A bintree energy approach for colour image segmentation using adaptive channel selection

A new hierarchical approach called bintree energy segmentation was presented for color image segmentation. The image features are extracted by adaptive clustering on multi-channel data at each level and used as the criteria to dynamically select the best chromatic channel, where the segmentation is carried out. In this approach, an extended direct energy computation method based on the Chan-Vese model was proposed to segment the selected channel, and the segmentation outputs are then fused with other channels into new images, from which a new channel with better features is selected for the second round segmentation. This procedure is repeated until the preset condition is met. Finally, a binary segmentation tree is formed, in which each leaf represents a class of objects with a distinctive color. To facilitate the data organization, image background is employed in segmentation and channels fusion. The bintree energy segmentation exploits color information involved in all channels data and tries to optimize the global segmentation result by choosing the ＂best＂ channel for segmentation at each level. The experiments show that the method is effective in speed, accuracy and flexibility.     

Deformable Registration of MR Brains with Best Features

Introduction Deformable registration is very important formedical image analysis and so far various methodshave been proposed in decades[1-4]. HAMMER reg-istration algorithm[5]defines an attribute vector in-cluding intensity, edge type, and geometric mo-ment invariants as a signature of each point, to re-duce ambiguity in correspondence matching duringthe image registration. Another characteristic ofHAMMER is the hierarchical matching mecha-nism, which helps avoid the warping being trapped…     

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．     

融合边缘检测与区域生长的交通图像分割方法

在交通监控中,如何从复杂的背景中分割运动物体是至关重要的一步,针对车辆的运动阴影对图像分割产生的不利影响,提出了一种新的融合边缘检测与区域生长的彩色图像分割算法,算法同时考虑了图像的彩色信息和空间信息.该算法首先对彩色图像边缘检测,并根据检测结果设置种子像素;再基于颜色相似性生长准则,结合边缘检测结果,对每个种子点进行区域生长;最后,利用区域合并算法对剩余的像素进行合并.实验结果表明该算法很大程度上克服了阴影给图像分割带来的不利影响.     

基于主元分析和色调的彩色图像分割

提出一种基于主元分析和色调的彩色图像分割方法.首先应用主元分析法确定分类数,将彩色图像分成几个区域,然后基于色调特征将区域再划分为若干个子区域,最后根据CIE(L*α,b*)空间的颜色差异合并具有相似颜色的子区域,得到最终的精确分割结果.     

基于多层前馈神经网络的车牌提取方法

文中提出一种采用三层前馈神经网络提取车牌的方法。该方法是一种基于纹理分析的图像分割算法,适合于彩色及灰度图像。实验表明,该方法能准确地提取图像中的车牌。     

噪声符合高斯分布的曲面图像分割

针对噪声符合高斯分布的曲面图像分割问题,提出了一种基于变分水平集方法的隐式曲面上噪声符合高斯分布的图像分割模型。为定义隐式曲面上图像区域划分的动态轮廓线,引入另一个动态水平集函数,其零水平集与隐式曲面的交线即为曲面上区域划分的轮廓线。该模型采用N个水平集函数划分2^N个区域的区域竞争策略。通过变分方法得到图像分割空间轮廓线演化的梯度降方程。通过显式差分格式对演化方程进行离散。实验结果表明,该模型对曲面上噪声符合高斯分布的图像具有良好的分割效果,并且在图像分割的同时能够完全保持曲面固有的几何特征。     

Automatic identification of cracks from borehole image under complicated geological conditions

Identifying cracks from the spread image of a borehole wall is one of the most common usages of borehole imaging method. The manual identification of cracks is time-consuming and can be easily influenced by objective judgment. In this study, firstly, the image translation from RGB color model to HSV color model is done to highlight the structural plane region, which is closer to the color recognition of human sight; secondly, the Saturation component is filtered for further processing and a twice segmentation method is proposed to improve the accuracy of automatic identification. The primary segmentation is based on the statistics of saturation over a longer borehole section and can give a rough estimation of a crack. Then, the pixels are shifted in the reverse direction to the sine curve estimated and make the centerline of the crack flat. Based on the shifted image, the secondary segmentation is done with a small rectangle region that takes the baseline of the roughly estimated crack as its centerline. The result of the secondary segmentation can give a correction to the first estimation. Through verifying this method with actual borehole image data, the result has shown that this method can identify cracks automatically under very complicated geological conditions.     

红外运动目标图像的识别与跟踪技术研究

文中实现了基于红外扫描图像的运动物体自动识别跟踪系统.选择了Butterworth滤波和中值滤波结合的方法对图像进行预处理,基于多帧图像的局部能量的分割算法来实现图像背景的分割,并采用卡尔曼跟踪算法和目标方位确定算法相结合完成对红外小目标的跟踪.在VC++中使用GDI来建立雷达扫描式的极坐标系,将目标运动轨迹在坐标系中显示出模拟轨迹.     

基于头脑风暴优化的PCNN路面裂缝分割算法

为提升裂缝检测的分割精度和鲁棒性,基于头脑风暴优化（brainstorming optimization,BSO）和脉冲耦合神经网络（pulse coupled neural network,PCNN）,提出了一种路面裂缝图像分割算法（BSO-PCNN）. 该算法采用最大熵准则作为BSO算法的适应度函数,并依据适应度值决定参与次轮迭代的个体;BSO具有强收敛性,可快速确定最优个体解;结合图像特征,获得PCNN模型的最优参数,将其代入PCNN模型实现对裂缝图像的分割. 试验结果表明:算法可在20次迭代内取得不同类型路面裂缝图像的最大适应值,从而确定最佳分割参数;与Sobel边缘检测算法、PCNN图像分割算法、基于最大熵的遗传算法（genetic algorithm based on the maximun entropy of the histogram,GA-KSW）、基于遗传算法参数优化的PCNN分割算法（genetic algorithm based on the pulse coupled neural network,GA-PCNN）相比,BSO-PCNN算法取得了0.9924的区域一致性与0.0900的区域对比度.      

Color image segmentation by edge linking and region grouping

A novel method toward color image segmentation is proposed based on edge linking and region grouping. Firstly, the edges extracted by the Canny detector are linked to form regions. Each of the end points of edges is connected by a direct line to the nearest pixel on another edge segment within a sub-window. A new distance is defined based on the feature that the edge tends to preserve its original direction. By sampling the lines to the image, the image is over-segmented to labeled regions. Secondly, the labeled regions are grouped both locally and globally. A decision tree is constructed to decide the importance of properties that affect the merging procedure. Finally, the result is refined by user’s selection of regions that compose the desired object. Experiments show that the method can effectively segment the object and is much faster than the state-of-the-art color image segmentation methods.     

FULLY AUTOMATIC FRAMEWORK FOR SEGMENTATION OF BRAIN MRI IMAGE

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

一种基于MRF的多层遗传递减视频对象分割算法

提出一种视频对象分割方法,将时域信息、空域信息和图像颜色特征有效地结合起来,采用MRF遗传递减式分类来提取视频对象.利用视频帧的颜色特征,在初始分割中采用时空域分水岭方法,建立一个时空域毗连图(ST-RAG);基于时空域毗连图建立马尔可夫随机场(MRF)模型,在分割过程中采用遗传递减式方法对区域进行合理分类,利用形态学进行后处理,分割出感兴趣的视频对象.实验结果表明所获取的分割方法具有灵活性,且精度高.     

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车辆阴影分割是智能交通领域中车辆识别的一项重要内容,阴影分割的好坏直接影响到车辆识别的准确性以及整个智能交通监控系统的性能。针对当前基于RGB和HSV颜色空间的车辆阴影分割算法缺陷与不足,本文提出了一种新的基于YCbCr空间的车辆阴影分割算法。首先选取图像中的运动区域,运动区域包括车辆以及阴影;然后根据阴影区域出现的特点,选择初始阴影数据;最后,通过本文提出的阴影分割算法最终确定阴影区域的形状与位置。经过实际道路运行测试,该算法能提取出的车辆阴影完整性好,具有较好的鲁棒性,在智能交通领域具有一定的应用价值与前景。车辆阴影分割是智能交通领域中车辆识别的一项重要内容,阴影分割的好坏直接影响到车辆识别的准确性以及整个智能交通监控系统的性能。针对当前基于RGB和HSV颜色空间的车辆阴影分割算法缺陷与不足,本文提出了一种新的基于YCbCr空间的车辆阴影分割算法。首先选取图像中的运动区域,运动区域包括车辆以及阴影;然后根据阴影区域出现的特点,选择初始阴影数据;最后,通过本文提出的阴影分割算法最终确定阴影区域的形状与位置。经过实际道路运行测试,该算法能提取出的车辆阴影完整性好,具有较好的鲁棒性,在智能交通领域具有一定的应用价值与前景。     

基于颜色差分特性的图像篡改检测

由于自然场景中目标物体表面的反射特性,以及图像采集过程中的CFA插值算法会使得图像三颜色分量之间具有相关性,特别是颜色分量的高频信息之间有近似相等的关系.拼接篡改操作会降低颜色高频分量的谱间相关性,同时也会给图像带来额外的高频信息.本文利用自然图像颜色通道高频分量之间的相关性,分析颜色差分图像中高频信息的分布情况来检测是否有拼接篡改操作.实验证明本文提取的高频特征分类效果较好,能够检测出图像的拼接篡改边缘.     

基于改进YCrCb颜色空间的肤色分割

人脸识别是计算机视觉、模式识别、生物特征识别、图像处理等多学科交叉的一门科学,肤色分割是人脸识别研究中一个重要的步骤。针对基于YCrCb颜色空间的肤色分割在强光下分割不准确的现状,提出了一种基于改进的YCrCb空间的肤色分割方法,本文利用改进YCrCb颜色空间提高了强光下肤色分割的准确性和适应性。实验证明该算法减弱了光线强弱对人脸肤色分割的影响,更加准确的找到人脸区域,误检率较低。     

基于面向对象分类的密云县城区地面不透水程度分析

利用LandsatTM影像数据,试验了一种基于面向对象分类分析的城区地面不透水程度分析制图方法,包括：对城区多光谱遥感影像进行分割,对分割生成的影像对象层进行面向对象的分类,再在分类的层上执行基于分类的融合,从而获得城区与非城区分布范围的影像对象层;第2次分割影像,获得表示城区独立结构单元的完全不透水地面影像对象层,对该层进行面向对象的分类;第3次分割影像,获得表示城区不同地块的影像对象层,结合第1次和第2次影像分割对象层分类的特征信息对该层进行面向对象的分类,把城区地块按不透水程度大小进行分类;最后输出第3次分割的影像对象层分类结果图,即获得城区地面不透水程度分析图．试验结果表明,面向对象分娄暑一种i舌干分析珐区王lb．面不番水程唐的有张方法     

IMAGE SEGMENTATION BASED ON MARKOV RANDOM FIELD AND WATERSHED TECHNIQUES

IntroductionThere are many methods to perform imagesegmentation and edge detection tasks that incor-porate region- growing and edge detection tech-niques,for example,it is applying edge detectiontechniques to obtain Difference In Strength( DIS)map then employ region growing techniques towork on the map as in Refs.[1 ,2 ]. In the others,combining both special and intensity information inimage segmentation approach based on multi- reso-lution edge detection,region selection and intensi-ty thre…