Structural plane recognition from three-dimensional laser scanning points using an improved region-growing algorithm based on the robust randomized Hough transform

The staggered distribution of joints and fissures in space constitutes the weak part of any rock mass.The identification of rock mass structural planes and the extraction of characteristic parameters are the basis of rock-mass integrity evaluation,which is very important for analysis of slope stability.The laser scanning technique can be used to acquire the coordinate information pertaining to each point of the structural plane,but large amount of point cloud data,uneven density distribution,and noise point interference make the identification efficiency and accuracy of different types of structural planes limited by point cloud data analysis technology.A new point cloud identification and segmentation algorithm for rock mass structural surfaces is proposed.Based on the distribution states of the original point cloud in different neighborhoods in space,the point clouds are characterized by multi-dimensional eigenvalues and calculated by the robust randomized Hough transform(RRHT).The normal vector difference and the final eigenvalue are proposed for characteristic distinction,and the identification of rock mass structural surfaces is completed through regional growth,which strengthens the difference expression of point clouds.In addition,nearest Voxel downsampling is also introduced in the RRHT calculation,which further reduces the number of sources of neighborhood noises,thereby improving the accuracy and stability of the calculation.The advantages of the method have been verified by laboratory models.The results showed that the proposed method can better achieve the segmentation and statistics of structural planes with interfaces and sharp boundaries.The method works well in the identification of joints,fissures,and other structural planes on Mangshezhai slope in the Three Gorges Reservoir area,China.It can provide a stable and effective technique for the identification and segmentation of rock mass structural planes,which is beneficial in engineering practice.

Steganography Using Reversible Texture Synthesis Based on Seeded Region Growing and LSB

Steganography technology has been widely used in data transmission with secret information.However,the existing steganography has the disadvantages of low hidden information capacity,poor visual effect of cover images,and is hard to guarantee security.To solve these problems,steganography using reversible texture synthesis based on seeded region growing and LSB is proposed.Secret information is embedded in the process of synthesizing texture image from the existing natural texture.Firstly,we refine the visual effect.Abnormality of synthetic texture cannot be fully prevented if no approach of controlling visual effect is applied in the process of generating synthetic texture.We use seeded region growing algorithm to ensure texture’s similar local appearance.Secondly,the size and capacity of image can be decreased by introducing the information segmentation,because the capacity of the secret information is proportional to the size of the synthetic texture.Thirdly,enhanced security is also a contribution in this research,because our method does not need to transmit parameters for secret information extraction.LSB is used to embed these parameters in the synthetic texture.

改进的区域生长算法在三维激光点云识别岩体结构面中的应用

交错分布的结构面构成了岩体中的薄弱部位,准确高效的岩体结构面识别和特征信息提取可为岩体稳定性评价提供重要依据。三维激光扫描技术可以极大地提高结构面勘测效率和精度,但目前主流的点云分析算法存在结构面边缘识别模糊、点云分割准确性不能满足结构面特征信息提取精度等问题。因此,考虑岩体结构面点云位置与其邻域的空间关系,利用KD-tree数据结构进行最邻近搜索的体素下采样,在稳健随机Hough变换的基础上改进了区域生长算法,通过多特征值对区域生长分割参数进行修正,依据点云法向量差值和特征终值进行结构面分割,实现了结构面产状、间距、延展度信息的提取。研究结果表明:与传统的主成分分析法和随机抽样一致法相比,在室内块体模型组成的24个结构面中,该方法在相同区域具有更高的识别率和准确率,既能在复杂变化的平面区域保证数据的完整识别,也能在平面的尖锐位置较好地分割边缘点云。利用该方法可以将24个结构面分为6组,并在识别数据中获取对应的结构面特征信息,与实际测量结果相比,角度信息误差约为1°,距离信息误差1cm以内。利用该方法在长江干流蟒蛇寨斜坡岩体中成功识别出3组结构面同时计算各组结构面间距与延展度信息,并采用赤平投影图分析不同结构面组对斜坡稳定性的影响。所提出的方法在室内模型及现场斜坡验证效果良好,可以为岩体结构面识别分割提供稳定且有效的技术支撑。

基于区域生长优化的水利工程设施点云单体化研究

水利工程中输变电设施安全监测是保证水电站安全运行的重要环节,其中基于点云的变形监测需要准确分割出各种输变电设施。为此,通过对三维点云进行降维处理,获取水利工程设施的二维点云图像,基于区域生长算法和点云单体化算法实现分割工作。同时在区域生长算法的基础上提出了一种旋转角度优化算法,通过旋转三维点云数据,得到易于分割的最优角度点云图像,提高了区域生长算法的精度和速度。为验证旋转角度算法的精度,设计了仿真试验分割立柱点云,试验结果精确率达98%以上,表明旋转角度算法可应用于结构点云单体化领域。实际水利工程应用结果表明,所提方法可有效解决普通像素图像过分割的问题,能准确分割水利工程不同区域及设施。

基于车载激光点云数据的城市道路面提取方法研究

随着智慧城市建设的不断发展以及城市交通管理难度的日益增加,准确获取道路信息的研究已成为热点。本文针对城市结构化道路的空间特征,提出了一种基于车载激光扫描点云数据的道路面提取方法。该方法实现道路面提取的有效步骤为:首先对原始车载点云数据进行抽稀、降噪等预处理,使用一种布料模拟滤波(Cloth Simulation Filter, CSF)算法对预处理后点云数据进行滤波,去除非地面点对后续算法的影响;其次,使用主成分分析法估算得到各地面点局部法向量与曲率值;最后,使用改进的区域生长算法,结合各参数设置实现道路面点云的精确提取。通过两段实测数据对本文提出方法进行检验,实验结果的完整性r、准确性p以及检测质量q均在94%以上,表明在不同点云场景下,使用本文方法均能得到良好的提取结果,具有较高的适应性。

基于道路扫描点云数据的城市道路标线提取

提出了一种基于车载激光扫描点云的道路标线提取方法。该方法实现道路标线提取的流程为:首先,为尽可能减少无用点云数据对道路标线提取的影响,使用布料模拟滤波(Cloth Simulation Filter,CSF)算法提取地面点,滤除非地面点,并利用基于法向量的区域生长算法提取得到路面点;其次,将路面点投影为强度特征图,根据标线几何特征提取标线边缘;最后,根据道路标线边缘信息实现标线候选点提取,并利用高斯混合模型(Gaussian Mixture Model,GMM)将噪声点剔除。使用两段道路点云数据对本文方法进行验证,结果表明本文方法提取道路标线点结果的精度指标均优于对比算法提取结果,能够提取较为完整的道路标线。

An adaptive region growing algorithm for breast masses in mammograms

This study attempted to accurately segment the mammographic masses and distinguish malignant from benign tumors.An adaptive region growing algorithm with hybrid assessment function combined with maximum likelihood analysis and maximum gradient analysis was developed in this paper.In order to accommodate different situations of masses,the likelihood and the edge gradients of segmented masses were weighted adaptively by the use of information entropy.106 benign and 110 malignant tumors were included in this study.We found that the proposed algorithm obtained segmentation contour more accurately and delineated the tumor body as well as tumor peripheral regions covering typical mass boundaries and some spiculation patterns.Then the segmented results were evaluated by the classification accuracy.42 features including age,intensity,shape and texture were extracted from each segmented mass and support vector machine(SVM)was used as a classifier.The classification accuracy was evaluated using the area(A_(z))under the receiver operating characteristic(ROC)curve.It was found that the maximum likelihood analysis achieved an A_(z)value of 0.835,the maximum gradient analysis got an A_(z)value of 0.932 and the hybrid assessment function performed the best classification result where the value of A_(z)was 0.948.In addition,compared with traditional region growing algorithm,our proposed algorithm is more adaptive and provides a better performance for future works.

A Fast Algorithm for Improving the Visual Distance in Fog

Images captured outdoor usually degenerate because of the bad weather conditions,among which fog,one of the widespread phenomena,affects the video quality greatly.The physical features of fog make the video blurred and the visible distance shortened,seriously impairing the reliability of the video system.In order to satisfy the requirement of image processing in real-time,the normal distribution curve fitting technology is used to fit the histogram of the sky part and the region growing method is used to segment the region of sky.As for the non-sky part,a method of self-adaptive interpolation to equalize the histogram is adopted to enhance the contrast of the images.Experiment results show that the method works well and will not cause block effect.

基于ORB与改进区域生长法的指针式仪表读数方法

为进一步提高倾斜仪表图像自动读数的准确度,提出了一种基于ORB和改进区域生长法的倾斜指针式仪表自动读数方法。首先,采用ORB算法检测目标图像和模板图像的特征点,根据匹配的特征点计算变换矩阵,采用透视变换实现倾斜矫正;然后,利用最小二乘法检测模板图像圆心,将逆变换后的圆心作为种子点,采用基于指针面积的灰度自适应阈值作为生长准则检测指针区域;最后,采用投影法和霍夫变换提取指针角度,根据量程与角度关系计算仪表读数。实验结果表明,所提读数方法的最大相对误差为2.96%,平均花费时间为400 ms,读数精度与效率均能够满足实际工程的读数要求,对推进能源物联网和数字化电网的建设有积极作用。

基于机载LiDAR点云数据的历史文化街区重建方法研究

为了提高历史街区重建过程中建筑物轮廓与实际建筑物激光脚点的吻合精度,提出了一种基于机载LiDAR点云数据的历史文化街区重建方法。首先,对机载LiDAR点云数据作预处理,去除点云数据中的噪声。其次,利用区域生长算法对激光脚点作分割,完成建筑物点云数据的获取。之后,采用Alpha Shape算法提取建筑物特征信息,得到轮廓线,再通过管子算法将轮廓线规则化。针对多边形轮廓线,采用分类强制正交法对其作规则化。最后,通过平面拟合、直线拟合等方法,对历史文化街区进行三维重建。结果表明,所提出的重建方法在提取建筑物轮廓时,激光脚点吻合更精确,并且重建耗时为25 min,优于文献所提的2种方法。基于机载LiDAR点云数据的历史文化街区重建方法,可以在一定程度上提高历史文化街区的重建质量,并为其他历史文化街区的保护与更新提供借鉴。

基于车载激光扫描的城市典型道路面点云提取

针对城市典型道路结构特征,提出一种车载道路面点云数据提取方法。根据高程阈值算法对原始车载激光扫描点云数据进行滤波处理,得到地面点,再使用改进区域生长算法对地面点进行处理,提取得到道路面点云。通过实测车载激光扫描数据进行实验,结果表明,本文提出方法提取道路面点云结果的检测质量q、完整性r以及准确性p均在93%以上,本文方法是行之有效的。

基于区域生长算法的汉字笔画统计与分析

针对现阶段汉字笔画数据集划分时所含笔画类别较少的问题,设计符合汉字特征的生长控制策略和算法框架,实现汉字笔画小类别的划分。同时,采用统计学方法,对汉字不同笔画的出现频率进行分析,并探究笔画与汉字语义之间的联系,构建了新的汉字笔画数据集,为字体设计中汉字笔画拼接奠定基础。使用GB2312编码和Unicode编码对《信息交换用汉字编码字符集》中的6763个汉字及32类笔画进行编码。通过对样本数据的实验验证,算法在汉字笔画的识别和统计分析方面表现良好,构建的汉字笔画数据集为汉字的研究以及文化传承提供了有力的技术支持。

Improved region growing segmentation for breast cancer detection:progression of optimized fuzzy classifier

Purpose-Breast cancer is one of the most common malignant tumors in women,which badly have an effect on women’s physical and psychological health and even danger to life.Nowadays,mammography is considered as a fundamental criterion for medical practitioners to recognize breast cancer.Though,due to the intricate formation of mammogram images,it is reasonably hard for practitioners to spot breast cancer features.Design/methodology/approach-Breast cancer is one of the most common malignant tumors in women,which badly have an effect on women’s physical and psychological health and even danger to life.Nowadays,mammography is considered as a fundamental criterion for medical practitioners to recognize breast cancer.Though,due to the intricate formation of mammogram images,it is reasonably hard for practitioners to spot breast cancer features.Findings-The performance analysis was done for both segmentation and classification.From the analysis,the accuracy of the proposed IAP-CSA-based fuzzy was 41.9%improved than the fuzzy classifier,2.80%improved than PSO,WOA,and CSA,and 2.32%improved than GWO-based fuzzy classifiers.Additionally,the accuracy of the developed IAP-CSA-fuzzy was 9.54%better than NN,35.8%better than SVM,and 41.9%better than the existing fuzzy classifier.Hence,it is concluded that the implemented breast cancer detection model was efficient in determining the normal,benign and malignant images.Originality/value-This paper adopts the latest Improved Awareness Probability-based Crow Search Algorithm(IAP-CSA)-based Region growing and fuzzy classifier for enhancing the breast cancer detection of mammogram images,and this is the first work that utilizes this method.

图像分割算法研究综述

图像分割是图像处理中的一个经典难题,也是图像处理和计算机视觉领域中的基本技术。目前,广大研究者在图像分割领域里已提出了上百种分割方法,每种分割方法只局限特定的分割对象,至今没有一种通用的方法。本文综述了近年在图像分割技术上出现的常用图像分割算法以及它们的优缺点,展望了图像分割的前景和面临的挑战。

基于灰色关联分析和区域生长的微小缺陷提取

提出了一种基于灰色关联分析的区域生长算法。首先通过计算二维灰色绝对关联度,将原始图像转换为仅包含高频信息的灰色关联图像,并在此基础上进行阈值处理和区域标记,获得缺陷区域的形心。然后将该形心作为种子像素在灰色关联图像内进行区域生长,实现微小缺陷的在线提取。实验结果表明,新的区域生长算法能够有效地抑制背景噪声、简化计算过程,满足在线提取和实时性要求。

几种图像分割算法在CT图像分割上的实现和比较

对目前几种在图像分割领域得到较多应用的交互式分割、区域生长分割以及阈值分割算法进行了探讨 ,并且结合实际 CT片图例分别进行分割实验研究 ,得到较为满意和可用性强的结果 .实验表明 :阈值分割对于 CT切片的效果最好 ;区域生长分割适宜于对面积不大的区域进行分割 ,分割效果较好 ;基于动态规划的交互式分割算法比较复杂 ,计算时间较长 ,但对于边缘较平滑的区域 ,同样具有较好的实际效果 .几种算法的评估为其在

改进的分水岭图像分割算法

针对分水岭图像分割算法对噪声敏感和易于产生过分割现象,提出了一种基于粒子群和区域生长的改进分水岭算法。该算法将区域生长与分水岭分割算法相结合,依据香农熵构建一个目标函数,确定区域生长参数;利用灰度均值计算区域间的差异度,将比较小的区域合并到与之相邻的差异度最小的区域中;利用粒子群算法对该目标函数进行全局寻优,实现图像分割。实验证明新算法较已有的几种分割算法有了很大提高,并有效地解决了分水岭算法的过分割问题,分割结果更加符合人的直观视觉特性,是一种有效、准确且实用的图像分割方法。

基于3D区域增长法和改进的凸包算法相结合的全肺分割方法

肺实质分割结果的准确性在实际临床应用中具有非常重要的意义。但由于肺结节的位置、大小、形状的不规则性,肺部病变的多样性,以及人体胸部解剖结构的明显差异等,使得各类分割方法不能统一地适用于所有的胸部CT图像,所以对于肺实质分割方法的研究仍具有很大的挑战。该文在国内外研究分析的基础上提出基于3D区域增长法与改进的凸包修补算法相结合的全肺分割方法。在3D区域增长法的粗分割基础上,对分割的结果进行细化工作,通过连通域标记法与形态学方法相结合去除气管和主支气管,得到初步的肺实质掩膜,最后应用改进的凸包算法对肺部轮廓进行修补平滑,最终得到肺部分割结果。通过与凸包算法及滚球法相对比,证明该文所提改进的凸包算法能够有效地修补肺部轮廓凹陷,修补后的结果分割精度较高。

基于图像处理的烟支污点面积测量方法

针对目前烟支污点检测人工测量的各种问题,提出一种基于图像处理技术的烟支污点面积测量方法。该方法以机器视觉系统为硬件平台,首先采用柱面模型进行图像转换,建立图像坐标系与展开坐标系的映射关系;然后利用正切函数对图像作增强处理,增强目标与背景的对比度;最后通过改进的区域生长法分割污点,排除烟支周期纹理和颜色分布的干扰。实验结果表明:该方法测量效率高,稳定性强,精度达到0.03 mm2,能够满足烟支污点的检测需求。

CT图像的颈部淋巴结半自动分割算法

针对手动分割颈部淋巴结的局限性,提出一种在较少人为干预下,准确批量分割颈部淋巴结的算法。采用遍历阈值算法提取疑似淋巴结种子点,加入基于统计学的强制停止条件,对种子点进行区域生长,依据淋巴结和周围软组织生理特征及其图像特性,提出颈部淋巴结判决算法。实验结果表明,颈部淋巴结体积的正确分割比TPVP为94.00%,阳性预测值PV为97.11%,无漏判,精确度达86.33%,该算法能实现颈部淋巴结精确批量分割,为临床诊断、治疗和预后提供有效辅助依据。