• The Korean Publising Journal, Monthly
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• s.370
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• pp.54-55
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• 2006

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.1453-1456
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• 2011

타일로 분할된 항공 라이다 데이터로 부터 데이터마이닝 기법을 이용한 지표면 분류 결과에 따라 건물을 포함하는 타일에 대해 적용할 건물영역 추출 기법을 제안한다. 본 기법은 재귀적 경계점 추출 알고리즘과 경계점 연결을 통해 경계선을 형성하고 경계선을 타일의 외벽과 연결해 건물영역의 외곽을 추출한다. 제안된 기법으로 추출된 건물 영역을 실제 항공사진과 비교하여 제시하고 재귀적 경계점 추출 알고리즘의 실행시간을 단축시키기 위해 사용된 지형정보 인덱스의 실행시간 단축 효과 분석이 제시된다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.29-31
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• 2020

제안된 방법은 외막의 경계선을 추출하기 위해 Max-Min 이진화를 적용하여 외막을 추출 한 후에 삼각 함수의 각도 값을 이용하여 외막의 경계점을 추출한다. 추출한 경계점들을 Bezier Curve 기법을 적용하여 외막의 경계점들을 연결하여 외막의 경계선을 추출한다. 그리고 내막 영역을 추출하기 위해 외막 영역을 ROI 영역으로 추출한다. 추출된 ROI 영역을 오목 파라볼라 기법을 적용하여 내막의 영역을 강조한다. 내막영역이 강조된 ROI 영역에 평균 이진화를 적용하여 내막의 영역을 추출한다. 추출된 영역에서 잡음을 제거하기 위해 내막 영역만 Labeling 기법을 적용한다. 제안된 방법을 IVUS 영상을 대상으로 실험한 결과, 내막과 외막간의 포함관계의 정도에 따라 환자의 수술 여부 결정에 대한 외막과 내막의 각 넓이 정보를 개관적으로 제공할 수 있는 가능성을 확인하였다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.1
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• pp.33-42
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• 2005

Identifying color codes needs precise color information of their constituents, and is far from trivial because colors usually suffer severe distortions throughout the entire procedures from printing to acquiring image data. To accomplish accurate identification of colors, we need a reliable segmentation method to separate different color regions from each other, which would enable us to process the whole pixels in the region of a color statistically, instead of a subset of pixels in the region. Color image segmentation can be accomplished by performing edge detection on component image(s). In this paper, we separately detected edges on component images from RGB, HSI, and YIQ color models, and performed mathematical analyses and experiments to find out a pair of component images that provided the best edge image when combined. The best result was obtained by combining Y- and R-component edge images.

• Journal of KIISE
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• v.41 no.9
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• pp.611-616
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• 2014

Rabin fingerprinting used for chunking requires the largest amount computation time in data deduplication, In this paper, therefore, we proposed parallel Rabin fingerprinting on GPGPU for efficient data deduplication. In addition, for efficient parallelism in Rabin fingerprinting, four issues are considered. Firstly, when dividing input data stream into data sections, we consider the data located near the boundaries between data sections to calculate Rabin fingerprint continuously. Secondly, we consider exploiting the characteristics of Rabin fingerprinting for efficient operation. Thirdly, we consider the chunk boundaries which can be changed compared to sequential Rabin fingerprinting when adapting parallel Rabin fingerprinting. Finally, we consider optimizing GPGPU memory access. Parallel Rabin fingerprinting on GPGPU shows 16 times and 5.3 times better performance compared to sequential Rabin fingerprinting on CPU and compared to parallel Rabin fingerprinting on CPU, respectively. These throughput improvement of Rabin fingerprinting can lead to total performance improvement of data deduplication.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.728-731
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• 2007

Though the Hough transform is a well-known method for detecting analytical shape represented by a number of free parameters, the basic property of the Hough transform, the one-to-many mapping from an image space to a Hough space, causes the innate problem, the sensitivity to noise. To remedy this problem, Edge Strength Hough Transform (ESHT) was proposed and proved to reduce the noise sensitivity. However the performance of ESHT depends on the size of a Hough space and image and some other parameters, which play an important role in ESHT and should be decided experimentally. In this paper, we derived a formula to decide decreasing parameter. Using the derived formulae, the decreasing parameter value can be decided only with the pre-determined values, the size of a Hough space and an image, which make it possible to decide them automatically.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.453-459
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• 2017

Users tend to check in and post their statuses in location-based social networks (LBSNs) to describe that their interests are related to a point-of-interest (POI). While previous studies on discovering area-of-interests (AOIs) were conducted mostly on the basis of density-based clustering methods with the collection of geo-tagged photos from LBSNs, we focus on estimating a POI boundary, which corresponds to only one cluster containing its POI center. Using geo-tagged tweets recorded from Twitter users, this paper introduces a density-based low-complexity two-phase method to estimate a POI boundary by finding a suitable radius reachable from the POI center. We estimate a boundary of the POI as the convex hull of selected geo-tags through our two-phase density-based estimation, where each phase proceeds with different sizes of radius increment. It is shown that our method outperforms the conventional density-based clustering method in terms of computational complexity.

• Korean Journal of Cognitive Science
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• v.15 no.4
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• pp.21-30
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• 2004

Additive color mixture of two different background colors appeared in the afterimage of a gray circle centered on an isoluminant bichromatic background (red/green, blue/green, or blue/yellow background). The chromatic mixture still appeared in the afterimage of a gray circle on a bichromatic background at different luminance levels, and also appeared in a large test field. The saturation of the induced color was observed to increase as the overall luminance of adaptation background stimulus increase of the size of test field decreases. It was found that the chromatic mixture does not appear with a chromatic or achromatic boundary inserted on the center of the test field. The boundary seems to prevent the induced color on each side of test field from spreading to the other side so that the induced color does not appear mixed but divided into two different colors. Without a boundary on the test stimulus, the color information induced in the afterimage seems to be too weak to create a subjective boundary between the two colors and consequently propagate inward appearing mixed.

• Journal of the Korea Society of Computer and Information
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• v.14 no.7
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• pp.33-40
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• 2009

Edge detection is very significant in low level image processing. However, majority edge detection methods are not only effective enough cause of the noise points' influence, even not flexible enough to different input images. In order to sort these problems, in this paper an algorithm is presented that has an extra noise reduction stage at first, and then automatically selects the both thresholds depending on gradient amplitude histogram and intra class minimum variance. Using this algorithm, can fade out almost all of the sensitive noise points, and calculate the propose thresholds for different images without setting up the practical parameters artificially, and then choose edge pixels by fuzzy algorithm. In finally, get the better result than the former Canny algorithm.

• Journal of the Korea Computer Graphics Society
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• v.28 no.1
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• pp.11-19
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• 2022

In this paper we present a new method for interactive segmentation of a triangle mesh by using the concavity-sensitive harmonic field and anisotropic geodesic. The proposed method only requires a single vertex in a desired feature region, while most of existing methods need explicit information on segmentation boundary. From the user-clicked vertex, a candidate region which contains the desired feature region is defined and concavity-senstive harmonic field is constructed on the region by using appropriate boundary constraints. An initial isoline is chosen from the uniformly sampled isolines on the harmonic field and optimal points on the initial isoline are determined as interpolation points. Final segmentation boundary is then constructed by computing anisotropic geodesics passing through the interpolation points. In experimental results, we demonstrate the effectiveness of the proposed method by selecting several features in various 3D models.