo data processing. Ground control points are generally required for the accurate sensor modeling though it is not possible over the area where the accessibility is limited or reference data is not available. For the areas, the relative orientation should be carried out to improve the geometric consistency between the stereo data though it does not improve the absolute positional accuracy. The relative orientation requires conjugate points that are well distributed over the entire image region. Therefore the automatic conjugate point extraction is required because the manual operation is labor-intensive. In this study, we applied the method consisting of the key point extraction, the search space minimization based on the epipolar line, and the rigorous outlier detection based on the RPCs (Rational Polynomial Coefficients) bias compensation modeling. We tested different parameters of window sizes for Kompsat-2 across track stereo data and analyzed the RPCs precision after the bias compensation for the cases whether the epipolar line information is used or not. The experimental results showed that matching outliers were inevitable for the different matching parameterization but they were successfully detected and removed with the rigorous method for sub-pixel level of stereo RPCs precision.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.1
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• pp.1-7
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• 2011

Terrain, building location and area, and building shape information is in need of implementing 3D map. This paper proposes a method of extracting a building area by an improved semi-automatic snake algorithm. The method consists of 3-stage: pre-processing, initializing control points, and applying an improved snake algorithm. In the first stage, after transforming a satellite image to a gray image and detecting the approximate edge of the gray image, the method combines the gray image and the edge. In the second stage, the user looks for the center point of a building and the system sets the circular or rectangular initial control points by an procedural method. In the third stage, the enhanced snake algorithm extracts the building area. In particular, this paper sets the one tenn of the snake in a new way in order to use the proposed method for specializing building area extraction. Finally, this paper evaluated the performance of the proposed method using sky view satellite image and it showed that the matching percentage to the exact building area is 75%.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.605-616
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• 2020

The construction quality control of hull blocks is of great significance for shipbuilding. The total station device is predominantly employed in traditional applications, but suffers from long measurement time, high labor intensity and scarcity of data points. In this paper, the Terrestrial Laser Scanning (TLS) device is utilized to obtain an efficient and accurate comprehensive construction information of hull blocks. To address the registration problem which is the most important issue in comparing the measurement point cloud and the design model, an automatic registration approach is presented. Furthermore, to compare the data acquired by TLS device and sparse point sets obtained by total station device, a method for key point extraction is introduced. Experimental results indicate that the proposed approach is fast and accurate, and that applying TLS to control the construction quality of hull blocks is reliable and feasible.

• Korean Journal of Remote Sensing
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• v.28 no.2
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• pp.191-202
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• 2012

A high resolution satellite imagery such as KOMPSAT-2 includes a material containing rational polynomial coefficient (RPC) for three-dimensional geopositioning. However, image geometries which are calculated from the RPC must have inevitable systematic errors. Thus, it is necessary to correct systematic errors of the RPC using several ground control points (GCPs). In this paper, we propose an efficient method for automatic correction of image geometries using tie points of a stereo pair and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) without GCPs. This method includes four steps: 1) tie points extraction, 2) determination of the ground coordinates of the tie points, 3) refinement of the ground coordinates using SRTM DEM, and 4) RPC adjustment model parameter estimation. We validates the performance of the proposed method using KOMPSAT-2 stereo pair. The root mean square errors (RMSE) achieved from check points (CPs) were about 3.55 m, 9.70 m and 3.58 m in X, Y;and Z directions. This means that we can automatically correct the systematic error of RPC using SRTM DEM.