一种高效的三维轮廓曲线匹配算法

针对三维碎片自动拼接中的碎片匹配问题,提出一种高效的轮廓曲线匹配算法。用B-样条曲线表示三维空间曲线,并计算轮廓曲线上各个点的曲率、挠率和法矢,在匹配过程中,对轮廓特征点按其邻域曲面片进行分类,根据特征点类型标志及特征段之间的欧式距离对不同轮廓上的特征段进行相似性度量,再利用法矢对相似性较高的轮廓段进行可匹配性验证。实验结果证明该算法是稳定、高效的。     

二维非规则碎片匹配的算法

提出了一种基于曲率相似性的二维碎片轮廓匹配算法。该算法根据碎片轮廓上各点的曲率值来确定轮廓上的特征点,按照特征点对轮廓进行分段,将二维碎片的匹配简化为特征段的相似性度量与可匹配性验证。对可匹配的轮廓段进行欧氏变换以拼合两个轮廓曲线,自动地检测在当前变换下两个轮廓是否有重叠情况存在。没有重叠的两个轮廓组合起来更新为一个新的轮廓,进行再匹配直至重建过程完成。实例表明,算法是有效、稳定的。     

非规则三维碎片部分匹配算法研究

针对三维碎片拼合中的碎片匹配问题,在提取碎片轮廓线的基础上,提出了一种快速的碎片匹配算法。该算法首先对轮廓特征点按其邻域曲面片的形状进行分类;其次根据特征点类型标志、曲率及特征段Hausdorff距离对不同轮廓线上的特征段进行相似性度量;最后利用法矢对相似度较高的轮廓段进行可匹配性验证。该算法较适合于用散乱点云表示的三维碎片模型。     

一种新的二维开曲线匹配算法研究

在提取碎片轮廓的基础上,提出了一种基于相似变换下的新的尺寸不变为标示符的二维开曲线匹配方法。基本思想是首先以弧长的曲率绝对值的积分方法,通过对轮廓重采样来计算轮廓曲线上的特征点,特征点分曲线为若干段,然后特征段之间的Hausdorff距离来比较两曲线的段的相似性,当Hausdorff距离小于给定的容差时,可认为相应的轮廓是匹配的,实验证明算法更快有效。     

三维破碎物体多尺度拼接技术

在分析三维物体碎片轮廓曲线的特征和表示的基础上,研究多尺度小波轮廓描述符的计算,提出了一种多尺度下轮廓曲线特征提取及基于多尺度分析的三维物体碎片拼接方法.轮廓曲线经多尺度小波变换平滑后,提取曲率和挠率构成特征矢量;在选择了基于小波轮廓描述符的三维曲线匹配起点后,比较2条轮廓曲线的特征矢量以判断轮廓的相似性,并将轮廓匹配的2块相邻物体碎片拼接,实现破碎物体复原.最后通过实验验证了该方法的有效性.     

基于轮廓特征点的三维模型相似性匹配算法

针对Heczko算法容易丢失一些表示三维模型轮廓的重要信息,从而降低匹配准确性这一问题,研究了一种基于轮廓特征点的三维模型相似性匹配算法。通过函数投影提取三维模型的轮廓,再提取每一个轮廓上的角点,把角点作为特征点,用特征点的曲率值构成一个点集,最后计算点集之间的Hausdorff距离,进行相似性匹配。实验结果表明该算法提高了三维模型的检索准确性。     

基于链码的二维碎片轮廓匹配算法

首先利用边界链码的跟踪技术,提取出二维碎片的轮廓信息,同时对轮廓线进行细化、去噪和编码处理,得到二维轮廓线的一维链码信息描述,并在此基础上提出一种基于链码的二维碎片轮廓匹配方法.将二维碎片的形状匹配变为只对一维字符的处理,从而使得二维碎片的拼合简单化.实验表明该方法能够有效的找出一对复杂轮廓线上恰当的对应特征段,实现碎片物体的复原.     

一种基于DDTW的三维碎片自动拼接方法

提出了一种基于导数动态时间规整(DDTW)的三维碎片自动拼接方法,可以计算两块碎片最合理的拼接方式.同时设计了一种基于抗噪区间拟合的挠率估计方法和常数时间复杂度的三维重叠检测方法,大幅度提高了拼接的速度和准确性.首先确定物体碎片的轮廓曲线,查找角点,根据角点将轮廓曲线分段成子轮廓线,计算子轮廓曲线的挠率特征串;然后使用DDTW对两个特征串进行匹配,并给出匹配度的度量值,继而根据对应点的空间位置关系对碎片进行放缩和刚体变换,调用三维重叠检测方法排除重叠匹配;最后根据给定的评价标准找到最优匹配作为最终拼接结果.实验表明,该方法实现简单,鲁棒性强,能快速得到三维碎片集合的拼接结果.     

基于多尺度分析的三维曲线匹配技术研究

从提取三维物体碎片轮廓曲线出发,提出一种基于多尺度分析的三维曲线匹配技术。轮廓曲线经多尺度平滑后,计算曲线的特征矢量,通过比较特征矢量以判断轮廓曲线的相似性,实现三维曲线匹配。实验表明提出的算法具有准确性、鲁棒性和容错性。     

基于匹配对的非规则碎片拼合算法

介绍了基于轮廓特征的非规则碎片复原系统的模块构成,并针对碎片序列拼合模块,利用碎片间的匹配对关系,提出了一种新的碎片拼合序列遍历算法。该算法中碎片均由当前匹配碎片和另一碎片共同匹配而得,从而在后续的拼合中碎片的定位和拼合可由两碎片共同确定,这将为拼合过程提供保证。最后结合实例,进行了算法验证,取得了较好效果。     

基于曲率分析的三次Bezier曲线采样方法的研究

针对三维建模过程中数据量大的缺点,提出一种简单的基于曲率分析的三次Bezier曲线采样方法。该方法采用每个分段的三次Bezier曲线的特征点和该段曲率半径的极小值作为采样密度的判断标准,曲线采样主要分为多层轮廓和单一轮廓两种情况,对于多层轮廓,采样密度涉及到的因素有曲线特征点,曲率半径极小值,轮廓之间的间距,曲线的长度。而对于单一轮廓,采样密度涉及到的因素有曲线特征点,曲率半径极小值,曲线的长度。通过以上因素,计算出采样点的数目。实验结果证明,提出的方法可行有效,可用于三维建模的数据点采样。     

Contour curve reconstruction from cloud data for rapid prototyping

In this study, a method for generation of sectional contour curves directly from cloud point data is given. This method computes contour curves for rapid prototyping model generation via adaptive slicing, data points reducing and B-spline curve fitting. In this approach, first a cloud point data set is segmented along the component building direction to a number of layers. The points are projected to the mid-plane of the layer to form a 2-dimensional (2D) band of scattered points. These points are then utilized to construct a boundary curve. A number of points are picked up along the band and a B-spline curve is fitted. Then points are selected on the B-spline curve based on its discrete curvature. These are the points used as centers for generation of circles with a user-define radius to capture a piece of the scattered band. The geometric center of the points lying within these circles is treated as a control point for a B-spline curve fitting that represents a boundary contour curve. The advantage of this method is simplicity and insensitivity to common small inaccuracies. Two experimental results are included to demonstrate the effectiveness and applicability of the proposed method.     

Lofted B-spline surface interpolation by linearly constrained energy minimization

This paper proposes a new approach for lofted B-spline surface interpolation to serial contours, where the number of points varies from contour to contour. The approach first finds a common knot vector consisting of fewer knots that contain enough degrees of freedom to guarantee the existence of a B-spline curve interpolating each contour. Then, it computes from the contours a set of compatible B-spline curves defined on the knot vector by adopting B-spline curve interpolation based on linearly constrained energy minimization. Finally, it generates a B-spline surface interpolating the curves via B-spline surface lofting. As the energy functional is quadratic, the energy minimization problem leads to that of solving a linear system. The proposed approach is efficient in computation and can realize more efficient data reduction than previous approaches while providing visually pleasing B-spline surfaces. Moreover, the approach works well on measured data with noise. Some experimental results demonstrate its usefulness and quality.     

Contour segmentation in 2D ultrasound medical images with particle filtering

Object segmentation in medical images is an actively investigated research area. Segmentation techniques are a valuable tool in medical diagnostics for cancer tumours and cysts, for planning surgery operations and other medical treatment. In this paper, a Monte Carlo algorithm for extracting lesion contours in ultrasound medical images is proposed. An efficient multiple model particle filter for progressive contour growing (tracking) from a starting point is developed, accounting for convex, non-circular forms of delineated contour areas. The driving idea of the proposed particle filter consists in the incorporation of different image intensity inside and outside the contour into the filter likelihood function. The filter employs image intensity gradients as measurements and requires information about four manually selected points: a seed point, a starting point, arbitrarily selected on the contour, and two additional points, bounding the measurement formation area around the contour. The filter performance is studied by segmenting contours from a number of real and simulated ultrasound medical images. Accurate contour segmentation is achieved with the proposed approach in ultrasound images with a high level of speckle noise.     

平面轮廓的分段与识别技术

采用十一点法计算轮廓的近似曲率，该处比普通的三点法具有更强的抗噪性，提出利用曲率差分图进行特征点识别，并根据曲率差分图中轮廓特征点对应的波形特点，一次性将特征点识别为角点与切点，最后，对特征点间的线段类型加以识别，并进行连接调整和圆弧的分割与融合，从而较精确地实现了平面轮廓的分段及识别。     

A 3-D contour segmentation scheme based on curvature and torsion

The Darboux vector contains both the curvature and torsion of a three-dimensional (3-D) image as its components. Curvature measures how sharply a curve is turning while torsion measures the extent of its twist in 3-D space. Curvature and torsion completely define the shape of a 3-D curve. A scheme is presented that uses the length of this vector, also called the total curvature, for the segmentation of 3-D contours. A quintic B-spline is used in this formulation to obtain the total curvature for noisy data. Examples of nonnoisy and noisy data illustrate the merit of the scheme