• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1998.11a
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• pp.21-24
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• 1998

논문은 상대적인 3차원 정보를 추출하기 위하여 스테레오 정합 알고리듬에 에피폴라 기하학을 적용하였다. 카메라로부터 입력받은 영상에서 추출된 특징 점으로부터 에피폴라 기하학 구조를 구성한다. 이렇게 구한 에피폴라 기하학 정보는 스테레오 영상에서의 정합 점들 간의 기하학적인 상관관계를 구성하고 조밀한 변이영상을 추출한다 실험결과를 통하여 제안된 알고리듬이 실제 공간상에서 대상물체를 실감 있게 표현함을 알 수 있다.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10c
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• pp.19-21
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• 2001

현재 실시간의 실감 영상을 위한 다양한 기법들에 대한 활발한 연구가 진행되고 있다. 영상 기반 렌더링은 새롭게 주목 받고 있는 렌더링 방법으로 기존의 기하학 기반 렌더링과는 다르게 모델을 작성하는데 쉽게 사용될 뿐 아니라. 실감 영상을 만들어 내는 것에도 탁월한 성능을 나타내고 있다. 현재 사용하는 그래픽 가속기는 기하학 기반 렌더링의 방법을 위주의 설계되고 있는 추세이다. 이에 영상 기반 렌더링을 지원하는 구조의 제안을 통해서 실시간 영상의 생성을 가능하게 하였다. 또한 기존의 그래픽 가속기와의 통합을 통해 하드웨어 비용을 절감하며 효율적으로 두 가지 기법을 지원하는 구조를 제안하였다.

• The KIPS Transactions:PartA
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• v.12A no.1 s.91
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• pp.13-22
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• 2005

Similarly to the edges defined in a 2D image, we can define the geometric features representing the boundary of the distinctive parts appearing on 3D meshes. The geometric features have been used as basic primitives in several applications such as mesh simplification, mesh deformation, and mesh editing. In this paper, we propose geometric livewire and geometric livelane for extracting geometric features in a 3D mesh, which are the extentions of livewire and livelane methods in images. In these methods, approximate curvatures are adopted to represent the geometric features in a 3D mesh and the 3D mesh itself is represented as a weighted directed graph in which cost functions are defined for the weights of edges. Using a well-known shortest path finding algorithm in the weighted directed graph, we extracted geometric features in the 3D mesh among points selected by a user. In this paper, we also visualize the results obtained from applying the techniques to extracting geometric features in the general meshes modeled after human faces, cows, shoes, and single teeth.

• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.1
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• pp.98-106
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• 2003

Because satellite images have the advantage of high resolution, multi-spectral, revisit and wide swath characteristics, it is increased to utilize satellite image and get information little by little in nowadays. In order to utilize remote sensed images effectively, it is necessary to process satellite images through many processing steps. Among them, geometric correction is essential step for satellite image processing. In this study, we constructed geometric correction data using LANDSAT satellite images. First, we extracted GCPs from maps and constructed database over the Korean peninsular. Second, LANDSAT satellite images, 165 scenes were corrected geometrically using GCP database. Finally, we made 7 mosaic images by means of geometric correction images over Korean peninsular. We think that constructed geometric correction data will be used for many application fields as basic data.

• Korean Journal of Remote Sensing
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• v.14 no.1
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• pp.69-82
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• 1998

SAR imagery can overcome the limitations of electro-optical sensor imagery and provide us Information which plays a supplementary role. But it is necessary to remove a variety of geometric errors in SAR imagery. An accurate geometric correction of SAR imagery is not easy task to achieve, though some techniques and theories are introduced. We also have difficulties such as transformation problem between 'International' ellipsoid in Radarsat system and 'Bessel' ellipsoid. Two widely used correction method, one is made by simulated image, and the other by collinearity equation, usually use DEM. In this study, the merits and demerits of geocoding methods respectively and the effective method for Korean terrain were found.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.6
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• pp.525-532
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• 2000

본 논문은 영상에서 형태적 정보를 추출하는데 사용되는 수학적 형태학(mathematical morphology)에 퍼지 집합 이론을 적용하여 새로운 퍼지 수학적 형태학을 제안한다. 일반적인 수학적 형태학은 이진 영상에만 적용되는 한계를 가지고 있었다. 이를 그레이 스케일 영상에도 적용 가능하도록 한 Sinha와 Dougherty[8]이 제안한 방법도 기하학적 형태 변환을 보장하지 못하는 결점이 있었는데 본 논문에서는 그 결점을 제거하는 새로운 수축(erosion)과 확장(dilation) 연산을 정의하고 그 특성을 연구하였다. 본 논문이 제안한 방법과 [8]의 방법을 실제 영상에 대한 실험으로 비교하였다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.6
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• pp.605-612
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• 2010

Numberous satellites have monitored the Earth in order to detect changes in a large area. These satellites provide orbit information such as ephemeris data, RPC coefficients and etc. besides image data. If we can use such orbit data afforded by satellite, we can reduce the number of control point for geo-referencing. This paper shows the efficient geometric correction method of strip-satellite RADARSAT-l SAR images acquired by same orbit using ephemeris data, single control point and virtual control points. For accuracy analysis of proposed method, this paper compared the image geometrically corrected by the proposed method to the image corrected by ERDAS Imagine.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.4
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• pp.15-23
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• 2004

In order to utilize remote sensed images effectively, it is necessary to correct geometric distortion. Geometric correction is a critical step to remove geometric distortions in satellite images. For geometric correction, Ground Control Points (GCPs) have to be chosen carefully to guarantee the quality of geocoded satellite images, digital maps, GPS surveying or other data. Traditional approach to geometric correction used GCPs requires substantial human operations. Also that is necessary much time and manpower. In this paper, we presented an on-line automatic geometric correction by constructing GCP Chip database. The Proposed on-line automatic geometric correction system is consists of four part. Input image, control the GCP Chip, revision of selected GCP, and output setting part. In conclusion, developed system reduced the processing time and energy for tedious manual geometric correction and promoted usage of Landsat imagery.

• Korean Journal of Remote Sensing
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• v.18 no.4
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• pp.233-242
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• 2002

In this paper, an epipolar model without the ephemeris data is proposed. Also, various epipolar models such as the epipolar geometry of perspective sensor, the one proposed by Gupta and Hartley and the one based on the Orun and Natarajan's sensor model are reviewed and their accuracy are quantitatively analyzed using devised measure. Modeling data from ground control points, ground control points, ephemeris data and independent checking points are selected on SPOT over Taejon and Boryung area and KOMPSAT over Taejon and Nonsan area. Based on the results, the epipolar model of perspective sensor and the one by Gupta and Hartley have the average accuracy within 1 pixel but show high errors in several checking points. The proposed epipolarity model provides better results than that of perspective sensor and by Gupta and Hartley. Also, it shows the accuracy similar to the one based on Orun and Natarajan's sensor model.

• Korean Journal of Remote Sensing
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• v.23 no.4
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• pp.297-309
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• 2007

The first Korean geostationary weather satellite, Communications, Oceanography and Meteorology Satellite (COMS) will be launched in 2008. The ground station for COMS needs to perform geometric correction to improve accuracy of satellite image data and to broadcast geometrically corrected images to users within 30 minutes after image acquisition. For such a requirement, we developed automated and fast geometric correction techniques. For this, we generated control points automatically by matching images against coastline data and by applying a robust estimation called RANSAC. We used GSHHS (Global Self-consistent Hierarchical High-resolution Shoreline) shoreline database to construct 211 landmark chips. We detected clouds within the images and applied matching to cloud-free sub images. When matching visible channels, we selected sub images located in day-time. We tested the algorithm with GOES-9 images. Control points were generated by matching channel 1 and channel 2 images of GOES against the 211 landmark chips. The RANSAC correctly removed outliers from being selected as control points. The accuracy of sensor models established using the automated control points were in the range of $1{\sim}2$ pixels. Geometric correction was performed and the performance was visually inspected by projecting coastline onto the geometrically corrected images. The total processing time for matching, RANSAC and geometric correction was around 4 minutes.