• Title/Summary/Keyword: spline curve

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Recognition of Profile Contours of Human Face by Approximation - Approximation -

  • Yang, Yun-Mo
    • Proceedings of the KIEE Conference
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    • 1988.07a
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    • pp.679-682
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    • 1988
  • From the viewpoint of general recognition system, B-spline is introduced for the approximation and recognition of human profile contours. Profile contour is approximated to the cublic B-spline curve by least square fitting so that B-Spline nodes nearly correspond with the curvature extrema of the contour. This method is designed for the spline to be good features in recognition, and also showed good approximation compared with the variants of B-spline appraximation.

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ECG signal compression based on B-spline approximation (B-spline 근사화 기반의 심전도 신호 압축)

  • Ryu, Chun-Ha;Kim, Tae-Hun;Lee, Byung-Gook;Choi, Byung-Jae;Park, Kil-Houm
    • Journal of the Korean Institute of Intelligent Systems
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    • v.21 no.5
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    • pp.653-659
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    • 2011
  • In general, electrocardiogram(ECG) signals are sampled with a frequency over 200Hz and stored for a long time. It is required to compress data efficiently for storing and transmitting them. In this paper, a method for compression of ECG data is proposed, using by Non Uniform B-spline approximation, which has been widely used to approximation theory of applied mathematics and geometric modeling. ECG signals are compressed and reconstructed using B-spline basis function which curve has local controllability and control a shape and curve in part. The proposed method selected additional knot with each step for minimizing reconstruction error and reduced time complexity. It is established that the proposed method using B-spline approximation has good compression ratio and reconstruct besides preserving all feature point of ECG signals, through the experimental results from MIT-BIH Arrhythmia database.

Representation of Sweep Surface in Bicubic Spline surface Form (쌍3차 스플라인곡면 식에 의한 이동곡면의 표현)

  • 전차수;조형래;박세형
    • Transactions of the Korean Society of Mechanical Engineers
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    • v.19 no.4
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    • pp.1005-1012
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    • 1995
  • This paper proposes a new approach for modeling sweep surfaces. The overall modeling procedure consists of following steps : (1)remeshing the section curves based on the curve lengths ; (2)remeshing the guide curve and the boundary curves based on a given sweeping rule ; (3)obtaining intermediate section curves at the remeshed points of the guide curve by blending the initial section curves ; (4)compensation of the intermediate section curves ; (5)interpolating the initial and intermediate curves using Hermite interpolant. The resulting sweep surface is expressed in a G$^{2}$ bicubic parametric spline surface.

Interpolation Algorithm Comparison for Contour of Magnified Image (확대 영상의 윤각선 보간 알고리즘 비교)

  • 이용중;김기대;조순조
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
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    • 2001.10a
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    • pp.381-386
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    • 2001
  • When a input image is extensively magnified on the computer system, it is almost impossible to replicate the original shape because of mismatched coordinates system. In order to resolve the problem, the shape of the magnified image has been reconfigured using the bilinear interpolation method, low pass special filtering interpolation method and B-spline interpolation method, Ferguson curve interpolation method based on the CAD/CAM curve interpolation algorithm. The computer simulation main result was that. Nearest neighbor interpolation method is simple in making the interpolation program but it is not capable to distinguish the original shape. Bilinear interpolation method has the merit to make the magnified shape smooth and soft but calculation time is longer than the other method. Low pass spatial filtering method and B-spline interpolation method has an effect to immerge the intense of the magnified shape but it is also difficult to distinguish the original shape. Ferguson curve interpolation method has sharping shape than B-spline interpolation method.

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The forecast of curve shape reforming by variation of B-spline knot vector values (B-스플라인 노트백터 값 변화에 의한 곡선 형상 변화 예측)

  • 김희중;정재현
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 1994.10a
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    • pp.866-871
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    • 1994
  • B-spline curves and surfaces are effective solutions for design and modelling of the freeform models. The control methods of these are using with control points, knot vectors and weight of NURBS. Using control point is easy and resonable for representation of general models. But the movement of control points bring out the reformation of original convex hull. The B-splines with nonuniform knot vector provide good result of the shape modification without convex hull reforming. B-splines are constructed with control points and basis functions. Nonuniform knot vectors effect on the basis functions. And the blending curves describe the prorities of knot vectors. Applying of these, users will forecast of the reformed curves after modifying controllabler primitives.

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A Study on the High Speed Curve Generator Using 1-Dimensional Systolic Array Processor (1차원 시스톨릭 어레이 프로세서를 이용한 고속 곡선 발생기에 관한 연구)

  • 김용성;조원경
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.31B no.5
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    • pp.1-11
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    • 1994
  • In computer graphics since objects atre constructed by lines and curves, the high-speed curve generator is indispensible for computer aided design and simulatation. Since the functions of graphic generation can be represented as a series of matrix operations, in this paper, two kind of the high-speed Bezier curve generator that uses matrix equation and a recursive relation for Bezier polynomials are designed. And B-spline curve generator is designed using interdependence of B-spline blending functions. As the result of the comparison of designed curve generator and reference [5], [6] in the operation time and number of operators, the curve generator with 1-dimensional systolic array processor for matrix vector operation that uses matrix equation for Bezier curve is more effective.

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Frontal Flow Field Construction for Wall Boundary Condition Treatment and Frontal Remeshing Using Spline Curve in Injection Molding Simulation (사술성형 모사에 있어서 벽면 경계조건 처리를 위한 선단 유동장 생성기법과 spline 곡선을 이용한 선단 격자 재구성)

  • 윤재륜
    • The Korean Journal of Rheology
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    • v.5 no.1
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    • pp.34-48
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    • 1993
  • 최근 CAD/CAM의 발전과 더불어 사출성형공정은 여러분야에 폭넓게 응용되고 있 다. 사출성형공정은 크게 충전과정(filling stage), 냉각과정(cooling stage), 보압과정(packing stage)로 나누어 지는데 이중 충전과정은냉각과정과 보압과정에서 나타날 물리적인 현상과 최종 성형품의 기계적 성질에 중요한 영향을 끼치게 된다. 충전과정의 수치 해석 방법은 대 표적으로 control volume method, branching flow method, transient moving boun-dary method로 구분된다. 본 연구에서는 격자의 형태를 양호하게 형성시키고 유동선단의 형태를 개선하기위한 기법인 Spline 곡선을 이용한 선단격자 재구성(frontal remeshing using spline curve)과 수치해석에 소요되는 시간을 줄이기 위하여 벽면경계조건 처리를 위한 선단 유동 장생성(frontal flow field construction for wall boun-dary condition treatment)기법을 개발 하고 transient moving voundary method에 적용시켜 원형 평판과 인장 및 굽힘시편 그리고 두께가 변하는 사각 형상을 가진 캐비터에서의 충전과정을 수치해석하였다. 그결과 압력 분 포, 온도분포, 속도장, 유동선단의 진전형태 등이 기존에 제출된 해석결과와 비교하여 볼 때 만족스러운 수치해석결과를 보였다.

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Weight Control and Knot Placement for Rational B-spline Curve Interpolation

  • Kim, Tae-Wan;Lee, Kunwoo
    • Journal of Mechanical Science and Technology
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    • v.15 no.2
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    • pp.192-198
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    • 2001
  • We consider an interpolation problem with nonuniform rational B-spline curves given ordered data points. The existing approaches assume that weight for each point is available. But, it is not the case in practical applications. Schneider suggested a method which interpolates data points by automatically determining the weight of each control point. However, a drawback of Schneiders approach is that there is no guarantee of avoiding undesired poles; avoiding negative weights. Based on a quadratic programming technique, we use the weights of the control points for interpolating additional data. The weights are restricted to appropriate intervals; this guarantees the regularity of the interpolating curve. In a addition, a knot placement is proposed for pleasing interpolation. In comparison with integral B-spline interpolation, the proposed scheme leads to B-spline curves with fewer control points.

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Trimmed surface analysis based on T-spline FEM (T-스플라인 유한요소해석을 이용한 트림 곡면 해석)

  • Kim, Hyun-Jung;Seo, Yu-Deok;Youn, Sung-Kie
    • Proceedings of the KSME Conference
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    • 2008.11a
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    • pp.612-617
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    • 2008
  • In this present work, trimmed surface analysis for the 2D elasticity problem is presented. The main benefit of the proposed method is that no additional modeling for analysis of a trimmed surface is necessary. As the first attempt to deal with a trimmed surface in spline FEM, the information of the trimming curve and trimmed surface exported from CAD system is directly utilized for analysis. For this, trimmed elements are searched through employing projection scheme. For the integration of the trimmed elements, NURBSenhanced integration scheme which is used in NEFEM is adopted. The quadtree refinement of integration cell is performed for the complicated trimmed cases. The information of trimming curve is used for obtaining integration points as well as constructing stiffness matrix. The robustness and effectiveness of the proposed method are investigated by presenting various numerical examples.

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EIGENVALUE ANALYSIS USING PIECEWISE CUBIC B-SPLINE (CUBIC B-SPLINE을 이용한 고유치 해석)

  • Kim Young-Moon
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • 2000.10a
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    • pp.355-360
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    • 2000
  • This paper presents properties of piecewise cubic B-spline function and Rayleigh-Ritz method to compute the smallest eigenvales. In order to compute the smallest eigenvalues, Rayleigh quotient approach is used and four different types of finite element approximating functions corresponding to the statical deflection curve, spanned by the linearly independent set of piecewise cubic B-spline functions with equally spaced 5 knots from a partion of [0, 1], each satisfying homogeneous boundary conditions with constraining effects are used to compute the smallest eigenvalues for a Sturm-Lionville boundary equations of u"+ λ²u=0, u(0.0)=u(0.0)=0, 0≤x≤1.0.

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