• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.283-286
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• 2002

Most of the Automatic Fingerprint Identification Systems define the ridge endings and bifurcation points as the minutia for matching. Therefore, the precise extraction of the minutia is critical in raising the efficiency and reliability of the system. The fingerprint images produced through the preprocessing may have the false minutia happened over the process and they can be the factors to decrease the system efficiency This paper suggests the algorithm, which removes the false minutia after extracting the candidate minutia from the thinned binary images of fingerprint images.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1135-1147
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• 2021

In order to geometrically correct high-resolution satellite imagery, the sensor modeling process that restores the geometric relationship between the satellite sensor and the ground surface at the image acquisition time is required. In general, high-resolution satellites provide RPC (Rational Polynomial Coefficient) information, but the vendor-provided RPC includes geometric distortion caused by the position and orientation of the satellite sensor. GCP (Ground Control Point) is generally used to correct the RPC errors. The representative method of acquiring GCP is field survey to obtain accurate ground coordinates. However, it is difficult to find the GCP in the satellite image due to the quality of the image, land cover change, relief displacement, etc. By using image maps acquired from various sensors as reference data, it is possible to automate the collection of GCP through the image matching algorithm. In this study, the RPC of KOMPSAT-3A satellite image was corrected through the extracted matching point using the UAV (Unmanned Aerial Vehichle) imagery. We propose a pre-porocessing method for the extraction of matching points between the UAV imagery and KOMPSAT-3A satellite image. To this end, the characteristics of matching points extracted by independently applying the SURF (Speeded-Up Robust Features) and the phase correlation, which are representative feature-based matching method and area-based matching method, respectively, were compared. The RPC adjustment parameters were calculated using the matching points extracted through each algorithm. In order to verify the performance and usability of the proposed method, it was compared with the GCP-based RPC correction result. The GCP-based method showed an improvement of correction accuracy by 2.14 pixels for the sample and 5.43 pixelsfor the line compared to the vendor-provided RPC. In the proposed method using SURF and phase correlation methods, the accuracy of sample was improved by 0.83 pixels and 1.49 pixels, and that of line wasimproved by 4.81 pixels and 5.19 pixels, respectively, compared to the vendor-provided RPC. Through the experimental results, the proposed method using the UAV imagery presented the possibility as an alternative to the GCP-based method for the RPC correction.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.04a
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• pp.275-278
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• 2004

The geo-referencing of satellite imagery is a key task in remote sensing. GCPs are points the position of which is known both in the image and in the supporting maps. Mapping function makes the determination of map coordinates of all image pixels possible. Generally manual operations are done to identify image points corresponding to the points on a digital topographic map. In order to accurately measure ground coordinates of GCPs, differential global positioning system (DGPS) surveying are used. To acquire the sufficient number of well distributed GCPs is one of the most time-consuming and cost-consuming tasks. This paper describes the procedure of automatically extracting GCOs using GCP database. GCP image chips and image matching technique are used for automatic extraction of GCPs. We developed image processing tool for making image chip GCPs and Web Server for management of GCPs.

• 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 the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.17 no.4
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• pp.421-429
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• 1999

It is estimated that, in the twenty-first century, 70％ of global citizens will live in urban areas. This accelerated urbanization will require a greater need for the building DEM and orthoimagery along with Geographic Information System for urban management. The building DEM requires the detection of outlines showing building shapes. To do this, automatic and semiautomatic building extractions are usually used. However, in cases where automatic extraction is performed directly from the aerial images, accurate building outline extraction is very difficult because of shadow, roof color, and neighboring trees making it hard to discern building roofs. To overcome this problem semiautomatic building extraction was suggested in this paper. When a roof texture was homogeneous, building outline detection was performed by mouse-clicking on a part of the roof. To construct the building outlines when the texture was not homogeneous, a computer program was developed to search out corner points by clicking spots near corner points. The building DEM was generated by taking into account building outlines and heights calculated by image matching.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.5
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• pp.1025-1034
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• 2009

In steel making production line, steel slabs are given a unique identification number. This identification number, Slab management number(SMN), gives information about the use of the slab. Identification of SMN has been done by humans for several years, but this is expensive and not accurate and it has been a heavy burden on the workers. Consequently, to improve efficiency, automatic recognition system is desirable. Generally, a recognition system consists of text localization, text extraction, character segmentation, and character recognition. For exact SMN identification, all the stage of the recognition system must be successful. In particular, the text localization is great important stage and difficult to process. However, because of many text-like patterns in a complex background and high fuzziness between the slab and background, directly extracting text region is difficult to process. If the slab region including SMN can be detected precisely, text localization algorithm will be able to be developed on the more simple method and the processing time of the overall recognition system will be reduced. This paper describes about the slab region localization using SIFT(Scale Invariant Feature Transform) features in the image. First, SIFT algorithm is applied the captured background and slab image, then features of two images are matched by Nearest Neighbor(NN) algorithm. However, correct matching rate can be low when two images are matched. Thus, to remove incorrect match between the features of two images, geometric locations of the matched two feature points are used. Finally, search rectangle method is performed in correct matching features, and then the top boundary and side boundaries of the slab region are determined. For this processes, we can reduce search region for extraction of SMN from the slab image. Most cases, to extract text region, search region is heuristically fixed [1][2]. However, the proposed algorithm is more analytic than other algorithms, because the search region is not fixed and the slab region is searched in the whole image. Experimental results show that the proposed algorithm has a good performance.

• 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%.

• Journal of Korean Society for Geospatial Information Science
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• v.15 no.4
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• pp.103-109
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• 2007

Ortho images and Digital Elevation Model (DEM) have been applied in various fields. It is necessary to acquire Ground Control Points (GCPs) for processing high resolution satellite images. However surveying GCPs require many time and expense. This study was performed to investigate whether GCPs automatically extracted from ortho images and DTED Level 2 can be applied to sensor modeling for high resolution satellite images. We analyzed the performance of the sensor model established by GCPs extracted automatically. We acquired GCPs by matching satellite image against ortho images. We included the height acquired from DTED Level 2 data in these GCPs. The spatial resolution of the DTED Level 2 data is about 30m. Absolution accuracy of this data is below 18m above MSL. The spatial resolution of ortho image is 1m. We established sensor model from IKONOS images using GCPs extracted automatically and generated DEMs from the images. The accuracy of sensor modeling is about $4{\sim}5$ pixel. We also established sensor models using GCPs acquired based on GPS surveying and generated DEMs. Two DEMs were similar. The RMSE of height from the DEM by automatic GCPs and DTED Level 2 is about 9 m. So we think that GCPs by DTED Level 2 and ortho image can use for IKONOS sensor modeling.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.2
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• pp.209-216
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• 2018

High-resolution satellite imagery is used in diverse fields such as meteorological observation, topography observation, remote sensing (RS), military facility monitoring and protection of cultural heritage. In satellite imagery, low-resolution imagery can take place depending on the conditions of hardware (e.g., optical system, satellite operation altitude, image sensor, etc.) even though the images were obtained from the same satellite imaging system. Once a satellite is launched, the adjustment of the imaging system cannot be done to improve the resolution of the degraded images. Therefore, there should be a way to improve resolution, using the satellite imagery. In this study, a super resolution (SR) algorithm was adopted to improve resolution, using such low-resolution satellite imagery. The SR algorithm is an algorithm which enhances image resolution by matching multiple low-resolution images. In satellite imagery, however, it is difficult to get several images on the same region. To take care of this problem, this study performed the SR algorithm by calibrating geometric changes on images after applying automatic extraction of feature points and projection transform. As a result, a clear edge was found just like the SR results in which feature points were manually obtained.

• The Korean Journal of Nuclear Medicine
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• v.32 no.5
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• pp.414-424
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• 1998

Purpose: Cross-modality coregistration of positron emission tomography (PET) and magnetic resonance imaging (MR) could enhance the clinical information. In this study we propose a refined technique to improve the robustness of registration, and to implement more realistic visualization of the coregistered images. Materials and Methods: Using the sinogram of PET emission scan, we extracted the robust head boundary and used boundary-enhanced PET to coregister PET with MR. The pixels having 10% of maximum pixel value were considered as the boundary of sinogram. Boundary pixel values were exchanged with maximum value of sinogram. One hundred eighty boundary points were extracted at intervals of about 2 degree using simple threshold method from each slice of MR images. Best affined transformation between the two point sets was performed using least square fitting which should minimize the sum of Euclidean distance between the point sets. We reduced calculation time using pre-defined distance map. Finally we developed an automatic coregistration program using this boundary detection and surface matching technique. We designed a new weighted normalization technique to display the coregistered PET and MR images simultaneously. Results: Using our newly developed method, robust extraction of head boundary was possible and spatial registration was successfully performed. Mean displacement error was less than 2.0 mm. In visualization of coregistered images using weighted normalization method, structures shown in MR image could be realistically represented. Conclusion: Our refined technique could practically enhance the performance of automated three dimensional coregistration.