一种在铸件缺陷识别中的区域分形分割方法

在利用X射线对铸造缺陷识别的过程中,由于X光图像中铸件形成的背景复杂,传统的区域分割技术难以分割出缺陷集中的兴趣区域。利用缺陷边缘的分形特性从铸件边缘线图中确定缺陷的边缘线,以聚类方法将缺陷边缘线聚集的区域,即缺陷集中的兴趣区域分割出来。针对汽车铝合金轮毂铸件X光图像,用铸造缺陷区域的分割实验结果证明了该方法的有效性。     

基于圆形约束C-V水平集的肺部CT图像病灶分割

针对肺部CT图像中圆形病灶区域的分割问题,对Chan-Vese水平集图像分割方法进行了分析和改进,提出了基于圆形约束的C-V水平集模型,进而提出了基于圆形约束水平集的肺部图像病灶分割算法,解决了图像中大小不同的多圆检测问题。对合成图像和实际临床肺部CT图像进行了分割实验,结果表明,该方法可以较好地分割出图像中的多个圆形区域,算法具有较好的抗噪性,实现速度较快,有利于实现肺部CT图像肺结节自动检测。     

基于空间二进制编码的阈值分割方法研究

在三维检测技术中为了得到良好的编码结果从而获取准确的三维数据,必须以清晰的阈值分割的结构光投影图案为前提.由于物体本身带有色彩和光照不均匀使得利用一般的阈值分割方法无法得到质量较好的二值化条纹图.因此根据空间二进制编码必须规范化的特点,提出了一种利用一系列投影图案而得到的基于单像素的阈值分割方法.采用新方法后,可以有效改善由于物体本身色彩和光照不均匀所引起的阈值分割结果较差的现象,得到黑白条纹清晰的二值化结构光投影图案.方法实现简单,阈值分割仅根据一系列图像中的单个像素点进行,因此不受图像中其他像素的影响,抗干扰能力强,可以很好地应用于基于空间二进制编码的形貌测量中.     

基于半监督聚类的3维肢体分割算法

行为分析已经成为计算机视觉研究领域的热点,行为主体的肢体部件分割是行为分析中很重要的一部分同时也是一个难点问题,为了对3维肢体进行有效分割,提出了一种基于半监督聚类的肢体分割算法。该算法首先利用前一帧姿势估计反馈的时域信息来对3维主体进行初始的肢体部件分割;然后根据人体结构信息进一步确定行为主体上各个点与肢体部件之间的关系来得到半监督聚类的初始值;之后基于各个肢体部件的形状信息进行半监督聚类,迭代求解肢体部件分割的最优解;最后利用分割后的各个肢体部件进行行为主体的姿势参数估计。通过对IXMAS数据库中6种行为下的6个行为主体进行的肢体部件的分割实验结果证明,该新算法具有主体适应性、视角适应性以及行为适应性。     

基于二进小波变换的主动轮廓模型

针对传统的主动轮廓模型对噪声敏感的缺陷,在推广梯度矢量流的基础上,提出了基于二进小波变换的改进外力场的计算方法.通过综合利用图像小波分解的高频信息,迭代计算获得其外力场,使蛇在图像的多尺度空间中搜索目标轮廓.针对蛇模型对初始轮廓的依赖性问题,给出了在一维最大熵阈值分割后的图像上获取初始轮廓点的方法,减少了人工的干预,加快了蛇的收敛速度和效果.实验结果表明该模型能有效地排除噪声的干扰,搜索凹陷轮廓,而且对脆弱轮廓有很好的逼近能力.     

A level set method for oil slick segmentation in SAR images

Despite much effort and significant progress in recent years, image segmentation remains a challenging problem in image processing, especially for the low contrast, noisy synthetic aperture radar (SAR) images. This paper explores the segmentation of oil slicks using a partial differential equation (PDE)‐based level set method, which represents the slick surface as an implicit propagation interface. Starting from an initial estimation with priori information, the level set method creates a set of speed functions to detect the position of the propagation interface. Specifically, the image intensity gradient and the curvature flow are utilized together to determine the speed and direction of the propagation. This allows the front interface to propagate naturally with topological changes, significant protrusions and narrow regions, giving rise to stable and smooth boundaries that discriminate oil slicks from the surrounding water. As the speckles are removed concurrently while the front interface propagates, the pre‐filtering of noise is saved. The proposed method has been illustrated by experiments on oil slick segmentation using the ERS‐2 SAR images. Its advantages over the traditional image segmentation approaches have also been demonstrated.     

TWO-LAYER STABILIZATION OF CONTINUOUS NEURAL NETWORKS WITH FEEDBACKS

Methods of stabilization as applied to Hopfield-type continuous neural networks with a unique equilibrium point are considered. These methods permit the design of stable networks where the elements of the interconnection matrix and nonlinear activation functions of separate neurons vary with time. For stabilization with a variable interconnection matrix it is suggested that a new second layer of neurons be introduced to the initial single-layer network and some additional connections be added between the new and old layers. This approach gives us a system with a unique equilibrium point that is globally asymptotically stable, i.e. the entire space serves as the domain of attraction of this point, and the stability does not depend on the interconnection matrix of the system. In the case of the variable activation functions, some results from a recent investigation of the absolute stability problem for neural networks are presented, along with some recommendations.     

Automatic segmentation of corpus collasum using Gaussian mixture modeling and Fuzzy C means methods

This paper presents a comparative study of the success and performance of the Gaussian mixture modeling and Fuzzy C means methods to determine the volume and cross-sectionals areas of the corpus callosum (CC) using simulated and real MR brain images. The Gaussian mixture model (GMM) utilizes weighted sum of Gaussian distributions by applying statistical decision procedures to define image classes. In the Fuzzy C means (FCM), the image classes are represented by certain membership function according to fuzziness information expressing the distance from the cluster centers. In this study, automatic segmentation for midsagittal section of the CC was achieved from simulated and real brain images. The volume of CC was obtained using sagittal sections areas. To compare the success of the methods, segmentation accuracy, Jaccard similarity and time consuming for segmentation were calculated. The results show that the GMM method resulted by a small margin in more accurate segmentation (midsagittal section segmentation accuracy 98.3% and 97.01% for GMM and FCM); however the FCM method resulted in faster segmentation than GMM. With this study, an accurate and automatic segmentation system that allows opportunity for quantitative comparison to doctors in the planning of treatment and the diagnosis of diseases affecting the size of the CC was developed. This study can be adapted to perform segmentation on other regions of the brain, thus, it can be operated as practical use in the clinic.     

Motion-based segmentation of objects using overlapping temporal windows

Motion segmentation refers to the problem of separating the objects in a video sequence according to their motion. It is a fundamental problem of computer vision, since various systems focusing on the analysis of dynamic scenes include motion segmentation algorithms. In this paper we present a novel approach, where a video shot is temporally divided in successive and overlapping windows and motion segmentation is performed on each window respectively. This attribute renders the algorithm suitable even for long video sequences. In the last stage of the algorithm the segmentation results for every window are aggregated into a final segmentation. The presented algorithm can handle effectively asynchronous trajectories on each window even when they have no temporal intersection. The evaluation of the proposed algorithm on the Berkeley motion segmentation benchmark demonstrates its scalability and accuracy compared to the state of the art.