Interpolation of 3-D binary images based on morphological skeletonization

In this paper, the morphological skeleton interpolation (MSI) algorithm is presented. It is an efficient, shape-based interpolation method used for interpolating slices in a three-dimensional (3-D) binary object. It is based on morphological skeletonization, which is used for two-dimensional (2-D) slice representation. The proposed morphological skeleton matching process provides translation, rotation, and scaling information at the same time. The interpolated slices preserve the shape of the original object slices, when the slices have similar shapes. It can also modify the shape of an object when the successive slices do not have similar shapes. Applications on artificial and real data are also presented.     

Automated optic disk boundary detection by modified active contour model

This paper presents a novel deformable-model-based algorithm for fully automated detection of optic disk boundary in fundus images. The proposed method improves and extends the original snake (deforming-only technique) in two aspects: clustering and smoothing update. The contour points are first self-separated into edge-point group or uncertain-point group by clustering after each deformation, and these contour points are then updated by different criteria based on different groups. The updating process combines both the local and global information of the contour to achieve the balance of contour stability and accuracy. The modifications make the proposed algorithm more accurate and robust to blood vessel occlusions, noises, ill-defined edges and fuzzy contour shapes. The comparative results show that the proposed method can estimate the disk boundaries of 100 test images closer to the groundtruth, as measured by mean distance to closest point (MDCP) <3 pixels, with the better success rate when compared to those obtained by gradient vector flow snake (GVF-snake) and modified active shape models (ASM).     

Voxel-based shape decomposition for feature-preserving 3D thumbnail creation

In this work, we study the problem of voxel-based shape decomposition and simplification to create simplified 3D models, called 3D thumbnails, which preserve the salient features of their original models to facilitate users’ interactive browsing. The proposed method decomposes a 3D model into multiple parts and simplifies each decomposed part individually. In this process, a 3D model is first converted into a voxel-based shape representation and a rough skeleton is extracted using the thinning operation. Then, a skeleton refinement algorithm is proposed to fine-tune the thinned skeleton and decompose the skeleton into multiple groups. The remaining processing steps include: (1) taking body measurements of each part with PCA transformation and (2) creating the 3D thumbnail by primitive approximation. It is shown by experimental results that the proposed voxel-based scheme outperforms the mesh-based scheme in the sense that the resultant 3D thumbnail can preserve more features when it is greatly simplified.     

长距离双M-Z干涉型振动传感器实时定位算法研究

提出了一种基于离散小波预处理的长距离双Mach-Zehnder(M-Z)干涉型振动传感器实时定位算法。在进行互相关时延估计前,首先利用离散小波分解寻找振动起始点,并提取出有效数据域,在显著提高运算速度的同时,有效降低了各类相干噪声和干扰引入的定位误差。利用总长30.498km的普通室外光缆进行了现场实验,结果表明,算法在2MHz采样率情况下达到了50m的定位精度,定位时间不超过1s。     

A polygonal approximation of shape boundaries of marine plankton based-on genetic algorithms

Polygonal approximation of a shape boundary can provide a minimalistic representation of the shape. It can also accelerate the processing speed of feature extraction. Our interest is in applying such a method to approximate the boundaries of plankton shapes. A polygonal approximation method based on genetic algorithms has been designed to compactly describe the plankton shapes by polygons. Firstly, two artificial digital curves are used to test the performance of our algorithm. Results are compared with other existing algorithms which show that our algorithm has efficient performance for solving the problem of the polygonal approximation. Secondly, the proposed method is applied to a selection of plankton images under three different approximation levels to a polygonal fit and then five evaluation criteria are applied to determine which approximation level of a particular image is most suitable for describing the shape. The stability and robustness of three approximation levels are also tested.     

A novel approach to microcalcification detection using fuzzy logictechnique

Breast cancer continues to be a significant public health problem in the United States. Approximately, 182,000 new cases of breast cancer are diagnosed and 46,000 women die of breast cancer each year. Even more disturbing is the fact that one out of eight women in the United States will develop breast cancer at some point during her lifetime. Since the cause of breast cancer remains unknown, primary prevention becomes impossible. Computer-aided mammography is an important and challenging task in automated diagnosis. It has great potential over traditional interpretation of film-screen mammography in terms of efficiency and accuracy. Microcalcifications are the earliest sign of breast carcinomas and their detection is one of the key issues for breast cancer control. In this study, a novel approach to microcalcification detection based on fuzzy logic technique is presented. Microcalcifications are first enhanced based on their brightness and nonuniformity. Then, the irrelevant breast structures are excluded by a curve detector. Finally, microcalcifications are located using an iterative threshold selection method. The shapes of microcalcifications are reconstructed and the isolated pixels are removed by employing the mathematical morphology technique. The essential idea of the proposed approach is to apply a fuzzified image of a mammogram to locate the suspicious regions and to interact the fuzzified image with the original image to preserve fidelity. The major advantage of the proposed method is its ability to detect microcalcifications even in very dense breast mammograms. A series of clinical mammograms are employed to test the proposed algorithm and the performance is evaluated by the free-response receiver operating characteristic curve. The experiments aptly show that the microcalcifications can be accurately detected even in very dense mammograms using the proposed approach     

Patchwise scaling method for content-aware image resizing

Image resizing becomes more and more important in content-aware image displaying. This paper proposes a patchwise scaling method to resize an image to emphasize the important areas and preserve the globally visual effect (smoothness, coherence and integrity). This method for resizing image is based on optimizing the image distance presented in this paper. The image distance is defined based on so-called local bidirectional similarity measurement and smoothness measurement to quantify the quality of resizing outputs. The original image is divided into small important patches and unimportant patches based on an important map. The important map is generated automatically using a novel combination of image edge and saliency measurement. A scaling factor is computed for each small patch. The resized image is produced by iteratively optimizing, which is based on our image distance, the scaling factor for each small patch. Experiments of different type images demonstrate that our method can be effectively used in image processing applications to locally shrink and enlarge important areas while preserving image quality.     

一种基于轮廓线的形状描述与匹配方法

该文提出了一种新的基于目标轮廓线的形状描述与匹配方法。该方法将两个待匹配的目标的轮廓线等弧长地分割成相同数目的弧段,用直线段连接相邻的割点构成对轮廓线的多边形近似,用多边形的顶点到形状的几何中心的距离和其两个后继顶点的距离构成的3个距离串来描述形状,形状之间的相似度用它们的3个距离串的欧氏距离和来计算。这种描述方法满足惟一性、紧致性和不变性,而且计算简单,对形状的全局特征和局部特征都能很好地描述。实验结果表明,将该方法用于形状匹配具有较高的精度和可靠性。     

Iterative Subspace Analysis Based on Feature Line Distance

Nearest feature line-based subspace analysis is first proposed in this paper. Compared with conventional methods, the newly proposed one brings better generalization performance and incremental analysis. The projection point and feature line distance are expressed as a function of a subspace, which is obtained by minimizing the mean square feature line distance. Moreover, by adopting stochastic approximation rule to minimize the objective function in a gradient manner, the new method can be performed in an incremental mode, which makes it working well upon future data. Experimental results on the FERET face database and the UCI satellite image database demonstrate the effectiveness.      

基于扩展LLE方法的非线性系统故障诊断研究

针对非线性系统故障诊断难以解决的问题,提出了一种基于扩展局部线性嵌入映射(Locally Linear Embedding,LLE)的故障诊断方法.通过引入切空间距离代替欧氏距离,可以更加科学的满足算法近邻点局部线性的要求,从而可以更好的保留原始数据的局部流形特征.另外,将故障状态与高维空间分布结合起来,通过确定数据点在空间超球内的分布完成故障的检测,在这个过程中将超球的确定与LLE算法中基于核函数的样本外数据扩展相结合,减少了计算量,提高了算法的实时性,从而为复杂非线性系统的故障诊断提供了一种新的有效的方法.     

基于平稳多小波变换的红外图像噪声抑制方法

提出了一种平稳多小波变换方法,该方法结合多小波和平稳小波变换在信号去噪方面的优点,给出了二维图像平稳多小波变换的mallat分解重构算法,并对红外图像的平稳多小波变换系数进行阚值处理实现图像去噪,仿真结果表明,相对于平稳标量小波变换和多小波的噪声抑制方法,此方法对噪声有更好的抑制作用,并尽可能多的保持目标的特征和细节．     

基于同底三角形面积描述的形状检索

为了在形状匹配的过程中提高形状特征对边界噪声和图像变形的鲁棒性,同时兼顾形状匹配算法的检索精度和运算效率,提出一种基于同底三角形面积的形状匹配方法.该方法首先计算每个轮廓采样点的同底三角形面积描述子,并对该描述子进行局部平滑,使其更加鲁棒.然后采用加权L1度量方法计算两个形状所有轮廓点的同底三角形描述子之间的距离,获得匹配代价矩阵.最后利用动态规划算法计算匹配代价矩阵的相似度,获得形状距离,实现形状匹配.通过在MPEG-7、Kimia以及铰接形状数据库上测试分析表明,该方法对变形目标具有良好的鲁棒性,且提高了运算效率和检索精度.     

Reduced Order Modeling for RLC Interconnect Tree Using Hurwitz Polynomial

We investigate on-chip RLC interconnect reduced order modeling problem. A provably realizable and stable model order reduction approach is proposed. To guarantee stability of reduced order circuits, we first employ a realizable reduction for load approximation to preserve the first three driving-point admittance coefficients. Then, we use Hurwitz polynomials to approximate the denominators of original rational transfer functions. We prove that stability can be guaranteed during a hierarchical analysis while circuit response moments can still be matched implicitly. We also give some experimental results to show the accuracy and efficiency of the proposed approach.     

Second-Order Approximation for DOA Estimation of Near-Field Sources

The direction-of-arrival estimation of near-field sources can be formulated as a multidimensional nonlinear optimization problem, where a performance index is minimized with respect to azimuth, range, and source power. For the single source case, under the assumption that the range is relatively larger than the interelement distance, we use the second-order approximation to derive a simpler performance index parameterized by azimuth only. The minimization of the new index is easier than that of the original one parameterized by azimuth, range, and source power. Moreover, the proposed method considers the degradation of signal powers, giving more accurate estimation results. Also for the multiple source case, an efficient computation method is developed by using the second-order approximation.     

Automated 3-D PDM construction from segmented images using deformable models

In recent years, several methods have been proposed for constructing statistical shape models to aid image analysis tasks by providing a priori knowledge. Examples include principal component analysis of manually or semiautomatically placed corresponding landmarks on the learning shapes [point distribution models (PDMs)], which is time consuming and subjective. However, automatically establishing surface correspondences continues to be a difficult problem. This paper presents a novel method for the automated construction of three-dimensional PDM from segmented images. Corresponding surface landmarks are established by adapting a triangulated learning shape to segmented volumetric images of the remaining shapes. The adaptation is based on a novel deformable model technique. We illustrate our approach using computed tomography data of the vertebra and the femur. We demonstrate that our method accurately represents and predicts shapes.     

Equivalent-circuit interconnect modeling based on the fifth-order differential quadrature methods

This paper introduces an efficient and passive discrete modeling technique for estimating signal propagation delays through on-chip long interconnects that are represented as distributed RLC transmission lines. The proposed delay model is based on a less frequently used numerical approximation technique, called the differential quadrature method (DQM). The DQM can compute the partial derivative of a function at any arbitrary point located within a prespecified closed domain of the function by quickly estimating the weighted linear sum of values of the function at a relatively small set of well-chosen grid points within the domain. By using the fifth-order DQM, a new approximation framework is constructed in this paper for discretizing the distributed RLC interconnect and thereafter modeling its delay. Due to high efficiency of DQM approximation, the proposed framework requires only few grid points to achieve good accuracy. The presented equivalent-circuit model appears like the ones derived by the finite difference (FD) method. However, it has higher accuracy and less internal nodes than generated by the FD-based modeling. The fifth-order DQM modeling technique is shown to preserve passivity. It has linear forms that are compatible with the passive order-reduction algorithm for linear network. Numerical experiments show that the proposed modeling approach leads to high accuracy as well as high efficiency.     

Polynomial-Approximation-Based Control for Nonlinear Systems

This paper is concerned with the stabilization problem for nonlinear systems. A new polynomial-approximation-based approach for modeling nonlinear systems is first proposed. The nonlinearity is approximated by polynomials, and the approximation errors are treated as modeling uncertainties. The original nonlinear systems are converted into polynomial systems with modeling uncertainties. In order to highlight the approximation accuracy, the piecewise polynomial approximation functions are utilized. A novel polynomial state-feedback controller is designed to solve the stabilization problem. Furthermore, switched polynomial state-feedback controllers are designed to improve the performance. The stabilization conditions are presented in terms of sum of squares, which can be numerically solved via SOSTOOLS. Finally, simulation examples are provided to demonstrate the feasibility of the proposed method and show its advantage over the polynomial-fuzzy-model-based approach.     

A modified finite-element method for dielectric waveguides using anasymptotically correct approximation on infinite elements

A modified finite-element method for the propagation analysis of such dielectric waveguides as optical fibers and integrated optical waveguides is presented. Possible applications include nondissipative structures of arbitrary anisotropic media with, in some cases, inhomogeneous exterior regions. The method is based on the full vectorial finite-element formulation, which is known to be without spurious solutions. With this formulation all appropriate boundary and interelement conditions on both tangential and normal components are a priori satisfied. For the unbounded, exterior region a novel type of asymptotically correct approximation on infinite elements is proposed that simultaneously, for each mode and frequency, locally adapts the rate of radial decay to the transversal wavenumbers. The linearity of the original finite-element method has been retained by using β/k₀ as a parameter, which results in a sparse generalized eigenvalue problem. Numerical examples including both optical fibers and integrated optical waveguides, isotropic as well as anisotropic, have been analyzed to confirm the validity of the method. The observed correspondence with analytical solutions has been excellent. For some examples a special near-field wavenumber has been added to preserve a high accuracy close to cutoff     

基于小波变换多尺度积的图像融合算法

图像融合是图像处理中的关键技术之一。它在军事和民用图像处理领域获得了广泛的应用。提出了一种新的基于小波变换多尺度积的图像融合算法,小波变换多尺度积具有放大信号边缘特征和降低信号噪声的特点,有利于在融合图像中保持图像的细节特征。利用统计分析的评判准则,如熵、标准偏差评价图像的融合效果。实验结果表明该方法提高了图像的熵和标准偏差。在保留原图像信息的情况下增强了融合图像的细节信息。     

基于改进LSDA的人体行为识别算法

提出了一种基于改进的局部敏感判别分析(LSDA)的人体行为识别算法.首先,利用样本间的距离信息重置类内最近邻点集和类间最近邻点集的权值矩阵,保证在最大化目标函数时尽可能保持局部线性重构特性,解决小样本难题;然后,通过Gran-Schmidt正交化方法将特征矩阵进行正交化,可有效地保留原始空间中的高维空间,解决图像特征重建难题;最后在Weizmann行为数据库进行实验,平均正确识别率达到98.21％.试验结果表明,该算法具有良好的分类性能,在不同训练样本个数下,该算法均能获得较高的识别率.