基于Hessian矩阵的中心路径提取算法

虚拟内窥镜可用来对人体内部管腔结构进行无损检测,在医疗诊断及手术上有着重要意义.要想快速准确地进行虚拟内窥漫游,一个首要的步骤是要先提取出模型的中心线以指导视点的移动.提出了一种基于Hessian矩阵的中心路径提取算法.在距离变换的基础上,先利用Hessian矩阵的几何意义找出中心线的大致形状作为初始路径;然后进行可视性检测以确定最终的路径点,可视球的半径通过Hessian矩阵的特征值自适应地确定;最后用最短路径生成算法得到模型的中心路径.实验结果表明了该算法的有效性.     

基于中轴线约束的最短路径的血管提取算法

血管分割对于血管精确可视化、心血管疾病诊断和血管疾病定量至关重要。本文提出了基于中轴线约束的最短路径的血管提取算法。该算法包括了两次最短路径搜索过程,第一次搜索过程使用了回溯累加技术来提取血管的中轴线,然后结合中轴线信息将其作为血管搜索的能量约束进行第二次搜索过程来提取血管。本文的方法只需要为血管提取设置一个起点,并不需要为血管树复杂的分支结构设置另外的端点。本文使用三维冠状动脉CT血管造影数据来验证本文的算法的可行性和有效性。     

基于最小代价路径的血管中心线提取

为解决传统最小代价路径算法提取血管中心线时存在偏向血管侧壁的问题,提出一种基于点的中心线校正方法。应用最小代价路径算法初步提取中心线,然后根据血管剖面灰阶值呈高斯分布的特点对每个中心点进行校正,再利用三次B样条将离散的中心点拟合为一条连续的中心线。实验结果表明,该算法提取的中心线更靠近血管的中心处,且对噪声具有鲁棒性。此外,将该算法用于起点、终点位置的校正,则提取的中心线对用户定义点的位置不敏感。     

动态环境下的激光条纹中心线提取方法

线结构光三维视觉测量技术最关键的一步是提取出结构光图像中的激光条纹中心线;针对动态测量环境下激光条纹图像存在复杂背景信息、激光光强分布不均、光带各部分宽度差别大、激光条纹断裂等问题,文章研究了一种适用于动态测量环境的激光条纹中心线提取方法;首先通过图像预处理以及自适应裁剪算法提取出感兴趣区域(ROI,region of interest);其次通过改进型伽马校正(IGC,improved gamma correction)以及改进型变阈值大津阈值分割算法(IVT Ostu,improved variable threshold)分割出激光条纹区域;然后使用二维灰度重心法(TD-GBM,two-dimensional gray baryeentric method)提取激光条纹的初始中心线;最终使用二次优化算法对初始中心线进行优化,精确地提取出激光条纹中心线;实验结果表明,相比于灰度重心法、Steger法等算法,文章所提方法受背景干扰以及激光条纹质量的影响较小,能够在多种复杂情况下更精确地提取激光条纹中心线,满足准确性高、稳定性强以及实时性好的要求.     

高分辨率SAR图像高速公路提取算法

给出了一种新的高分辨率合成孔径雷达(SAR)图像高速公路提取算法。首先对SAR图像进行FROST滤波,抑制斑点噪声,再使用OSTU算法对SAR图像进行二值化,然后使用腐蚀-膨胀算法与HOUGH变换对二值化后的SAR图像进行处理,提取出高速公路中的隔离带,再利用圆形模板匹配方法提取高速公路中单行道的道路中心线,最后基于单行道的宽度提取出整个高速公路。实验结果表明,该方法可以消除噪声与干扰的影响,完整地提取出高速公路。     

最短路径原理正射影像镶嵌线自动提取

<正>射影像镶嵌过程中,镶嵌线的选取是一个重要的步骤,其自动化程度是影响全自动镶嵌的一个重要因素。本文提出一种基于图论最短路径原理的正射影像镶嵌线自动提取方法。该算法将图像上提取得到的底层视觉信息(Canny边缘)作为镶嵌过程中的障碍物信息,在边缘图像上根据像素点的邻接关系构建带权有向图,利用初始镶嵌线作为初始条件计算图中有向边的权值,将正射影像镶嵌线的提取过程转化为图论中最短路径问题。实验表明,该算法可以较为准确提取镶嵌线,对全自动镶嵌具有重要应用价值。     

基于Dijkstra算法和OpenCV的交通导航

最短路径是交通路线导航系统的关键问题,使用Dijkstra算法可以有效解决简单有向网络图中任意两个顶点之间的最短路径问题。应用开源计算机视觉库(OpenCV)开发了模拟城市交通导航系统,该导航系统给出了直观的人机交互图像界面,在图像上标记好起讫点后,即运用Dijkstra算法寻找两地间的最短路径和距离,并可用图标标记最短路径节点、线段指示线路,最后模拟小汽车沿着最短路径线路行驶。     

基于W型网络的乳腺动态对比度增强磁共振图像合成

动态对比增强核磁共振成像已被广泛应用于乳腺癌等恶性肿瘤的临床诊断,但是对造影剂过敏患者将无法进行该项检查,提出一种融合边界及区域信息的增强图像生成网络用于生成乳腺虚拟动态增强MRI图像。该网络由一条下采样路径和两条对称的上采样路径组成,使用跳跃连接将下采样路径的低维特征图直接传递到上采样路径上,同时在两条上采样路径间也实现边界与区域特征图的传递。实验结果表明,该方法能够安全、高效及低费用地实现乳腺虚拟动态增强MRI图像合成。     

基于近似最小距离场的二维图像骨架提取方法

提出了基于近似最小距离场提取二值图像的8-连通骨架的算法。该算法对图像中的每个像素根据其与边界的相对距离进行整数编码,形成近似最小距离场,将该距离场中的几何邻接的、具有局部最大值的像素形成聚类,对聚类进行细化,用最短路径将不同的细化后的聚类连接起来。该算法简单,将其在实验数据集上进行实验,结果证明算法具有很高的效率。     

视觉导引智能车的自适应路径识别及控制研究

为了提高智能循迹小车路径识别的精度与行驶速度,提出了一种基于自适应闽值二值化的路径识别算法.首先采用OTSU算法计算出图像分割的最佳阈值,利用此闽值二值化灰度图像;然后提取二值化图像中赛道中心线,得到智能车在赛道中所处位置信息.其次利用线性回归方程对丢失赛道边缘图像拟合直线,根据图像特征完成路径识别.最后采用分段式PID算法实现方向控制,使用增量式PID进行速度调节.测试结果表明,此方法有效提高了不同路径的识别率以及对外界灯光环境的适应性.     

Tubular Structure Segmentation Based on Minimal Path Method and Anisotropic Enhancement

We present a new interactive method for tubular structure extraction. The main application and motivation for this work is vessel tracking in 2D and 3D images. The basic tools are minimal paths solved using the fast marching algorithm. This allows interactive tools for the physician by clicking on a small number of points in order to obtain a minimal path between two points or a set of paths in the case of a tree structure. Our method is based on a variant of the minimal path method that models the vessel as a centerline and surface. This is done by adding one dimension for the local radius around the centerline. The crucial step of our method is the definition of the local metrics to minimize. We have chosen to exploit the tubular structure of the vessels one wants to extract to built an anisotropic metric. The designed metric is well oriented along the direction of the vessel, admits higher velocity on the centerline, and provides a good estimate of the vessel radius. Based on the optimally oriented flux this measure is required to be robust against the disturbance introduced by noise or adjacent structures with intensity similar to the target vessel. We obtain promising results on noisy synthetic and real 2D and 3D images and we present a clinical validation.     

Fast Constrained Surface Extraction by Minimal Paths

In this paper we consider a new approach for single object segmentation in 3D images. Our method improves the classical geodesic active surface model. It greatly simplifies the model initialization and naturally avoids local minima by incorporating user extra information into the segmentation process. The initialization procedure is reduced to introducing 3D curves into the image. These curves are supposed to belong to the surface to extract and thus, also constitute user given information. Hence, our model finds a surface that has these curves as boundary conditions and that minimizes the integral of a potential function that corresponds to the image features. Our goal is achieved by using globally minimal paths. We approximate the surface to extract by a discrete network of paths. Furthermore, an interpolation method is used to build a mesh or an implicit representation based on the information retrieved from the network of paths. Our paper describes a fast construction obtained by exploiting the Fast Marching algorithm and a fast analytical interpolation method. Moreover, a Level set method can be used to refine the segmentation when higher accuracy is required. The algorithm has been successfully applied to 3D medical images and synthetic images.     

基于Fast Marching方法的多目标点路径规划的研究

目前,水下自主机器鱼已经被应用于对水域多个目标点依次进行水质监测,因此有必要研究多个目标点的路径规划。针对遍历多个目标点的路径规划问题,提出一种Multi-Direction Fast Marching（MDFM）方法和遗传算法相结合的路径规划方法。该方法首先使用MDFM方法对工作站和多个目标点两两之间进行路径规划,然后使用遗传算法规划出遍历所有点的最短路径,最后通过仿真实验验证算法的可行性。     

Shape-from-Shading Under Perspective Projection

Shape-from-Shading (SfS) is a fundamental problem in Computer Vision. A very common assumption in this field is that image projection is orthographic. This paper re-examines the basis of SfS, the image irradiance equation, under a perspective projection assumption. The resultant equation does not depend on the depth function directly, but rather, on its natural logarithm. As such, it is invariant to scale changes of the depth function. A reconstruction method based on the perspective formula is then suggested; it is a modification of the Fast Marching method of Kimmel and Sethian. Following that, a comparison of the orthographic Fast Marching, perspective Fast Marching and the perspective algorithm of Prados and Faugeras on synthetic images is presented. The two perspective methods show better reconstruction results than the orthographic. The algorithm of Prados and Faugeras equates with the perspective Fast Marching. Following that, a comparison of the orthographic and perspective versions of the Fast Marching method on endoscopic images is introduced. The perspective algorithm outperformed the orthographic one. These findings suggest that the more realistic set of assumptions of perspective SfS improves reconstruction significantly with respect to orthographic SfS. The findings also provide evidence that perspective SfS can be used for real-life applications in fields such as endoscopy.This research has been supported in part by Tel-Aviv University fund, the Adams Super-Center for Brain Studies, the Israeli Ministry of Science, the ISF Center for Excellence in Applied Geometry, the Minerva Center for geometry, and the A.M.N. fund.     

Path Planning for Autonomous Underwater Vehicles

Efficient path-planning algorithms are a crucial issue for modern autonomous underwater vehicles. Classical path-planning algorithms in artificial intelligence are not designed to deal with wide continuous environments prone to currents. We present a novel Fast Marching (FM)-based approach to address the following issues. First, we develop an algorithm we call FM* to efficiently extract a 2-D continuous path from a discrete representation of the environment. Second, we take underwater currents into account thanks to an anisotropic extension of the original FM algorithm. Third, the vehicle turning radius is introduced as a constraint on the optimal path curvature for both isotropic and anisotropic media. Finally, a multiresolution method is introduced to speed up the overall path-planning process     

利用模糊识别算法分析冠状动脉造影图像中的血管结构

定性与定量地描述冠状动脉血管,很大程度依赖于造影图像中的血管结构识别结果.对此,该文提出了一种多特征模糊识别算法判别血管结构.实现过程中,首先通过图像预处理获得血管初始特征,然后利用一圆周探测器沿血管路径扫描并获取多种局部测度;在定义各种局部测度的多特征模糊子集及其隶属度函数之后,通过构造模糊识别算子准确地判别血管的端、段、分支和交叉结构.该方法在仿真血管模型和多套实际冠状动脉造影图像上获得了较好的效果,对实际图像的结构识别平均正确率达到92.60%.     

基于多尺度的Marching Cube改进算法

在医学图像三维重建中,经典的等值面重建算法Marching Cube是一种比较常用的算法。该算法具有可以在给定阈值的情况下提取任意三维数据场的等值面的优点,但因需计算大量的数据和三角面片而使得该算法速度较慢。提出了使用不同尺度的等值面重建理论,实现了一个多尺度的Marching Cube算法,经过实验对比,该算法比原Marching Cube算法具有效率高、速度快的优点。     

Robot formation motion planning using Fast Marching

This paper presents the application of the Voronoi Fast Marching (VFM) method to path planning of mobile formation robots. The VFM method uses the propagation of a wave (Fast Marching) operating on the world model to determine a motion plan over a viscosity map (similar to the refraction index in optics) extracted from the updated map model. The computational efficiency of the method allows the planner to operate at high rate sensor frequencies. This method allows us to maintain good response time and smooth and safe planned trajectories. The navigation function can be classified as a type of potential field, but it has no local minima, it is complete (it finds the solution path if it exists) and it has a complexity of order n(O(n)), where n is the number of cells in the environment map. The results presented in this paper show how the proposed method behaves with mobile robot formations and generates trajectories of good quality without problems of local minima when the formation encounters non-convex obstacles.