A variational method for contour tracking via covariance matching

This paper presents a novel formulation for contour tracking.We model the second-order statistics of image regions and perform covariance matching under the variational level set framework.Specifically,covariance matrix is adopted as a visual object representation for partial differential equation(PDE) based contour tracking.Log-Euclidean calculus is used as a covariance distance metric instead of Euclidean distance which is unsuitable for measuring the similarities between covariance matrices,because the matrices typically lie on a non-Euclidean manifold.A novel image energy functional is formulated by minimizing the distance metric between the candidate object region and a given template,and maximizing the one between the background region and the template.The corresponding gradient flow is then derived according to a variational approach,enabling partial differential equations(PDEs) based contour tracking.Experiments on several challenging sequences prove the validity of the proposed method.     

A faster divide-and-conquer algorithm for constructing delaunay triangulations

An easily implemented modification to the divide-and-conquer algorithm for computing the Delaunay triangulation ofn sites in the plane is presented. The change reduces its (n logn) expected running time toO(n log logn) for a large class of distributions that includes the uniform distribution in the unit square. Experimental evidence presented demonstrates that the modified algorithm performs very well forn2¹⁶, the range of the experiments. It is conjectured that the average number of edges it creates—a good measure of its efficiency—is no more than twice optimal forn less than seven trillion. The improvement is shown to extend to the computation of the Delaunay triangulation in theL _p metric for 1<p.This research was supported by National Science Foundation Grants DCR-8352081 and DCR-8416190.     

Fuzzy度量空间上映射的又一个不动点定理

继文献〔1〕之后证明了Fuzzy度量空间上映射的又一个不动点定理，使文献〔2〕的§1（3，4）的定理成为本文的特例。     

Multimodel analysis and controller design for nonlinear processes

Multimodel analysis and controller design for nonlinear processes via gap metric is discussed. It is shown that the loop-shaping H_∞ approach can integrate the procedure of selecting operating points and the local controller design. The local controllers can guarantee not only stability but also performance specified by the pre- and/or post-compensators. Thus, at each operating points, local controllers can have similar performance, and the global performance of the system can be predicted.     

Deformation similarity measurement in quasi-conformal shape space

This paper presents a novel approach based on the shape space concept to classify deformations of 3D models. A new quasi-conformal metric is introduced which measures the curvature changes at each vertex of each pose during the deformation. The shapes with similar deformation patterns follow a similar deformation curve in shape space. Energy functional of the deformation curve is minimized to calculate the geodesic curve connecting two shapes on the shape space manifold. The geodesic distance illustrates the similarity between two shapes, which is used to compute the similarity between the deformations. We applied our method to classify the left ventricle deformations of myopathic and control subjects, and the sensitivity and specificity of our method were 88.8% and 85.7%, which are higher than other methods based on the left ventricle cavity, which shows our method can quantify the similarity and disparity of the left ventricle motion well.     

一种面向对象体系结构的度量模型

针对面向对象体系结构的软件系统缺乏有效度量方法的现状,通过行为、逻辑、构件和部署4个互为补充的模型,对面向对象的软件进行了架构分析,并确定了度量数据的采集集合.基于架构分析获得的原始系统数据,吸收快速原型与白盒、黑盒的基本原理,作者提出了一种适合面向确定对象系统的功能-结构-原型度量模型FCPMM(Function Configuration Prototype Metric Model).结合面向对象的基本准则与基本遗传算法,导出了该模型的循环收敛算法,从而可动态求解软件系统的最优状态.最后利用Matlab仿真实例,从度量参数适应度和最佳个体解码值两个方面验证了该算法的可行性.     

打印机光谱特性化方法研究综述

目的 打印输出技术是目前国内外图像复制领域的主流技术。打印机特征化旨在建立打印机输入控制值与输出色彩信息值之间的对应关系,保证打印输出图像各像素色彩值的准确性。基于可见光谱的打印机特性化过程,以图像光谱信息作为打印机特性化过程中的色彩控制依据,可避免基于色度的打印输出过程所固有的同色异谱问题,实现图像色彩复制领域最高级别的无条件颜色匹配。在广泛文献调研的基础上,本文回顾了打印机光谱特性化方法各部分研究的发展历程,分析介绍了该领域主要研究内容及其研究现状,对打印机光谱特性化方法流程进行了较为全面的综述。方法 以Yule-Nielsen修正的光谱Neugebauer模型（YNSN模型）为典型代表,从打印机墨量限制方法、正向光谱预测模型、反向光谱分色模型以及色彩匹配评价度量4个方面,对目前基于可见光谱的打印机特性化方法研究情况进行了总结与归纳,并结合现有研究存在的问题对未来研究发展趋势进行了展望。结果 综合近几年该领域研究发展情况,认为目前打印机光谱特性化方法研究在墨量限制算法合理性、正反向建模精度等方面已经取得了较为理想的结果。然而,尽管已有20多年的广泛研究,鉴于光谱数据的高维性以及具体应用场景的多样性,导致准确而高效的实现光谱打印机特性化仍然是一个极具难度的问题,特别在建模效率提高、模型适用性增强、模型物理意义提升以及评价度量综合性能优化等方面,目前研究仍存在较大的研究发展空间。结论 打印机光谱特性化是实现图像色彩信息高保真打印复制的必要手段,其今后将继续在图像复制领域起到非常关键作用。然而,由于基于光谱的打印机特性化过程涉及光谱学及色度学理论、半色调呈色理论、最优化计算理论、人眼视觉机理、数字加网技术等多种理论与技术,故其研究具有一定的难度与挑战性。本文对打印机光谱特性化方法研究现状进行了较为深入的介绍与分析,意在使国内同行对该领域有一个较为全面的了解,以期引起更多研究者对该领域的关注。由于篇幅所限,许多问题未及深究。     

A secure routing model based on distance vector routing algorithm

Distance vector routing protocols have been widely adopted as an efcient routing mechanism in current Internet,and many wireless networks.However,as is well-known,the existing distance vector routing protocols are insecure as it lacks of efective authorization mechanisms and routing updates aggregated from other routers.As a result,the network routing-based attacks become a critical issue which could lead to a more deteriorate performance than other general network attacks.To efciently address this issue,this paper,through analyzing the routing model and its security aspect,and presents a novel approach on guaranteeing the routing security.Based on the model,we present the security mechanism including the message exchange and update message security authentication mechanism.The suggested approach shows that the security mechanism can efectively verify the integrity and validate the freshness of routing update messages received from neighbor nodes.In comparison with exiting mechanisms(SDV,S-RIP etc),the proposed model provides enhanced security without introducing significant network overheads and complexity.     

对抗样本三元组约束的度量学习算法

针对已有三元组约束的度量学习算法大多利用先验知识构建约束,一定程度上制约了度量学习算法性能的问题,本文借鉴对抗训练中样本扰动的思想,在原始样本附近学习对抗样本以构造对抗三元组约束,基于对抗三元组和原始三元组约束构建了度量学习模型,提出了对抗样本三元组约束的度量学习算法(metric learning algorithm with adversarial sample triples constraints,ASTCML)。实验结果表明,提出的算法既克服了已有固定约束方法受先验知识影响大的问题,也提高了分类精度,说明区分更加难以区分的三元组约束能够提升算法的性能。     

Compositional metric learning for multi-label classification

Multi-label classification aims to assign a set of proper labels for each instance, where distance metric learning can help improve the generalization ability of instance-based multi-label classification models. Existing multi-label metric learning techniques work by utilizing pairwise constraints to enforce that examples with similar label assignments should have close distance in the embedded feature space. In this paper, a novel distance metric learning approach for multi-label classification is proposed by modeling structural interactions between instance space and label space. On one hand, compositional distance metric is employed which adopts the representation of a weighted sum of rank-1 PSD matrices based on component bases. On the other hand, compositional weights are optimized by exploiting triplet similarity constraints derived from both instance and label spaces. Due to the compositional nature of employed distance metric, the resulting problem admits quadratic programming formulation with linear optimization complexity w.r.t. the number of training examples.We also derive the generalization bound for the proposed approach based on algorithmic robustness analysis of the compositional metric. Extensive experiments on sixteen benchmark data sets clearly validate the usefulness of compositional metric in yielding effective distance metric for multi-label classification.