一种基于区域分级合并的彩色图像分割方法

在区域合并过程中,手工设置颜色相似性和边界距离的权重极大地影响了分割的精度和自动化.针对这一问题,提出了一种新的基于区域分级合并的彩色图像分割算法.该方法能够根据邻接区域的边界特点设置权重因子,从而自适应地融合区域的颜色相似性和边界距离.使用均值漂移算法对图像进行初始分割,将原图像分割为具有较好边界的同质区域;通过计算区域相似度对区域进行分级合并.多幅彩色图像的分割实验结果证明,所提算法优于传统的基于区域合并的方法.     

一种改进的桥梁图像分水岭分割算法

针对传统分水岭分割方法存在的过分割问题,提出了一种改进的桥梁图像分水岭分割算法。该算法首先对桥梁裂缝图像进行高低帽形态学滤波,并运用多尺度梯度算子提取梯度图像,在分水岭变换之前使用自适应的标记提取方法对区域极小值进行标定,然后对初步分水岭分割的过分割区域使用改进fisher距离的区域合并算法进行合并,取散度作为停止度量。实验表明,该算法减少了分水岭算法的过分割现象,提高了桥梁图像分割的精确性,具有很好的鲁棒性和适应性。     

基于上下文分析的无监督分层迭代算法用于SAR图像分割

基于聚类的分割算法能够有效地分析目标特征在特征域的分布结构,进而准确判断目标的所属类别,但难以利用图像的空间和边缘信息,而基于区域增长的分割算法能够在空间域利用多种图像信息计算目标之间的相似性,但缺乏对特征结构本身的深层挖掘,容易出现欠分割或过分割的结果. 本文结合这两种算法各自的优势,针对合成孔径雷达（Synthetic aperture radar,SAR）图像的特点,提出了一种基于上下文分析的无监督分层迭代算法. 该算法使用过分割区域作为操作单元,以提高分割速度,降低SAR图像相干斑噪声的影响. 在合并过分割区域时,该算法采用了分层迭代的策略:首先,设计了一种改进的模糊C均值聚类算法,对过分割区域的外观特征进行聚类分析,获得其类别标记,该类别标记包含了特征的分布结构信息. 然后,利用多种SAR图像特征对同类区域的空域上下文进行分析,使用区域迭代增长算法对全局范围内的相似区域进行合并,直到不存在满足合并条件的过分割区域对为止,再重新执行聚类算法. 这两种子算法分层交替迭代,扬长避短,实现了一种有效的方法来组织和利用多种信息对SAR图像进行分割. 对模拟和真实SAR图像的实验表明,本文提出的算法能够在区域一致性和细节保留之间做到很好的平衡,准确地分割出各类目标区域,对相干斑噪声具有很强的鲁棒性.     

A stochastic gravitational approach to feature based color image segmentation

In this paper, a novel image segmentation algorithm based on the theory of gravity is presented, which is called as “stochastic feature based gravitational image segmentation algorithm (SGISA)”. The proposed SGISA uses color, texture, and spatial information to partition the image into homogenous and semi-compact segments. The proposed method benefits from the advantages of both clustering and region growing image segmentation techniques. The SGISA is equipped with a new operator called “escape” that is inspired by the concept of escape velocity in physics. Moreover, motivated by heuristic search algorithms, we incorporate a stochastic characteristic with the SGISA, which gives algorithm the ability to search the image for finding the fittest regions (pixels) that are suitable for merging. Several experiments on various standard images as well as Berkley standard image database are reported. Results are compared with a well-known clustering based segmentation method, C-means, a gravitational based clustering method (SGC), and the well-known mean-shift method. The results are reported using unsupervised criteria and pre-ground-truthed measures. The obtained results confirm the effectiveness of the proposed method in color image segmentation.     

SAR imagery segmentation by statistical region growing and hierarchical merging

This paper presents an algorithm to segment synthetic aperture radar (SAR) images, corrupted by speckle noise. Most standard segmentation techniques may require speckle filtering previously. Our approach performs radar image segmentation using the original noisy pixels as input data, i.e. without any preprocessing step. The algorithm includes a statistical region growing procedure combined with hierarchical region merging. The region growing step oversegments the input radar image, thus enabling region aggregation by employing a combination of the Kolmogorov–Smirnov (KS) test with a hierarchical stepwise optimization (HSWO) algorithm for performance improvement. We have tested and assessed the proposed technique on artificially speckled image and real SAR data.     

Color image segmentation based on multiobjective artificial bee colony optimization

This paper presents a new color image segmentation method based on a multiobjective optimization algorithm, named improved bee colony algorithm for multi-objective optimization (IBMO). Segmentation is posed as a clustering problem through grouping image features in this approach, which combines IBMO with seeded region growing (SRG). Since feature extraction has a crucial role for image segmentation, the presented method is firstly focused on this manner. The main features of an image: color, texture and gradient magnitudes are measured by using the local homogeneity, Gabor filter and color spaces. Then SRG utilizes the extracted feature vector to classify the pixels spatially. It starts running from centroid points called as seeds. IBMO determines the coordinates of the seed points and similarity difference of each region by optimizing a set of cluster validity indices simultaneously in order to improve the quality of segmentation. Finally, segmentation is completed by merging small and similar regions. The proposed method was applied on several natural images obtained from Berkeley segmentation database. The robustness of the proposed ideas was showed by comparison of hand-labeled and experimentally obtained segmentation results. Besides, it has been seen that the obtained segmentation results have better values than the ones obtained from fuzzy c-means which is one of the most popular methods used in image segmentation, non-dominated sorting genetic algorithm II which is a state-of-the-art algorithm, and non-dominated sorted PSO which is an adapted algorithm of PSO for multi-objective optimization.     

基于K均值聚类与区域合并的彩色图像分割算法

提出一种基于K均值聚类与区域合并的彩色图像分割算法。首先,对图像运用mean shift算法进行滤波,在对图像进行平滑的同时保持图像的边缘;然后,运用K均值算法对图像在颜色空间进行聚类,得到初始分割的结果;最后,给出了一种区域合并策略,对初始分割获得的区域进行合并,得到最终的分割结果。仿真结果表明,算法的分割结果和人的主观视觉感知具有良好的一致性。     

融合多特征的均值漂移彩色图像分割方法

针对均值漂移图像分割方法中只考虑图像颜色和空间信息,对纹理丰富的图像不能进行有效分割的情况,提出一种新的融合图像颜色、纹理和空间等低层特征信息的图像分割方法.用极性、各向异性和对比度来表示图像的纹理信息,并结合颜色和空间信息形成图像分割特征;然后用均值漂移进行图像滤波;最后,进行区域合并得到分割结果.实验结果表明,该方法对纹理丰富的自然风景图像有较好的分割效果.     

基于超像素的高分遥感影像分割算法

针对高分遥感影像中存在地物数目多,特征信息复杂导致分割边缘不清晰、对象细节丢失等问题,提出一种改进的超像素分割和多特征结合的遥感影像分割合并算法。在对图像进行分割前的预处理阶段,使用超像素分割技术得到初始分割图像;区域合并过程中,基于对象间的异质性和对象内部的同质性,结合光谱、纹理和形状特征,对对象进行合并;通过调整全局分割参数来调整合并尺度,得到最终的影像分割结果。实验结果表明,所提方法能得到较好的影像分割效果。     

一种融合聚类与区域生长的彩色图像分割方法

论文提出了一种将聚类和区域生长有机融合的彩色图像分割方法。为了捕获图像的纹理特征,首先将图像划分成16×16子块,然后在块中按照视觉一致性准则进行颜色聚类,对于聚类后的子块,提取其颜色与纹理特征,然后采用符合人类视觉特征的生长规则,进行基于子块的区域生长。该方法充分利用了聚类算法和区域生长算法的各自优点,并符合人类视觉特征的分割策略。利用提出的算法对多幅自然图像进行了分割实验,实验结果证明了算法的有效性。     

Extraction of diffuse objects from images by means of watershed and region merging: example of solar images.

This paper presents a new method for extraction of diffuse objects from images, which was developed for segmentation of solar images obtained from extreme-UV imaging telescope (EIT) experiments of the satellite SOHO mission. As a particular type of objects to be extracted coronal holes in EIT images have been chosen. The method described is based on the use of a watershed algorithm. The result of the watershed segmentation is a partition of the whole domain of the image into a large number of small regions. These regions are then combined in a region merging process. The proposed region merging algorithm iteratively adds the darkest regions and maximizes the average contrast between a current mask and a set of its neighboring regions. We show that the maximization of the average contrast gives segmentation results that are visually acceptable. Furthermore, this approach allows us to conduct the segmentation of EIT images independently of any explicit fine-tuning parameters. The proposed method was extensively tested on EIT images obtained at various times and various levels of solar activity, and we will show that it can be used independently of the local brightness level and the extent of coronal holes.     

基于区域生长的翡翠云雾纹理分割方法

根据翡翠云雾纹理特征,提出基于区域生长算法的翡翠纹理分割方法。首先,将图像转换到YCbCr颜色空间,根据统计的翡翠CbCr分量范围,利用边界跟踪算法确定分析区域,然后针对CbCr分量进行FCM聚类,选定生长区域种子,根据生长区域颜色相近和纹理相似的特征,进行区域生长,利用灰度共生矩阵分析Y分量进行纹理特征提取,针对分割效果影响因素,进行区域合并。实验表明该方法的分割效果良好。     

Automatic tongue image segmentation based on gradient vector flow and region merging

This paper presents a region merging-based automatic tongue segmentation method. First, gradient vector flow is modified as a scalar diffusion equation to diffuse the tongue image while preserving the edge structures of tongue body. Then the diffused tongue image is segmented into many small regions by using the watershed algorithm. Third, the maximal similarity-based region merging is used to extract the tongue body area under the control of tongue marker. Finally, the snake algorithm is used to refine the region merging result by setting the extracted tongue contour as the initial curve. The proposed method is qualitatively tested on 200 images by traditional Chinese medicine practitioners and quantitatively tested on 50 tongue images using the receiver operating characteristic analysis. Compared with the previous active contour model-based bi-elliptical deformable contour algorithm, the proposed method greatly enhances the segmentation performance, and it could reliably extract the tongue body from different types of tongue images.     

基于分水岭变换的多尺度遥感图像分割算法

分水岭变换是一种适用于图像分割的强有力的形态工具,能够自动生成一系列封闭分割区域。分水岭变换的不足之处在于它的过分割结果。为了克服分水岭变换固有的过度分割现象,利用非线性滤波和改进的快速区域合并算法优化分水岭变换得出的初始分割结果,并针对高分辨遥感图像所体现出来的地物的多种信息特征,结合多种特征进行了区域合并。实验结果与MeanShift算法得到的结果进行了比较,证明该算法不仅能充分利用高分辨率遥感图像中地物的信息特征获得良好的分割效果,而且大大减少了计算时间。     

基于信息瓶颈法的图像分离-合并分割算法

在图像处理中,分割算法是其主要研究焦点之一。针对该问题,提出基于信息瓶颈法的新图像分离 合并分割算法。该方法的目的是抽取与输入相关的一个变量的紧密表征,并使得在考虑与输出相关的另一个变量互信息的损失最小。首先,基于一系列图像区域和强度直方图集合之间定义信息渠道,在此渠道中,以互信息的最大化来优化图像分割法;然后,通过最小化互信息损失,完成在上一阶段中获得的多区域合并过程。在二维图像上做的实验表明所提出算法的性能。     

一种基于数据融合的医学图像分割方法

针对一类纹理特征明显的医学图像,提出了一种融合纹理信息和灰度信息的图像分割方法,设计了基于金字塔结构的区域增长分割算法,该方法在区域内部结合使用纹理信息和灰度信息,在区域边缘部分则充分利用灰度信息,计算结果表明,该方法对某一类医学图像能够获得较好的分割效果。     

基于数学形态学和区域合并的医学CT图像分割*

针对传统分水岭算法分割腹部CT图像存在的过分割情况,提出了一种基于形态学优化和区域合并的分水岭分割算法。该方法先利用多尺度数学形态学方法检测出梯度图像,并用形态学重构去除细密纹理和噪声引起的局部极值,然后进行分水岭变换。为了产生有意义的分割,采用简单的区域灰度均值对变换后的图像进行有效的合并。实验结果表明,该方法能有效解决分水岭算法的过分割问题,得到较好的分割效果。     

Perceptual hashing for SAR image segmentation

Image segmentation is an important step in the implementation of the interpretation of synthetic aperture radar (SAR) image due to speckle. This article proposes a SAR image segmentation method based on perceptual hashing. The new algorithm is divided into two phases. The first phase is to obtain initial regions with multi-thresholding based on histogram after reducing the speckle noise. The initial regions are used as input data. And the next phase is to merge regions according to the similarity between regions. In this phase, to segment SAR image effectively, the proposed hashing algorithm is used to obtain hash value and similarity between regions, which preserve the texture features of SAR images. In addition, we can obtain a smooth segmentation result by reducing the redundant information with principal component analysis. Furthermore, morphological methods are used to eliminate the uneven background in the segmentation results. These improvements make our algorithm more effective to segment the images with high speed. The experimental results of four real and one synthetic SAR images verify the efficiency of our algorithm.