CAD mesh model segmentation by clustering

CAD mesh models have been widely employed in current CAD/CAM systems, where it is quite useful to recognize the features of the CAD mesh models. The first step of feature recognition is to segment the CAD mesh model into meaningful parts. Although there are lots of mesh segmentation methods in literature, the majority of them are not suitable to CAD mesh models. In this paper, we design a mesh segmentation method based on clustering, dedicated to the CAD mesh model. Specifically, by the agglomerative clustering method, the given CAD mesh model is first clustered into the sparse and dense triangle regions. Furthermore, the sparse triangle region is separated into planar regions, cylindrical regions, and conical regions by the Gauss map of the triangular faces and Hough transformation; the dense triangle region is also segmented by the mean shift operation performed on the mean curvature field defined on the mesh faces. Lots of empirical results demonstrate the effectiveness and efficiency of the CAD mesh segmentation method in this paper.     

A novel 3D mesh compression using mesh segmentation with multiple principal plane analysis

This paper proposes a novel scheme for 3D model compression based on mesh segmentation using multiple principal plane analysis. This algorithm first performs a mesh segmentation scheme, based on fusion of the well-known k-means clustering and the proposed principal plane analysis to separate the input 3D mesh into a set of disjointed polygonal regions. The boundary indexing scheme for the whole object is created by assembling local regions. Finally, the current work proposes a triangle traversal scheme to encode the connectivity and geometry information simultaneously for every patch under the guidance of the boundary indexing scheme. Simulation results demonstrate that the proposed algorithm obtains good performance in terms of compression rate and reconstruction quality.     

曲面的有限元网格顶点法矢算法

分析计算有限元三角形网格顶点法矢的各种算法原理,比较各种算法的结果精度,指出Max方法考虑了三角形网格的形状,且本质上是一种通过对四面体进行外接球面拟合的计算方法,结果精度很高.在此基础上,针对曲面在有限元网格划分后可能同时存在三角形网格和四边形网格,提出适应于单独的三角形网格和四边形网格与两者并存的混合网格的顶点法矢求取算法,计算结果表明了算法的适应性和有效性.     

Automatic plaque segmentation based on hybrid fuzzy clustering and k nearest neighborhood using virtual histology intravascular ultrasound images

Thin cap fibroatheroma (TCFA) or “vulnerable plaque” is responsible for the majority of coronary artery death. Virtual Histology Intravascular Ultrasound (VH-IVUS) image is a clinically available method for visualizing color coded tissue maps. However, this technique has considerable limitations in providing medical relevant information for identifying vulnerable plaque. The aim of this paper is to improve the identification of TCFA in VH-IVUS image. Therefore, this paper proposes a set of algorithms for segmentation, feature extraction, and plaque type classification to accurately identify TCFA. A hybrid model using the FCM and kNN (HFCM-kNN) is proposed to accurately segment the VH-IVUS image. The proposed technique is capable of eliminating outliers and detecting clusters with different densities in VH-IVUS image. The next process is extracting plaque features to provide an accurate definition of the unstable (vulnerable) plaque. To achieve the above contribution, five algorithms are proposed to extract significant features from VH-IVUS images. Machine learning approaches are applied for training 440 in-vivo images obtained from 8 patients. Results proved the dominance of the proposed method for TCFA detection with accuracy rate of 98.02% compared with the 76.5% obtained by the cardiologist decision. Moreover, by validation of VH-IVUS images and their corresponding Optical Coherence Tomography (OCT) images, accuracy of 92.85% is achieved.     

Motif analysis for automatic segmentation of CT surface contours into individual surface features

This paper presents a new layer-based technique for automatic high-level segmentation of 3-D surface contours into individual surface features through motif analysis. The procedure starts from a contour-based surface model representing a composite surface area of an object. For each of the surface contours, a relative turning angle (RTA) map is derived. The RTA map usually contains noise and minor features. Algorithms based on motif analysis are applied for extracting a main profile of the RTA map free from background noise and other minor features. All feature points on the extracted profile are further identified from the extracted main profile through further motif analysis. The original contour is thus partitioned into individual segments with the identified feature points. A collection of consecutive contour segments among different layers form an individual 3-D surface feature of the original composite surface. The developed approach using motif analysis is particularly useful for the identification of smooth joins between individual surface features and for the elimination of superposed noise and unwanted minor features.     

联合手肘法和期望最大化的高斯混合聚类电力系统客户分群算法

为进一步提升电力系统客户的用户体验,针对现有聚类算法寻优能力差、紧凑性不足以及较难求解聚类数目最优值的问题,提出一种联合手肘法与期望最大化（EM）的高斯混合聚类算法,挖掘大量客户数据中的潜在信息。该算法通过EM算法迭代出良好的聚类结果,而针对传统的高斯混合聚类算法需要提前获取用户分群数量的缺点,利用手肘法合理找出客户的分群数量。案例分析表明,所提算法与层次聚类算法和K-Means算法相比,FM、AR指标的增幅均超过10%,紧凑度（CI）和分离度（DS）的降幅分别低于15%和25%,可见性能有较大提升。     

破碎刚体三角网格模型的断裂面分割

针对基于断裂面匹配的破碎刚体复原,提出了一种分割断裂面的方法。首先,根据相邻三角片法矢的夹角,将碎块外表面以棱边为界限分割成多张曲面;然后,根据曲面法矢的扰动大小和扰动图像,经过二次分割,将曲面区分为原始面和断裂面。实验结果表明,所提方法能够正确快速地提取出形状较复杂碎块的断裂面。     

An entropy weighting mixture model for subspace clustering of high-dimensional data

In high-dimensional data, clusters of objects usually exist in subspaces; besides, different clusters probably have different shape volumes. Most existing methods for high-dimensional data clustering, however, only consider the former factor. They ignore the latter factor by assuming the same shape volume value for different clusters. In this paper we propose a new Gaussian mixture model (GMM) type algorithm for discovering clusters with various shape volumes in subspaces. We extend the GMM clustering method to calculate a local weight vector as well as a local variance within each cluster, and use the weight and variance values to capture main properties that discriminate different clusters, including subsets of relevant dimensions and shape volumes. This is achieved by introducing negative entropy of weight vectors, along with adaptively-chosen coefficients, into the objective function of the extended GMM. Experimental results on both synthetic and real datasets show that the proposed algorithm outperforms its competitors, especially when applying to high-dimensional datasets.     

A study on fuzzy clustering for magnetic resonance brain image segmentation using soft computing approaches

This paper presents a novel idea of intracranial segmentation of magnetic resonance (MR) brain image using pixel intensity values by optimum boundary point detection (OBPD) method. The newly proposed (OBPD) method consists of three steps. Firstly, the brain only portion is extracted from the whole MR brain image. The brain only portion mainly contains three regions–gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF). We need two boundary points to divide the brain pixels into three regions on the basis of their intensity. Secondly, the optimum boundary points are obtained using the newly proposed hybrid GA–BFO algorithm to compute final cluster centres of FCM method. For a comparison, other soft computing techniques GA, PSO and BFO are also used. Finally, FCM algorithm is executed only once to obtain the membership matrix. The brain image is then segmented using this final membership matrix. The key to our success is that we have proposed a technique where the final cluster centres for FCM are obtained using OBPD method. In addition, reformulated objective function for optimization is used. Initial values of boundary points are constrained to be in a range determined from the brain dataset. The boundary points violating imposed constraints are repaired. This method is validated by using simulated T1-weighted MR brain images from IBSR database with manual segmentation results. Further, we have used MR brain images from the Brainweb database with additional noise levels to validate the robustness of our proposed method. It is observed that our proposed method significantly improves segmentation results as compared to other methods.     

A fast and automated segmentation method for detection of masses using folded kernel based fuzzy c-means clustering algorithm

Present study proposes a fast, accurate and automated segmentation approach of mammographic images using kernel based fuzzy c-means (FCM) clustering technique. This approach exploits the significant regional features of mammograms which address the properties of different breast densities. The proposed segmentation approach captures those regional features using appropriate kernel and hence apply fuzzy clustering technique for segmenting the masses. This study also introduces kernel based FCM (KFCM) approach in a folded way to process a combination of significant features simultaneously. Suitable choice of kernel size also assists to collect all possible variations of regional features with minimum blocking effect in the output results. Performances of the proposed methodology are analyzed qualitatively and quantitatively in compare to other clustering-based segmentation techniques. Since the proposed approach is able to resolve uncertain and imprecise characteristics of mammograms, it performs superior to other techniques. Convergence time of the proposed method is also assessed and compared with other conventional clustering techniques. Kernel based approach of the proposed segmentation technique reduces the number of data points for clustering and hence convergence speed improves over the conventional algorithms. This study also shows a variation of convergence speed of the proposed segmentation method with different image sizes.     

基于高斯混合模型最大期望聚类的同时定位与地图构建数据关联

数据关联是移动机器人同时定位与地图构建(SLAM)中状态估计的前提和基础,针对当前联合兼容分支定界算法存在计算复杂度高、耗时长的问题,提出了基于高斯混合模型(GMM)最大期望聚类分组的SLAM数据关联算法.首先,为减少同一时刻参与关联的观测值数目,在局部区域内采用GMM最大期望聚类算法对当前时刻的观测值进行分组;其次,在各观测小组中采用联合兼容分支定界算法进行数据关联;最后,综合各观测小组的观测值同局部地图特征得到的关联解,得到最优的关联结果.仿真实验结果表明,基于高斯混合模型最大期望聚类分组的SLAM数据关联算法在保证数据关联准确度的前提下,计算复杂度得到了降低,缩短了运行时间.     

基于IFC标准的BIM自适应分词方法

建筑信息模型(BIM)已经成为建筑行业信息技术应用的有效方案.随着BIM数据不断增长,为了高效使用BIM数据,很多研究将自然语言处理(NLP)引入BIM应用中.在中文环境中,由于缺乏建筑行业的术语特征,导致基础环节的中文分词在建筑领域BIM应用中的适应性较差.通过分析当前流行的BIM数据格式工业基础类(industry...     

Market segmentation and ideal point identification for new product design using fuzzy data compression and fuzzy clustering methods

In product design, various methodologies have been proposed for market segmentation, which group consumers with similar customer requirements into clusters. Central points on market segments are always used as ideal points of customer requirements for product design, which reflects particular competitive strategies to effectively reach all consumers’ interests. However, existing methodologies ignore the fuzziness on consumers’ customer requirements. In this paper, a new methodology is proposed to perform market segmentation based on consumers’ customer requirements, which exist fuzziness. The methodology is an integration of a fuzzy compression technique for multi-dimension reduction and a fuzzy clustering technique. It first compresses the fuzzy data regarding customer requirements from high dimensions into two dimensions. After the fuzzy data is clustered into marketing segments, the centre points of market segments are used as ideal points for new product development. The effectiveness of the proposed methodology in market segmentation and identification of the ideal points for new product design is demonstrated using a case study of new digital camera design.     

Model-based detection,segmentation, and classification for image analysis using on-line shape learning

Detection, segmentation, and classification of specific objects are the key building blocks of a computer vision system for image analysis. This paper presents a unified model-based approach to these three tasks. It is based on using unsupervised learning to find a set of templates specific to the objects being outlined by the user. The templates are formed by averaging the shapes that belong to a particular cluster, and are used to guide a probabilistic search through the space of possible objects. The main difference from previously reported methods is the use of on-line learning, ideal for highly repetitive tasks. This results in faster and more accurate object detection, as system performance improves with continued use. Further, the information gained through clustering and user feedback is used to classify the objects for problems in which shape is relevant to the classification. The effectiveness of the resulting system is demonstrated in two applications: a medical diagnosis task using cytological images, and a vehicle recognition task. Received: 5 November 2000 / Accepted: 29 June 2001 Correspondence to: K.-M. Lee     

Quality based design approach for a single crystal silicon microactuator using DOE technique and response surface model

 This paper discusses the use of DOE technique and response surface model as an efficient quality based design approach to optimize coupled electromechanical behavior of a single crystal silicon micro-actuator for hard disk drives (HDD). A number of experiments for different settings of the microactuator parameters are planned and analyzed. The fitted response surface models are built using the regression technique. Finite element method (FEM), boundary element method (BEM) and optimization techniques are utilized to predict and verify the microactuator performance. Example results show that the proposed approach is effective to guide microactuator design to achieve a robust and reliable design in a most efficient way. Received: 5 July 2001/Accepted: 17 October 2001