基于组合隶属度的快速模糊聚类算法

为了改善模糊C-均值聚类算法(FCM)对噪声图像的分割效果,Stelios等提出了鲁棒性的模糊局部C-均值聚类算法(FLICM),通过引入模糊因子,充分利用邻域像素的灰度信息和空间信息,提高了算法对噪声的鲁棒性,但因每次迭代必须计算邻域像素到聚类中心的距离,导致耗时高、效率低.针对该问题,提出了基于组合隶属度的快速模糊聚类算法,通过构造组合隶属度函数,对迭代中的隶属度矩阵直接进行滤波处理,避免了计算邻域信息耗时较高的缺点,组合隶属度函数不仅考虑了隶属度的局部信息,而且考虑了隶属度的空间信息,在确保算法对图像分割精度的前提下,降低了算法的时间复杂度.实验表明,基于组合隶属度的快速模糊聚类算法可在较短时间内完成高精度的图像分割.     

基于改进的FCM在人脑MR图像分割中的应用

为解决模糊C-均值聚类（FCM）算法在医学图像分割中存在计算量大、运行时间过长以及样本集不理想会导致不好的聚类结果的问题,提出了相应的改进算法．利用收敛速度快的K均值聚类法得到的聚类中心作为FCM算法的初始聚类中心,并将样本对于各个聚类的隶属度之和为1这一约束条件,改变为所有样本对各类的隶属度总和等于样本总数．实验表明,该方法用于人脑磁共振图像分割时,运行速度提高了近3倍,分割准确度明显得到提高．     

基于MS-RG混合图像分割模型的道路检测研究

道路场景因其结构的多样性、纹理变化的复杂性和自然曝光的不稳定性,使得传统基于道路分割的道路检测方法大多存在信息冗余,并且存在边界丢失、模糊等质量问题.本文首先在道路图像上使用 Meanshift均值漂移算法,通过空间内的概率密度呈梯形上升去寻找局部最优,并搜索属于同一模点的像素然后生成获得超像素块.然后利用 Meanshift算法获得的聚类超像素块进行多种子点区域生长,规范生长规则,克服不能得到封闭边界的缺陷,改进道路图像的分割效果.实验结果表明,本文提出的模型适用性强,相比于传统方法有效地提升了分割准确性和实时性,可准确识别出图像中的道路信息,确保车辆能够行驶在可行驶区域上.     

一种噪声车牌的模糊预处理方法

本文提出了一种车牌模糊预处理方法,主要用于解决现场车牌图像模糊不清、对比度不强,以及传统二值化方法带来的噪声、粘联、变形等不理想现象。通过采用模糊增强技术采增强车牌图像的对比度,便于后续的分割、识别等操作;并提出应用模糊c均值算法采确定车牌图像二值化中的聚类阈值,从而实现对车牌图像的二值化,并将二值化结果与传统的Otsu二值化方法进行了对比。实验结果显示,应用本方法处理车牌噪声和粘联等情况具有较好的优越性。     

基于粗约简的数据流增量聚类算法

针对数据流聚类算法CluStream需预先指定微聚类数目无法准确描述数据流的变化,进而影响最终聚类结果的缺陷,提出了基于粗约简的数据流增量聚类算法RICStream(rough incremental clustering stream).该算法在保证聚类精度的前提下,对参与聚类的数据流属性进行动态调整,有效地减少了聚类时间和计算量.提出了一种可增量调整的网格结构以存储数据流,保证了聚类结果能有效反映数据流的变化情况.基于真实数据集和仿真数据集的实验结果表明,RICStream算法具有较高的效率和聚类精度.     

基于HSI空间的模糊C均值彩色图像分割方法

给出了一种在HSI空间上基于模糊C均值的彩色图像分割方法．首先对每个像素根据H分量和I分量计算出4个隶属度,然后将其中的两个隶属度结合形成一个二雏特征矢量来表征像素的全部颜色特征,最后对二维矢量运用模糊C均值聚类算法得到最终的彩色图像分割结果．     

分水岭算法在胃上皮内肿瘤图像分割中的应用

图像分割是图像识别的关键,笔者曾经分别采用松弛迭代和K均值聚类方法对胃上皮内肿瘤图像进行分割,实验表明这些算法对粘连严重的图像分割效果很差,故本文应用分水岭分割算法Vincent和Inver,对粘连情况不同的多类胃上皮内肿瘤图像进行了图像分割实验,实验结果表明：对于粘连较少的细胞图像,这两种算法都能较好地分离出目标细胞,但对于粘连严重的细胞图像,Inver算法的分割效果比Vincent要好,但lnver算法容易出现过分割现象．     

基于优化模糊C均值算法的锚泊船聚集特性

针对模糊C均值算法随机选择初始聚类中心导致聚类结果对噪声样本点敏感性的不足, 采用局部密度加权的方法, 将初始聚类中心的选择范围限制在局部密度较高样本点区域, 优化初始聚类中心的选择方法; 利用样本点的局部密度改进目标函数, 提高局部密度较高的样本点在目标函数迭代过程中的影响力, 从而提升模糊C均值算法的聚类性能, 并采用人造数据集和鸢尾花真实数据集验证优化的局部密度模糊C均值算法的聚类效果; 通过计算锚泊船位置数据的局部密度, 分析了船舶锚泊偏好。试验结果表明: 对比模糊C均值算法, 优化的局部密度模糊C均值算法聚类精准率提高了2.9%, 召回率提高了3.8%, F度量值提高了3.9%, 说明优化的局部密度模糊C均值算法的性能优于模糊C均值算法; 在锚泊船位置数据上的聚类结果正确反映了天津港锚泊船的聚集特点和锚泊偏好, 其结果与船舶的常规做法一致, 说明优化的局部密度模糊C均值聚类算法是一种分析锚泊船聚集特性和锚泊偏好的有效方法。      

基于激光雷达的无人驾驶障碍物检测和跟踪

针对激光雷达动态障碍物检测与跟踪过程中聚类适应性差、实时性低和跟踪准确度不高等问题，提出一种自适应的密度聚类算法和多特征数据关联方法，分别用于检测和跟踪. 首先，对激光雷达采集的点云进行路沿检测、感兴趣区域提取和地面分割等预处理，去除无关点云；然后，基于自适应的密度聚类算法对非地面的点云进行聚类，完成障碍物点云检测；最后，利用加权多特征数据关联算法结合卡尔曼滤波器实现对动态障碍物跟踪. 通过实验表明:本算法能够根据10 Hz的激光雷达数据实现对障碍物准确、稳定的检测和跟踪，且聚类时间缩短32%.      

应用形态学方法提高SAR目标检测中的聚类效果

Lincoln实验室提出的SAR(synthetic aperture radar)ATR(automatic target recognition)算法由于其经典性而被广泛采用,该算法为三级结构模式,处于其中Prescreener级和Discriminator级之间的聚类算法对于检测算法整体性能有重要的影响,文中介绍了SAR ATR算法采用的常规聚类算法,分析了常规算法在聚类过程中存在的杂波干扰问题,针对问题在聚类前引入形态学操作方法,将待聚类图像中包含的孤立点删除而只保留团状分布的样本,从而消除了杂波点对聚类的干扰,基于实际SAR图像的聚类结果验证了应用形态学方法对提高聚类效果的有效性.     

Fusion of remote sensing images based on nonsubsampled contourlet transform and region segmentation

The purpose of remote sensing images fusion is to produce a fused image that contains more clear, accurate and comprehensive information than any single image. A novel fusion method is proposed in this paper based on nonsubsampled contourlet transform (NSCT) and region segmentation. Firstly, the multispectral image is transformed to intensity-hue-saturation (IHS) system. Secondly, the panchromatic image and the component intensity of the multispectral image are decomposed by NSCT. Then the NSCT coefficients of high and low frequency subbands are fused by different rules, respectively. For the high frequency subbands, the fusion rules are also unalike in the smooth and edge regions. The two regions are segregated in the panchromatic image, and the segmentation is based on particle swarm optimization. Finally, the fusion image can be obtained by performing inverse NSCT and inverse IHS transform. The experimental results are evaluated by both subjective and objective criteria. It is shown that the proposed method can obtain superior results to others.     

Lesion segmentation and identification of breast tumor on dynamic contrast-enhanced magnetic resonance imaging

Dynamic contrast-enhanced magnetic resonance imaging （DCE-MRI） can show subtle lesion morphol- ogy, improve the display of lesion definitions, and objectively reflect the blood supply of breast tumors; it can also reflect different strengthening patterns of normal tissues and lesion areas after medical tracer injection. DCE-MRI has become an important basis for the clinical diagnosis of breast cancer. To DCE-MRI data acquired from several hospitals across multiple provinces, a series of in-silico computational methods were applied for lesion segmentation and identification of breast tumor in this paper. The image segmentation methods include Otsu segmentation of subtraction images, signal-interference-ratio segmentation method and an improved variational level set method, each has its own application scope. After that, the distribution of benign and malignant in lesion region is iden- tified based on three-time-point theory. From the experiment, the analysis of DCE-MRI data of breast tumor can show the distribution of benign and malignant in lesion region, provide a great help for clinicians to diagnose breast cancer more expediently and lay a basis for medical diagnosis and treatment planning.     

FULLY AUTOMATIC FRAMEWORK FOR SEGMENTATION OF BRAIN MRI IMAGE

Automaticbraintissuesegmentationfrommag neticresonanceimages(MRI)isofgreatimportance forresearchandclinicalstudyofmuchneurological pathology.Duringthepastdecade,theMRIhashad agreatimpactonthediagnosticimagingofmosthu manorgansystem.Thesegmentationofbrai…     

复杂背景下多目标图像分割方法研究

真实路况中的运动车辆图像进行图像分割时,图像中往往存在多个车辆车牌信息,且这些车牌信息具有尺度不一,位置随机等特点,加之光照及复杂背景的影响,如何兼顾多个车辆车牌的分割效果是车辆检测和跟踪领域亟待解决的问题.为了解决这类工程应用中的问题,需要在尺度空间下对多目标图像进行分析.因本文在前期多尺度分割模型的基础上引入视觉注意机制,利用不变性特征实现多目标的定位及最优分割尺度的选取.经大量实验测试结果表明,该算法较好地实现了图像中多个车牌图像的分割并且具有较好的分割效果.     

车辆牌照自动识别系统中的字符分割技术

车辆牌照定位以后,需要进行字符分割,以便系统自动识别。介绍了一种基于阈值化与投影法的分割技术,详细阐述了车牌图像归一化、域值化和字符分割的具体过程,算法简单实用,经过实验验证,可以满足实时自动识别系统的要求。     

IMAGE ANALYSIS BASED ON EDGE DETECTION TECHNIQUES

IntroductionEdges are pixels where brightness changesabruptly and often used in image analysis for find-ing region boundaries.It locates sharp changes inthe intensity function.Edges detection is basic im-age features.They carry useful information aboutobject boundaries.Edges can be used for object i-dentification,image analysis and image filteringapplications as well.We shall consider as an edgethe border between two homogeneous image re-gions having different illumination.This definitionimp…     

Image series segmentation and improved MC algorithm

A semiautomatic segmentation method based on active contour is proposed for computed tomography （CT） image series. First, to get initial contour, one image slice was segmented exactly by C-V method based on Mumford-Shah model. Next, the computer will segment the nearby slice automatically using the snake model one by one. During segmenting of image slices, former slice boundary, as next slice initial contour, may cross over next slice real boundary and never return to right position. To avoid contour skipping over, the distance variance between two slices is evaluated by an threshold, which decides whether to initiate again. Moreover, a new improved marching cubes （MC） algorithm based on 2D images series segmentation boundary is given for 3D image reconstruction. Compared with the standard method, the proposed algorithm reduces detecting time and needs less storing memory. The effectiveness and capabilities of the algorithm were illustrated by ,experimental results.     

Unsupervised color-texture image segmentation

The measure J in J value segmentation （JSEG） fails to represent the discontinuity of color, which degrades the robustness and discrimination of JSEG. An improved approach for JSEG algorithm was proposed for unsupervised color-texture image segmentation. The texture and photometric invariant edge information were combined, which results in a discriminative measure for color-texture homogeneity. Based on the image whose pixel values are values of the new measure, region growing-merging algorithm used in JSEG was then employed to segment the image. Finally, experiments on a variety of real color images demonstrate performance improvement due to the proposed method.     

基于Kittler最小误差分割算法的小视场星图分割

小视场星图易受光照不均和噪声影响,常用阈值分割算法存在处理效果不佳或效率较低的不足.针对星图灰度的高斯分布特征,基于贝叶斯最小误差理论,提出利用Kittler最小误差分割算法处理小视场星图.以视频测量机器人为测量平台,以"优度法"、区域一致性、区域对比度和时间复杂度为评价指标,对比了常用的阈值分割算法和一维最大熵法,验...