• 제목/요약/키워드: Line Cluster

검색결과 252건 처리시간 0.025초

선군집분할방법에 의한 특징 추출 (Feature Extraction by Line-clustering Segmentation Method)

  • 황재호
    • 정보처리학회논문지B
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    • 제13B권4호
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    • pp.401-408
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    • 2006
  • 영상신호의 수직축 및 수평축 화소 성분 분석을 통해서, 영상 내부에 존재하는 각 영역의 군집적 특성을 통계 및 영역적으로 처리 분류함으로써 필요한 특징을 추출할 수 있는 새로운 형태의 영역분할처리 알고리즘을 제시한다. 종래의 점처리나 면처리 방식에 비해 이 방식은 수평축과 수직축 상에서의 연속적인 선처리 방식이라고 할 수 있다. 영상을 구성하는 영역간 경계가 암시적으로 구분되어 있으나, 명시적으로는 불투명하고, 영상 특성의 분기점 또한 불명확하고 중복되어 있음으로 인하여 문턱치처리나 분기점처리로 그 영역간 특정을 분할, 추출하기가 곤란한 경우에 이 방식은 우수한 효과가 있다. 수평축 및 수직축 선처리를 통해 각 영역들의 특성들을 군집으로 처리한 다음 처리한 축과 수직 방향으로 축차적 적응진행처리한다. 그 결과 영상 내 각 영역은 화소값의 중복에도 불구하고 하나의 군집으로 자리매김하면서 군집 고유의 화소 값을 갖는다. 그리고 처리후 영상은 각 군집에 부여한 새로운 화소값으로 변환함으로 필요한 특정이 추출된다. 이 방식은 특히 영역 분할을 통해 시각적 효과를 극대화시킬 필요가 있는 경동맥 초음파 의료영상에서 우수한 결과를 보였다.

RADIAL ABUNDANCE GRADIENT IN GLOBULAR CLUSTERS

  • Chun, M.S.
    • 천문학회지
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    • 제14권1호
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    • pp.13-17
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    • 1981
  • The observed radial UBV colour variations (both B-V and U-B) of some globular clusters are examined for correlations with radial variations in the integrated spectra. The results show that the presence of a radial colour gradient is correlated with the presence of a gradient of the CN (and possibly the G-band) line strength, in the sense that the CN (and possibly the G-band) is stronger in the centre (where the cluster is redder) and becomes weaker in the outer region of the cluster (where the cluster is bluer). This may suggest that a primordial abundance, possibly nitrogen and carbon gradient was set up in the early stage of cluster formation.

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A Combination Capture-Recapture and Line Transect Model in Clustered Population

  • Choi, Jin-Sik;Pyong, Nam-Kung
    • Communications for Statistical Applications and Methods
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    • 제6권3호
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    • pp.729-748
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    • 1999
  • In this paper we present combined estimator of capture-recapture and line transect model using bivariate detection function and detection probability according to objects being in cluster population. Here bivariate detection function use distance and cluster size. The simulation shows that combined estimator approaches the more true value the larger size parameter. Therefore this estimator using the bivariate detection function is more efficient in estimate the population size and density by size parameter.

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Virtual Hill 및 Sink 개념 기반의 군집 로봇의 직선 대형 주행 기법 (Cluster Robots Line formatted Navigation Based on Virtual Hill and Virtual Sink)

  • 강요환;이민철;김지언;윤성민;노치범
    • 로봇학회논문지
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    • 제6권3호
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    • pp.237-246
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    • 2011
  • Robots have been used in many fields due to its performance improvement and variety of its functionality, to the extent which robots can replace human tasks. Individual feature and better performance of robots are expected and required to be created. As their performances and functions have increased, systems have gotten more complicated. Multi mobile robots can perform complex tasks with simple robot system and algorithm. But multi mobile robots face much more complex driving problem than singular driving. To solve the problem, in this study, driving algorithm based on the energy method is applied to the individual robot in a group. This makes a cluster be in a formation automatically and suggests a cluster the automatic driving method so that they stably arrive at the target. The energy method mentioned above is applying attractive force and repulsive force to a special target, other robots or obstacles. This creates the potential energy, and the robot is controlled to drive in the direction of decreasing energy, which basically satisfies lyapunov function. Through this method, a cluster robot is able to create a formation and stably arrives at its target.

그리드지도의 방향정보 이용한 형상지도형성 (Feature Map Construction using Orientation Information in a Grid Map)

  • 송도성;강승균;임종환
    • 한국정밀공학회:학술대회논문집
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    • 한국정밀공학회 2004년도 추계학술대회 논문집
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    • pp.1496-1499
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    • 2004
  • The paper persents an efficient method of extracting line segment in a grid map. The grid map is composed of 2-D grids that have both the occupancy and orientation probabilities based on the simplified Bayesian updating model. The probabilities and orientations of cells in the grid map are continuously updated while the robot explorers to their values. The line segments are, then, extracted from the clusters using Hough transform methods. The eng points of a line segment are evaluated from the cells in each cluster, which is simple and efficient comparing to existing methods. The proposed methods are illustrated by sets of experiments in an indoor environment.

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체표면 전개도에 의한 여자 상반신의 유형분석 -20대 여성을 중심으로- (Somatotype of Women's Upper Body through a Development Figure of the Surface of the Body)

  • 최은주
    • 한국의류학회지
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    • 제20권1호
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    • pp.170-182
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    • 1996
  • The purpose of this study is to classify the upper body of women into several kind.; of somatotypes, using the method of Surgical Tape and making their shells. The subjects are 50 females 20 to 29 years-old. Fifty-one anthropometric data are measured per shell of body surface : eight somatotype factors are obtained through principal component analysis and orthogonal rotation by the method of Varimax, Somatotype of women's upper body is achieved by cluster analysis, using the standardized factor score a.: an independent variable and the FASTCLUS of SAS by Kmeans. The results are as follows : 1. The number of the factors which explain the somatotype is eight and these factors comprise 81.63 percent of total variance. Factor 1 related to the degree of fatness in the front of upper body Factor 2 related to the degree of fatness in the back of upper body Factor 3 . related to the length of the upper body Factor 4 : related to the type of the upper chest over the chest circmference line Factor 5 : related to the armhole and neck Factor 6 : related to the type of lower chest under the chest circumference line Factor 7: related to the part of the back shoulder Factor 8: related to the depth of front neck and side dart of front independently 2. Cluster analysis results in classification of upper body into five clusters. Cluster 1 : the of circumference i.: lager and that of length is longer than the average The louver part of chest is the lagest and widest among surface areas. Cluster 2 : the circumference is the smallest , the length and surface area are small. The upper and lower chest is small Cluster 3 : the length and surface area are the smallest , the circumference is average. The body line (silhouette) from chest to waist is curved slightly.

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Chemical Properties of Emission Line Galaxies in the Virgo and Ursa Major Cluster

  • 정지원;성언창;이수창;박민아;김석
    • 천문학회보
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    • 제36권2호
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    • pp.78.2-78.2
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    • 2011
  • We utilize Sloan Digital Sky Survey DR7 spectroscopy of ~600 emission line galaxies (ELGs) in the Virgo and Ursa Major clusters to investigate their chemical properties depending on the environments. We derived chemical abundances of galaxies using either a direct estimation of the electron temperature or empirical calibrations. We also estimated star formation rates (SFRs) using H alpha and GALEX ultraviolet (UV) luminosities. We see no significant difference of UV colors and SFRs of ELGs between the Virgo and Ursa Major, indicating weak dependence of their star formation activity on global cluster environment. We also discuss the segregation of gas-phase element abundances in cluster environment.

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퍼지 신경망을 이용한 온라인 클러스터링 방법 (A On-Line Pattern Clustering Technique Using Fuzzy Neural Networks)

  • 김재현;서일홍
    • 전자공학회논문지B
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    • 제31B권7호
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    • pp.199-210
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    • 1994
  • Most of clustering methods usually employ a center or predefined shape of a cluster to assign the input data into the cluster. When there is no information about data set, it is impossible to predict how many clusters are to be or what shape clusters take. (the shape of clusters could not be easily represented by the center or predefined shape of clusters) Therefore, it is difficult to assign input data into a proper cluster using previous methods. In this paper, to overcome such a difficulty a cluster is to be represented as a collection of several subclusters representing boundary of the cluster. And membership functions are used to represent how much input data bllongs to subclusters. Then the position of the nearest subcluster is adaptively corrected for expansion of cluster, which the subcluster belongs to by use of a competitive learning neural network. To show the validity of the proposed method a numerical example is illustrated where FMMC(Fuzzy Min-Max Clustering) algorithm is compared with the proposed method.

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Hydrodynamics Simulation of the Off-Axis Cluster Merger Abell 115

  • Lee, Wonki;Kim, Mincheol;Jee, M. James
    • 천문학회보
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    • 제43권1호
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    • pp.60.3-61
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    • 2018
  • Abell 115 is a renowned cluster merger at z=0.197. It exhibits an asymmetric X-ray distribution with cometary tails and a megaparsec-sized radio relic stretching in the northeastern direction from the core of the northern cluster. Many observations have concluded that this cluster merger has a large impact parameter, but there has been no numerical analysis on the structure of Abell 115. In this study, we simulate Abell 115 with Gadget2 N-body/SPH code to reproduce the X-ray and weak lensing features of Abell 115. We find a new plausible merger scenario of Abell 115, wherein the northern cluster is currently in an outgoing phase. The predicted X-ray emission has a similar morphology to the observed tail of the northern cluster. However, in order to reproduce the observed line-of-sight velocity and projected distance while maintaining the two systems gravitationally bound, the system should possess a large projection angle, which makes the shock look considerably more diffused than the observed radio relic.

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얼굴의 조형적 특성에 따른 유형 분석 (An Analysis According to the Shape on Formative Attributes of a Face)

  • 김애경;이경희
    • 한국의류산업학회지
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    • 제7권6호
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    • pp.650-656
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    • 2005
  • The objective of this study is to analyze the formative attributes of face by measuring the shape and features of face. The faces of women in 20's were taken by digital camera and measured, then it has conducted a statistical analysis using a SPSS for factor analysis, correlation and cluster analysis. The findings are that it is consisted of six(6) different factors and it is responsible for 73.93%. In Factor 1 and Factor 2, it has explained the most significant factor to determine the shape of face. The result on cluster analysis is that it is classified into 5 groups and it is as follows. Attributes of each group is that Group 1 has a wide and long forehead, small and longish chin-line and chubby cheeks that represent polished and modern images, while Group 2 has small and longish forehead and chin-line that represent classical and mature images. On the other hand, Group 3 has a narrow forehead, small and longish chin-line and upward-style eyebrows that represents provocative images, whereas Group 4 has a shaped style that represent intellectual images and Group 5 has small and longish forehead and chin-line and cheekbones that represent polished and cute images.