• The KIPS Transactions:PartB
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• v.17B no.5
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• pp.363-374
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• 2010

In this paper, we propose the method for extracting Homogeneity Threshold($H_T$) and for segmenting homogeneous regions by USRG(Unseeded Region Growing) with $H_T$. The $H_T$ is a criterion to distinguish homogeneity in neighbor pixels and is computed automatically from the original image by proposed method. Theoretical background for proposed method is based on the Otsu's single level threshold method. The method is used to divide a small local part of original image int o two classes and the sum($\sigma_c$) of standard deviations for the classes to satisfy special conditions for distinguishing as different regions from each other is used to compute $H_T$. To find validity for proposed method, we compare the original image with the image that is regenerated with only the segmented homogeneous regions and show up the fact that the difference between two images is not exist visually and also present the steps to regenerate the image in order the size of segmented homogeneous regions and in order the intensity that includes pixels. Also, we show up the validity of proposed method with various results that is segmented using the homogeneity thresholds($H^*_T$) that is added a coefficient ${\alpha}$ for adjusting scope of $H_T$. We expect that the proposed method can be applied in various fields such as visualization and animation of natural image, anatomy and biology and so on.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.1
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• pp.55-68
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• 2012

In this paper, we propose a novel method to solve the problem of excessive segmentation out of the method of segmenting regions from an image using Homogeneity Threshold($H_T$). The algorithm of the previous image segmentation based on $H_T$ was carried out region growth by using only the center pixel of selected window. Therefore it was caused resulting in excessive segmented regions. However, before carrying region growth, the proposed method first of all finds out whether the selected window is homogeneity or not. Subsequently, if the selected window is homogeneity it carries out region growth using the total pixels of selected window. But if the selected window is not homogeneity, it carries out region growth using only the center pixel of selected window. So, the method can reduce remarkably the number of excessive segmented regions of image segmentation based on $H_T$. In order to show the validity of the proposed method, we carried out multiple experiments to compare the proposed method with previous method in same environment and conditions. As the results, the proposed method can reduce the number of segmented regions above 40% and doesn't make any difference in the quality of visual image when we compare with previous method. Especially, when we compare the image united with regions of descending order by size of segmented regions in experimentation with the previous method, even though the united image has regions more than 1,000, we can't recognize what the image means. However, in the proposed method, even though image is united by segmented regions less than 10, we can recognize what the image is. For these reason, we expect that the proposed method will be utilized in various fields, such as the extraction of objects, the retrieval of informations from the image, research for anatomy, biology, image visualization, and animation and so on.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.3A
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• pp.268-275
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• 2002

This paper proposes a region-based segmentation algorithm fur extracting a video object by using the cost of potential of frame-difference energies. In the first step of a region-based segmentation using spatial intensity, each frame is segmented into a partition of homogeneous regions finely so that each region does not contain the contour of a video object. The fine partition is used as an initial partition for the second step of spatio-temporal segmentation. In spatio-temporal segmentation, the homogeneity cost for each pair of adjacent regions is computed which reflects the potential between the frame-difference energy on the common contour and the frame-difference energy of the lower potential region of the two. The pair of adjacent regions whose cost is minimal then is searched. The two regions of minimum cost ale merged, which result in updating the partition. The merging is recursively performed until only the contours remain which have Same difference energies of high potential. In the fecal step of post-processing, the video object is extracted removing the contours inside the object.

• Journal of Korea Multimedia Society
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• v.9 no.8
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• pp.1045-1053
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• 2006

We introduce a new method of painterly rendering. Instead of using the gradient direction of the source image to generate a brush stroke, we extract regions that can be drawn in one stroke using MHN filtering followed by identification of connected components, and make a brush stroke from each, based on an approximation to the medial axis. This method results in realistic-looking brush strokes of varying width that have an irregular directions where necessary.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.42 no.5 s.305
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• pp.51-58
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• 2005

This paper proposes an efficient algorithm for extracting a tumor region from an breast ultrasound volume image by using rate-distortion (R-D) based seeded region growing. In the proposed algorithm the rate and the distortion represent the roughness of the contour and the dissimilarity of pixels in a region, respectively. Staring from an initial seed region set in each cutting plane of a volume, a pair of the seed region and one of adjacent regions whose R-D cost is minimal is searched and then they are merged into a new updated seed region. This procedure is recursively performed until the averaged R-D cost values per the number of contour pixels in the seed region becomes maxim. As a result, the final seed region has good pixel homogeneity and a much smooth contour. Finally, the tumor volume is extracted using the contours of the final seed regions in all the cutting planes. Experimental results show that the averaged error rate of the proposed method is shown to be below 4%.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.23 no.2
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• pp.165-173
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• 2010

In this paper, the dynamic stiffness of scaled boundary finite element method(SBFEM) was analytically derived to represent the non-homogeneous space. The non-homogeneous parameters were introduced as an expotential value of power function which denoted the non-homogeneous properties of analysis domain. The dynamic stiffness of analysis domain was asymptotically expanded in frequency domain, and the coefficients of polynomial series were determined to satify the radiational condition. To verify the derived dynamic stiffness of domain, the numerical analysis of the typical problems which have the analytical solution were performed as various non-homogeneous parameters. As results, the derived dynamic stiffness adequatlly represent the features of the non-homogeneous space.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.1641-1644
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• 2004

3차원 볼륨데이터에서 분할 대상영역의 밝기 값이 다양하면서 밝기 값이 유사한 영역과 인접한 경우 3차원 영역확장(region growing) 방법을 사용하여 영역을 분할하기 위해서는 영역확장의 중요한 요인인 동질성 기준 값의 적절한 선택이 요구된다. 본 논문에서는 영역 복셀(voxel)의 1차 미분 값의 크기인 기울기 크기(gradient magnitude)만으로 영역의 경계를 찾기가 쉽지않은 대상의 분할을 위해 볼륨데이터의 지역적인 밝기 값의 변화의 특징을 고려하면서 분할 대상영역의 복셀의 2차 미분(second partial derivation)을 행렬의 요소(element)로 갖는 Hessian 행렬의 고유치(eigenvalue)를 영역확장의 문턱치 결정에 이용하였다. 제안한 알고리즘은 3차원 영역확장의 결과에 가장 큰 영향을 미치는 적절한 문턱치의 선택으로 대상영역의 분할을 성공적으로 수행하여 3차원 영역확장의 단점을 보완하였다.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.482-484
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• 1999

본 논문에서는 매개변수 없이 입력 문서 영상을 최대 동질 영역들로 분할한 다음, 각 동질 영역을 텍스트, 그림, 표 그리고 선으로 자동 분류하는 새로운 방법을 제안한다. 다단계 분석과 하향식 접근 방법을 사용하기 위하여 문서 영상을 피라미드 구조로 계층화하였으며, 어떤 영역을 분할할 지의 여부를 결정하기 위하여 그 영역의 주기성을 이용하여 판단하였다. 이러한 주기성 정보를 이용함으로써, 어떠한 매개변수 없이도 활자체 크기와 행간에 무관하게 텍스트 영역을 정확히 분석할 수 있었으며, 피라미드 구조를 만드는데 걸리는 시간이 질감 분석 접근방법보다 빠른 방법으로 설계되었다. Washington 대학의 문서 영상 데이터베이스를 이용한 실험 결과, 제안된 방법이 기존의 방법들보다 더 정확하게 문서 영상을 분할 및 분류할 수 있음을 확인할 수 있었다.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07a
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• pp.703-705
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• 2005

본 논문에서는 기울기 크기 기반의 3차원 영역확장 알고리즘을 사용하여 small animal PET(Positron Emission Tomography) 영상으로부터 종양을 분할하는 연구를 수행하였다. 픽셀 값의 범위가 다양하고 저해상도의 특성을 갖는 PET영상으로부터 대상영역을 정확하게 분할하기 위해서 전처리(preprocessing)과정으로 영상 픽셀값의 분포를 펼쳐줌으로써 영상의 가시화를 높이는 히스토그램 스트레칭(histogram stretching) 기법을 적용하고 대상영역과 픽셀값이 유사한 인접영역과의 경계를 찾기 위해 가우시안의 1차 미분 함수를 사용하여 계산된 기울기 크기(gradient magnitude) 기반의 3차원 영역확장(region growing) 알고리즘을 제안한다. 제안한 알고리즘은 영역확장의 결과에 가장 큰 영향을 미치는 적절한 동질성 기준의 선택으로 대상영역의 분할을 성공적으로 수행하여 일반적인 영역확장의 단점을 보완하였다.

• Knowledge Management Research
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• v.9 no.3
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• pp.159-172
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• 2008

기업의 지적자산을 효과적으로 관리하는 지식경영 차원에서 기업들은 디지털 시대에 부합되도록 다양한 종류의 지식을 창조해내는 디지털 창의성을 기반으로 한 지식 경쟁력 향상 방안을 요구하고 있다. 이에 본 연구에서는 디지털창의성의 개념을 정의하고 이들에게 영향을 미치는 구성요인으로 디지털환경, 전문성영역, 디지털리스트성향을 제시하고 구성원의 다양성에 따른 디지털 창의성 변화와 경영성과에 미치는 영향을 다중 에이전트 시뮬레이션 접근방법을 사용하여 분석 하였다. 분석결과 구성원의 다양성이 높은 집단일 경우 시간의 흐름에 따라 구성원의 다양성이 낮은 집단, 즉 동질성이 높은 집단 보다 지식관리시스템 (KMS)을 이용한 지식의 생성 및 공유와 디지털창의성에 영향을 주는 강도가 높은 것으로 나타났다. 또한, 전문성영역, 디지털리스트성향이 디지털 창의성에 유의한 영향을 미치며, 디지털 창의성도 경영성과에 유의한 영향을 미치는 것으로 나타났다. 이러한 결과를 바탕으로 디지털 창의성과 경영성과 향상에 대한 시사점 및 향후 연구방향을 제시하였다.