• Proceedings of the Korea Society for Simulation Conference
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• 2005.11a
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• pp.130-137
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• 2005

본 논문은 심장이 수축$\cdot$이완함에 따라 그 형태와 위치가 변하는 관상동맥의 구조와 그 움직임을 사실적으로 표현하기 위한 매개변수적 모델링 기법을 제안한다. 완성된 모델은 관상동맥의 움직임을 관찰함으로써 심장질환 판단에 도움을 주고, 심장시술 시뮬레이션 및 시술계획수립에 사용될 수 있다. 매개변수적 기법으로 생성된 모델은 메쉬 정점의 인덱스만으로 모델간 매칭을 위한 대응점을 찾을 수 있으므로, 시간대별로 달라지는 정점의 위치를 쉽게 추적함으로써 모델의 움직임을 표현할 수 있다. 그러나 이러한 기법으로 생성된 모델은 분리, 접합 등의 변형조작이 어렵고, 트리형태 객체에 적용하기 힘든 단점이 있다. 본 논문에서는 이를 극복하기 위해 분할된 혈관영역의 골격데이타에서 찾아낸 분기점을 중심으로 Generalized Cylinder를 이용하여 실린더 형태의 각 혈관세그먼트를 모델링 한 후, 분기영역을 3개의 하프파이프(half pipe)와 2개의 삼각형 패치로 연결하여 모델링하였다. 완성된 모델은 다시점 관상동맥데이터에 적용하였고, 각 시점에서 구해진 정점의 위치를 선형보간함으로써 부드러운 혈관의 움직임을 나타내었다.

• Journal of KIISE
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• v.43 no.5
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• pp.606-612
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• 2016

The multi-view video service based on the DASH(Dynamic Adaptive Streaming over HTTP) switches the viewpoint or object which is selected by the user among the multiple video streams captured by multiple cameras. However, the problem is that the conventional DASH-based multi-view video service takes a long time to switch the viewpoint. The reason is that the conventional scheme switches to the new video stream after consuming all buffered segments of the previous video stream. In this paper, we propose a video streaming scheme for minimizing the viewpoint switching delay in the DASH-based multi-view video service. In order to minimize the viewpoint switching delay, the proposed scheme configures the video streams by controlling the GoP (Group of Pictures) size and controls the client buffer based on bandwidth estimation and playback buffer occupancy. Through the experimental results, we prove that the proposed scheme reduces the viewpoint switching delay.

• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.882-897
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• 2020

Recently, the use of immersive media contents representing Panorama and 360° video is increasing. Since the viewing angle is limited to generate the content through a general camera, image stitching is mainly used to combine images taken with multiple cameras into one image having a wide field of view. However, if the parallax between the cameras is large, parallax distortion may occur in the stitched image, which disturbs the user's content immersion, thus an image stitching overcoming parallax distortion is required. The existing Seam Optimization based image stitching method to overcome parallax distortion uses energy function or object segment information to reflect the location information of objects, but the initial seam generation location, background information, performance of the object detector, and placement of objects may limit application. Therefore, in this paper, we propose an image stitching method that can overcome the limitations of the existing method by adding a weight value set differently according to the type of object to the energy value using object detection based on deep learning.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.2
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• pp.93-98
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• 2013

Multi-temporal high resolution satellite images are essential data for efficient city analysis and monitoring. Yet even when acquired from the same location, identical sensors as well as different sensors, these multi-temporal images have a geometric inconsistency. Matching points between images, therefore, must be extracted to match the images. With images of an urban area, however, it is difficult to extract matching points accurately because buildings, trees, bridges, and other artificial objects cause shadows over a wide area, which have different intensities and directions in multi-temporal images. In this study, we analyze a shadow effect on image matching of high resolution satellite images in urban area using Scale-Invariant Feature Transform(SIFT), the representative matching points extraction method, and automatic shadow extraction method. The shadow segments are extracted using spatial and spectral attributes derived from the image segmentation. Also, we consider information of shadow adjacency with the building edge buffer. SIFT matching points extracted from shadow segments are eliminated from matching point pairs and then image matching is performed. Finally, we evaluate the quality of matching points and image matching results, visually and quantitatively, for the analysis of shadow effect on image matching of high resolution satellite image.

• Korean Journal of Remote Sensing
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• v.25 no.2
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• pp.145-154
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• 2009

High spatial resolution satellite image classification has a limitation when only using the spectral information due to the complex spatial arrangement of features and spectral heterogeneity within each class. Therefore, the extraction of the spatial information is one of the most important steps in high resolution satellite image classification. This study proposes a new spatial feature extraction method, named SSI(Shape-Size Index). SSI uses a simple region-growing based image segmentation and allocates spatial property value in each segment. The extracted feature is integrated with spectral bands to improve overall classification accuracy. The classification is achieved by applying a SVM(Support Vector Machines) classifier. In order to evaluate the proposed feature extraction method, KOMPSAT-2 and QuickBird-2 data are used for experiments. It is demonstrated that proposed SSI algorithm leads to a notable increase in classification accuracy.