基于自适应权值的图割立体匹配算法

针对传统自适应权重存在计算复杂度高和传统图割匹配精度低的问题,提出一种改进的基于自适应权值和图割的立体匹配算法。首先根据灰度相似性和空间相近性重新定义能量函数的数据项,利用图像的梯度信息作为能量函数的平滑项;然后运用图割理论和 α 扩展算法进行模型求解;最后对视差图运用左右一致性检验和加权中值滤波进行视差精化。算法采用了Middlebury 网站提供的四幅国际标准立体图像对进行测试,实验表明所提算法能够得到较准确的视差图。     

一种改进的基于图割的立体匹配算法

为了提高立体匹配算法的精确度,提出了一种结合图割与Mean Shift图像分割的立体匹配算法。首先用Mean Shift算法对参考图像进行图像分割,得到分割后的标记图,然后将分割信息结合到图割算法的能量函数中,最后用改进的能量函数和图割算法得出致密的视差图。实验结果表明,提出的算法具有更高的精确度和更好的边缘特征。     

一种基于SSD和图割的快速立体匹配算法

针对图割法的立体匹配算法耗时多的问题,提出了一种基于S S D和图割的快速立体匹配算法。为了缩小视差搜索范围,缩短匹配时间,先采用区域匹配S S D算法得到初始视差,然后再采用左右一致性校验法去除误匹配点,可以提高初始视差图的质量;在构造能量函数时,把初始视差图中的像素视差作为图割的能量函数的限制项,根据这些限制可以减少不必要的节点,从而减少了计算量,缩短匹配时间。通过实验证明了本文算法在保证匹配图像质量的情况下,能提高匹配效率,减少匹配时间。     

模糊熵的区域合并和图割的快速矿岩分割

目的 针对现有区域合并和图割的结合算法没有考虑矿岩图像模糊特性,导致分割精度和运行效率较低,模糊边缘无法有效分割的问题,利用快速递推计算的最大模糊2-划熵信息设置以区域为顶点的图割模型似然能来解决。方法 首先利用双边滤波器和分水岭算法对矿岩图像进行预处理,并将其划分为若干一致性较好的区域;然后利用图像在计算最大模糊2-划分熵时,目标和背景的模糊隶属度函数来设计图割能量函数似然能,使得能量函数更接近模糊图像的真实情况,期间为了提高最大模糊2-划分熵值的搜索效率,提出了时间复杂度为O（n²）的递推算法将模糊熵的计算转化为递推过程,并保留不重复的递推结果用于后续的穷举搜索;最后利用设计的图割算法对区域进行标号,以完成分割。结果 本文算法的分割精度较其他区域合并和图割结合算法提高了约23%,分割后矿岩颗粒个数的统计结果相对于人工统计结果,其误差率约为2%,运行时间较其他算法缩短了约60%。结论 本文算法确保精度同时,有效提高矿岩图像的分割效率,为自动化矿岩图像高效分割的工程实践提供重要指导依据。     

基于图割与改进模糊C均值的图像分割

为提高图割算法对图像的分割效果,提出一种改进的模糊C均值聚类算法(FCMA)和图割分割算法相结合的图像分割方法。首先,用均值漂移算法将图像过分割成多个小区域(超像素),用得到的超像素代替像素点作为图的顶点,以相邻像素块间的关系为边构建图模型;然后,采用改进的模糊C均值(FCMA)算法对前景和背景的混合高斯模型分别进行聚类分析;最后,用最大流/最小割算法求取能量函数的全局最优解即得到图像的分割结果。实验结果表明,该方法在分割结果上具有较强的区域一致性及较为清晰、平滑的图像边缘,并且该方法对含有噪声的图像也能得到较好的分割结果。     

一种基于图割理论的快速立体匹配算法

针对传统的立体匹配算法中存在的低纹理区域和遮挡区域匹配精度低、实时性不好等问题,提出了一种基于图割理论的立体匹配算法.把图像分割成色彩单一的不同区域;计算初始视差图,利用可靠点求取各分割区域的平面模板参数,对模板参数相同的相邻区域进行融合;构造全局能量函数,采用图割算法求取全局能量最小的视差最优分配.实验结果表明,该算法对低纹理区域和遮挡区域均有较好的匹配结果,能够满足高精度、高实时性的要求.     

无向关系图视觉清晰化显示算法

本文提出了一种无向图视觉清晰化显示算法,使一般的无向关系图经过该算法重新确定顶点位置后,能得到清晰美观的输出结果。该算法首先将无向关系图去除孤立点,分离连通分支,并通过识别割边将每个连通分支分解成一系列的团,每个团内无割边,这些团以树型结构连接;然后通过识别割点和虚连线将每个团分解成子团,每个子团内无割点;最后将子团内顶点均匀分布在一个圆环上。该算法的优点在于实现方便,方法简单,运行高效,输出结果美观,并易于并行化。     

基于图割的图像分割方法及其新进展

鉴于图割的理论意义和实际应用价值,系统综述了基于图割的图像分割方法. 首先,深入分析了基于图割的图像分割方法的基本原理,主要从定性和定量角度剖析了图割与能量函数最小化之间的关系, 然后,概括了基于图割的图像分割方法的基本步骤,包括能量函数的设计、图的构造和最小割/最大流方法, 其次,系统梳理和评述了基于图割的图像分割方法的国内外研究现状,最后,指出了基于图割的图像分割方法的发展方向.     

一种尺度自适应的Mean Shift跟踪算法

针对传统Mean Shift中跟踪窗口尺度不能实时适应跟踪目标变化这一问题,提出一种基于图割理论的Mean Shift尺度自适应算法.根据每一帧图像的Mean Shift迭代结果,在其周围的一个小区域内,利用先验的肤色混合高斯模型构造图并建立关于标号的能量模型,使用max flow/min cut算法计算出能量函数最小值实现图割,在图割后的肤色团块中寻找最大团判定为跟踪目标,并以该团的尺度来实时调整目标跟踪窗口.实验结果表明,该方法克服了缩放10%核带宽的经典尺度适应方法的带宽趋于缩小问题,实时地反映跟踪目标真实尺度变化,避免背景中其他目标的干扰,具有较好的实用性和鲁棒性,而且可以应用到娱乐游戏控制中,丰富人机交互操作方式.     

基于SIFT特征和图割算法的图像分割方法研究

图割算法是图像分割方法中的一种高效的最优化计算方法,针对图像中目标物体的旋转尺度光照变化导致的分割不准确问题,提出了一种基于SIFT(Scale-Invariant Feature Transform)特征的图割(Graph Cuts)算法;该方法将SIFT特征的尺度旋转不变性和图割算法的准确快速性结合在一起,通过提取图像中物体SIFT特征点做为图割算法的种子点,求解最小能量函数快速从而获得该图像的最优分割;实验结果表明,该方法鲁棒性较好,能准确地分割出目标物体在图像中的轮廓。     

Parallel Globally Consistent Normal Orientation of Raw Unorganized Point Clouds

A mandatory component for many point set algorithms is the availability of consistently oriented vertex‐normals (e.g. for surface reconstruction, feature detection, visualization). Previous orientation methods on meshes or raw point clouds do not consider a global context, are often based on unrealistic assumptions, or have extremely long computation times, making them unusable on real‐world data. We present a novel massively parallelized method to compute globally consistent oriented point normals for raw and unsorted point clouds. Built on the idea of graph‐based energy optimization, we create a complete kNN‐graph over the entire point cloud. A new weighted similarity criterion encodes the graph‐energy. To orient normals in a globally consistent way we perform a highly parallel greedy edge collapse, which merges similar parts of the graph and orients them consistently. We compare our method to current state‐of‐the‐art approaches and achieve speedups of up to two orders of magnitude. The achieved quality of normal orientation is on par or better than existing solutions, especially for real‐world noisy 3D scanned data.     

Curve skeleton extraction by coupled graph contraction and surface clustering

In this paper, we present a practical algorithm to extract a curve skeleton of a 3D shape. The core of our algorithm comprises coupled processes of graph contraction and surface clustering. Given a 3D shape represented by a triangular mesh, we first construct an initial skeleton graph by directly copying the connectivity and geometry information from the input mesh. Graph contraction and surface clustering are then performed iteratively. The former merges certain graph nodes based on computation of an approximate centroidal Voronoi diagram, seeded by subsampling the graph nodes from the previous iteration. Meanwhile, a coupled surface clustering process serves to regularize the graph contraction. Constraints are used to ensure that extremities of the graph are not shortened undesirably, to ensure that skeleton has the correct topological structure, and that surface clustering leads to an approximately-centered skeleton of the input shape. These properties lead to a stable and reliable skeleton graph construction algorithm.Experiments demonstrate that our skeleton extraction algorithm satisfies various desirable criteria. Firstly, it produces a skeleton homotopic with the input (the genus of both shapes agree) which is both robust (results are stable with respect to noise and remeshing of the input shape) and reliable (every boundary point is visible from at least one curve-skeleton location). It can also handle point cloud data if we first build an initial skeleton graph based on k-nearest neighbors. In addition, a secondary output of our algorithm is a skeleton-to-surface mapping, which can e.g. be used directly for skinning animation.Highlights(1) An algorithm for curve skeleton extraction from 3D shapes based on coupled graph contraction and surface clustering. (2) The algorithm meets various desirable criteria and can be extended to work for incomplete point clouds.     

3D Surface Reconstruction of Noisy Point Clouds Using Growing Neural Gas: 3D Object/Scene Reconstruction

With the advent of low-cost 3D sensors and 3D printers, scene and object 3D surface reconstruction has become an important research topic in the last years. In this work, we propose an automatic (unsupervised) method for 3D surface reconstruction from raw unorganized point clouds acquired using low-cost 3D sensors. We have modified the growing neural gas network, which is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation, to perform 3D surface reconstruction of different real-world objects and scenes. Some improvements have been made on the original algorithm considering colour and surface normal information of input data during the learning stage and creating complete triangular meshes instead of basic wire-frame representations. The proposed method is able to successfully create 3D faces online, whereas existing 3D reconstruction methods based on self-organizing maps required post-processing steps to close gaps and holes produced during the 3D reconstruction process. A set of quantitative and qualitative experiments were carried out to validate the proposed method. The method has been implemented and tested on real data, and has been found to be effective at reconstructing noisy point clouds obtained using low-cost 3D sensors.     

Reconstructing Animated Meshes from Time‐Varying Point Clouds

In this paper, we describe a novel approach for the reconstruction of animated meshes from a series of time‐deforming point clouds. Given a set of unordered point clouds that have been captured by a fast 3‐D scanner, our algorithm is able to compute coherent meshes which approximate the input data at arbitrary time instances. Our method is based on the computation of an implicit function in ?⁴ that approximates the time‐space surface of the time‐varying point cloud. We then use the four‐dimensional implicit function to reconstruct a polygonal model for the first time‐step. By sliding this template mesh along the time‐space surface in an as‐rigid‐as‐possible manner, we obtain reconstructions for further time‐steps which have the same connectivity as the previously extracted mesh while recovering rigid motion exactly. The resulting animated meshes allow accurate motion tracking of arbitrary points and are well suited for animation compression. We demonstrate the qualities of the proposed method by applying it to several data sets acquired by real‐time 3‐D scanners.     

心内膜表面几何模型三维重建算法研究

心内膜三维表面重建是心内膜三维标测系统中的关键问题。为了满足实际应用需求, 根据采集到的散乱点云数据的特点, 提出了一种改进的泊松表面重建算法。在估计表面点云法向量的基础上, 对表面点云法向量进行法向量一致化处理, 有效地控制时间复杂度, 快速重建出平滑的心脏模型。针对泊松表面重建算法中构建MC曲面出现的二义性问题, 提出一种消除二义性的简化改进方法, 可以更加精确地获取模型逼真表面, 提高重建的速度和精度。同时, 可以根据医生的要求, 对重建出的模型实时修正, 满足临床应用。最后, 通过实验验证了算法的有效性和可行性。     

A high-accuracy method for fine registration of overlapping point clouds

This paper presents a high-accuracy method for fine registration of two partially overlapping point clouds that have been coarsely registered. The proposed algorithm, which is named dual interpolating point-to-surface method, is principally a modified variant of point-to-surface Iterative Closest Point (ICP) algorithm. The original correspondences are established by adopting a dual surface fitting approach using B-spline interpolation. A novel auxiliary pair constraint based on the surface fitting approach, together with surface curvature information, is employed to remove unreliable point matches. The combined constraint directly utilizes global rigid motion consistency in conjunction with local geometric invariant to reject false correspondences precisely and efficiently. The experimental results involving a number of realistic point clouds demonstrate that the new method can obtain accurate and robust fine registration for pairwise 3D point clouds. This method addresses highest accuracy alignment with less focus on recovery from poor coarse registrations.     

基于参数限定的CS-RBF曲面重建算法*

针对非密度均匀的点云,提出了一种高效保持特征的曲面重建算法。首先利用八叉树进行点云空间分割,然后对每个点在小邻域内求出局部逼近曲面,建立隐式曲面方程。通过参数限定点的邻域范围,使整个算法既保证了重建效果,又不致于很大程度上增加重建时间,达到了速度和效果在一个范围内的平衡。实验结果证明,本算法重建效果良好,适用于各种散乱点云的重建。     

利用VTK进行三维肠道重建算法的改进

肠道CT的三维重建是提高肠道疾病诊疗准确性的迫切需要。利用可视化工具包VTK并结合VC++,实现了肠道三维重建。经典三维重建Marching Cubes（简称MC）算法会产生二义性,针对常用的渐近线法消除二义性计算量大的问题,提出了一种改进的MC算法：采用线性插值法求出二义性面与等值面的交点,然后分别连接二义性面对边上的交点形成两条相交直线,最后通过判断直线交点的状态值,来唯一地确定等值线的连接方式,从而快速重建出三维肠道。实验结果表明,利用改进的MC算法比起传统MC算法,在三维重建的质量和效率上都得到了很大的提高。     

Robust normal estimation and region growing segmentation of infrastructure 3D point cloud models

Modern remote sensing technologies such as three-dimensional (3D) laser scanners and image-based 3D scene reconstruction are in increasing demand for applications in civil infrastructure design, maintenance, operation, and as-built construction verification. The complex nature of the 3D point clouds these technologies generate, as well as the often massive scale of the 3D data, make it inefficient and time consuming to manually analyze and manipulate point clouds, and highlights the need for automated analysis techniques. This paper presents one such technique, a new region growing algorithm for the automated segmentation of both planar and non-planar surfaces in point clouds. A core component of the algorithm is a new point normal estimation method, an essential task for many point cloud processing algorithms. The newly developed estimation method utilizes robust multivariate statistical outlier analysis for reliable normal estimation in complex 3D models, considering that these models often contain regions of varying surface roughness, a mixture of high curvature and low curvature regions, and sharp features. An adaptation of Mahalanobis distance, in which the mean vector and covariance matrix are derived from a high-breakdown multivariate location and scale estimator called Deterministic MM-estimator (DetMM) is used to find and discard outlier points prior to estimating the best local tangent plane around any point in a cloud. This approach is capable of more accurately estimating point normals located in highly curved regions or near sharp features. Thereafter, the estimated point normals serve a region growing segmentation algorithm that only requires a single input parameter, an improvement over existing methods which typically require two control parameters. The reliability and robustness of the normal estimation subroutine was compared against well-known normal estimation methods including the Minimum Volume Ellipsoid (MVE) and Minimum Covariance Determinant (MCD) estimators, along with Maximum Likelihood Sample Consensus (MLESAC). The overall region growing segmentation algorithm was then experimentally validated on several challenging 3D point clouds of real-world infrastructure systems. The results indicate that the developed approach performs more accurately and robustly in comparison with conventional region growing methods, particularly in the presence of sharp features, outliers and noise.