基于区域离散曲率的三维网格分水岭分割

针对离散曲率估计对噪声敏感且特征值计算量大的特点提出了基于区域离散曲率的三维网格分水岭分割算法。寻找三维模型显著特征点;对三维模型进行预分割,确定分割带;在分割带区域上计算离散曲度极值点,利用测地距离和曲度极值点对三维模型进行分水岭分割。算法在分割前无需进行网格去噪,实验结果证明,对主体分支明显的模型具有较高的分割边缘准确度和较快的分割速度。     

基于Laplace谱嵌入和Mean Shift的 三角网格一致性分割

针对现有网格分割算法对模型姿态及噪声敏感的不足,提出一种基于Laplace谱嵌入和Mean Shift聚类的网格一致性分割算法。采用Laplace-Beltrami算子,将3维空域中的网格模型转化成高维Laplace谱域中的标准型,降低了姿态变化和噪声对分割算法的影响,并增强了网格的结构可分性;在高维谱域中,采用非参数核聚类MeanShift算法,获取模型有视觉意义的语义区域。实验结果表明:该算法可以快速有效地实现具有分支结构三角网格模型的有意义分割且对模型姿态和噪声具有较好的鲁棒性。     

融合均值漂移和加权谱聚类的彩色图像分割

为了提高彩色图像分割的效率,提出了一种彩色图像分割新方法。该方法首先利用均值漂移算法滤除噪声干扰并对图像进行初始分割,初始分割后的图像由一些互不相交的区域组成;然后将这些区域视为图的节点建立区域连接图;最后采用融入了区域之间面积差异信息与空间距离信息的加权谱聚类算法进行聚类,获得最终分割结果。实验结果表明,该方法可较好地对彩色图像进行分割,并能有效地降低噪声的影响,保持图像边缘,且运算速度较快。     

CAD mesh model simplification with assembly features preservation

This paper aims to investigate a CAD mesh model simplification method with assembly features preservation, in order to satisfy the requirement of assembly field for the information of 3D model. The proposed method simplifies a CAD mesh model as follows. Firstly, the notion of ＂conjugation＂ is incorporated into the definition of assembly features, with the purpose of benefitting the downstream applications such as assembly features recognition and preservation. Subsequently, the attributed adjacency graphs （AAGs） of the region- level-represented parts are established. The assembly features are automatically recognized by searching for conjugated subgraphs of every two AAGs based on subgraph isomorphism algorithm. In order to improve the efficiency of assembly features recognition, the characteristics of conjugated subgraphs are adopted to initialize the mapping matrix, and the ＂verifying while matching strategy＂ is used to verify the validity of every two newly founded vertices which are correspondingly matched. Then, simplified CAD mesh model with assembly features preserved is constructed after suppressing the common form features. The method is applied successfully to simplify the CAD mesh model with assembly features well preserved. Moreover, the tradeoff between the cost of time for conjugated subgraphs matching and the complexity of the to-be-matched parts is proven to be almost linear.     

勾画式交互的网格模型分割

提出一种基于区域增长的交互三维网格模型分割方法。在区域增长的基础上,首先由用户利用基于勾画的交互方式选定部分顶点作为目标和背景,其余顶点作为未知区域,利用区域增长的方法自动生成目标的边界,从而完成模型的分割。此方法中边界顶点分割结果的好坏直接影响到了最终的分割结果,因此,在利用区域增长方法形成边界时,将既与目标相邻又与背景相邻的顶点标记为特殊点,在其余未知部分分割完成之后,重新对特殊点进行一次区域增长算法。此时由于大部分顶点的状态已经确定,获得的边界将更为准确。实验表明分割结果有了很大程度的改进。     

基于图划分的图像直方图聚类分割

传统的基于图论的图像分割方法都是直接对图像灰度数据进行聚类分割,算法计算量较大。提出一种新的基于图论的直方图聚类分割算法,算法对图像直方图数据进行聚类,并由此得到分割阈值。由于输入值为直方图数据而不是图像灰度,数据量最大为 256而与像素数无关。实验研究表明,本方法在分割质量基本不变的情况下使得计算量大为减少。     

一种基于特征线的曲面网格分割方法

各类网格分割法将曲面网格进行分割后,各子网格区域之间的交界线便可以作为曲面网格的封闭特征线。相反,如果根据网格模型的几何、拓扑特征,确定了网格模型的封闭特征线后,网格曲面便被这些特征线分割开来。为此,从曲面网格封闭特征线的角度出发,提出一种基于特征线的曲面网格分割方法。实验验证了该方法的可行性和有效性。     

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。     

基于空间模式聚类的彩色图像分割

采用了一种基于空间模式聚类的方法,它将图像中的每个像素看成是一个模式,每个模式既体现了所代表像素的空间信息,又包括了像素的颜色信息。这样,对像素的聚类,转变成为对模式的聚类,聚类过程考虑了彩色图像空间中的三个颜色分量。经过实验,此方法能够比较好的对一些彩色图像进行聚类图像分割。     

改进的基于空间模式聚类的图像分割*

为了提高算法在含有一定噪声的图片中的分割功能,将一种隶属度函数计算方法加入到一种空间模式聚类算法当中,使原本已经充分挖掘图像空间信息的聚类算法,在含有一定噪声的图片上得到较好的分割效果。实验证明,修改后的算法提高了抗噪性能。     

Multiscale stochastic hierarchical image segmentation by spectral clustering

This paper proposes a sampling based hierarchical approach for solving the computational demands of the spectral clustering methods when applied to the problem of image segmentation. The authors first define the distance between a pixel and a cluster, and then derive a new theorem to estimate the number of samples needed for clustering. Finally, by introducing a scale parameter into the simi- larity function, a novel spectral clustering based image segmentation method has been developed. An important characteristic of the approach is that in the course of image segmentation one needs not only to tune the scale parameter to merge the small size clusters or split the large size clusters but also take samples from the data set at the different scales. The multiscale and stochastic nature makes it feasible to apply the method to very large grouping problem. In addition, it also makes the segmentation compute in time that is linear in the size of the image. The experimental results on various synthetic and real world images show the effective- ness of the approach.     

Interactive image segmentation by maximal similarity based region merging

Efficient and effective image segmentation is an important task in computer vision and object recognition. Since fully automatic image segmentation is usually very hard for natural images, interactive schemes with a few simple user inputs are good solutions. This paper presents a new region merging based interactive image segmentation method. The users only need to roughly indicate the location and region of the object and background by using strokes, which are called markers. A novel maximal-similarity based region merging mechanism is proposed to guide the merging process with the help of markers. A region R is merged with its adjacent region Q if Q has the highest similarity with Q among all Q's adjacent regions. The proposed method automatically merges the regions that are initially segmented by mean shift segmentation, and then effectively extracts the object contour by labeling all the non-marker regions as either background or object. The region merging process is adaptive to the image content and it does not need to set the similarity threshold in advance. Extensive experiments are performed and the results show that the proposed scheme can reliably extract the object contour from the complex background.     

一种精度可控的CAD网格模型及轻量化算法的研究

针对现有三维CAD系统中,CAD网格模型显示精度固定,难以动态调整以及细分后网格数量过多等瓶颈问题,在原始模型基础上,结合CAD模型中边、面等几何与拓扑信息,提出了一种精度可控的三维CAD网格模型.根据该模型的结构特点,采用曲面内部插值与边界曲线插值相结合的轻量化分类细分算法来实现模型精度调整,并有效控制细分后的网格数量,可满足三维CAD系统对模型精度可控、减少网格数量的需要.     

Adaptive spatial information-theoretic clustering for image segmentation

The incorporation of spatial context into clustering algorithms for image segmentation has recently received a significant amount of attention. Many modified clustering algorithms have been proposed and proven to be effective for image segmentation. In this paper, we propose a different framework for incorporating spatial information with the aim of achieving robust and accurate segmentation in case of mixed noise without using experimentally set parameters based on the original robust information clustering (RIC) algorithm, called adaptive spatial information-theoretic clustering (ASIC) algorithm. The proposed objective function has a new dissimilarity measure, and the weighting factor for neighborhood effect is fully adaptive to the image content. It enhances the smoothness towards piecewise-homogeneous segmentation and reduces the edge blurring effect. Furthermore, a unique characteristic of the new information segmentation algorithm is that it has the capabilities to eliminate outliers at different stages of the ASIC algorithm. These result in improved segmentation result by identifying and relabeling the outliers in a relatively stronger noisy environment. Comprehensive experiments and a new information-theoretic proof are carried out to illustrate that our new algorithm can consistently improve the segmentation result while effectively handles the edge blurring effect. The experimental results with both synthetic and real images demonstrate that the proposed method is effective and robust to mixed noise and the algorithm outperforms other popular spatial clustering variants.     

基于分裂式K均值聚类的图像分割方法

模糊C均值聚类(FCM)算法是一种有效的无监督图像分割方法,适用于任意分类数,不需要预知图像特征,但其聚类效果直接受待分类样本噪声和分类初始条件的影响。因此,提出了一种适用于彩色图像分割的分裂式K均值聚类(FKM)算法,该算法首先使用中值滤波对分类样本去噪,然后使用一种分裂聚类法对图像样本进行预分类,得到一组样本集初始划分,最后以这组划分为起点,使用基于概率距离的K均值聚类对图像分割进行迭代优化。实验结果表明,该算法可以避免FCM的误分类,诸如陷于中心死区、中心重叠和局部极小值,而且提高了分割速度。     

自适应尺度的局部强度聚类图像分割模型

针对传统活动轮廓模型无法精确分割强度不均匀图像，并且对尺度参数比较敏感的问题，提出了一种基于区域信息的自适应尺度的活动轮廓模型。根据图像的局部熵构建自适应尺度算子，利用图像的局部强度聚类性质构建能量函数。使用一组平滑基函数的线性组合来表示偏移场，这样可以增加模型的稳定性。通过最小化该能量，所提模型能够同时分割图像和估计偏移场，并且估计的偏移场可以用于强度不均匀校正。实验结果表明，与其它4种模型相比，该模型拥有更高的分割精确度，且分割结果对水平集函数的初始化和噪声具有鲁棒性。     

基于商空间粒度理论的三维网格分割方法

通过研究已有的网格分割和模型简化方法 ,分析三维模型的网格分割中的商空间粒度思想 ,并将商空间粒度计算引入到网格分割中 ,对网格分割过程进行描述 ,提出了基于粒度分层合成技术的网格分割方法。该算法通过分别提取模型中各三角形网格区域的几何特征构成不同的粒度区域 ,再根据粒度合成理论。将这些所形成的粒度组织起来 ,从而实现对三维网格的最终分割 ,为三角网格模型的简化提供了快速有效的方法。实验表明了该算法对于网格分割的有效性和正确性。     

融合聚类和分级区域合并的彩色图像分割方法

提出了一种融合聚类的分级区域合并彩色图像分割方法。为平滑图像且保持良好边缘,首先用均值偏移算法进行滤波,在此基础上运用改进的k均值聚类方法在颜色空间对图像进行聚类,形成图像的初始分割区域。融合颜色、空间和邻域信息度量区域的距离,对初始分割区域进行分级合并,直至满足停止区域合并的准则。利用形态学腐蚀与膨胀算法对区域边缘进行平滑。仿真结果表明,算法的分割结果符合人类主观视觉感知,具有良好的一致性。