基于中心点的指纹细节结构匹配算法

指纹细节匹配算法是自动指纹识别系统(AFIS)中一项关键的任务,目前存在大量的研 究和算法.依据算法是否依赖中心点,指纹细节点匹配算法可以分为两类:基于中心点的匹配算 法和非中心点匹配算法.大多数非中心点匹配算法都非常耗时,因此不适合在线应用.而基于中 心点方法的效率相对较高,但是这类算法极度依赖于中心点的定位精度.在本文中,提出了一种 全新的基于中心点的指纹细节结构匹配算法,该算法综合了基于中心点匹配算法和非中心点匹 配算法的优点,同时又避免了二者的缺点.首先利用中心点检测算法获得中心点的位置,然后在 中心区域定义了一些局部的结构,同时利用这些局部结构寻找指纹细节的对应点,并通过对应点 和中心点的相对关系来确认这些对应细节点.其次利用这些细节对应点匹配全局的细节信息,最 后,利用匹配细节的全局距离和距离方差来判决最终匹配结果.实验结果表明,算法的匹配效果 非常好,同时匹配效率较高,非常适合在线指纹识别系统的应用.     

A multimedia retrieval framework based on semi-supervised ranking and relevance feedback

We present a new framework for multimedia content analysis and retrieval which consists of two independent algorithms. First, we propose a new semi-supervised algorithm called ranking with Local Regression and Global Alignment (LRGA) to learn a robust Laplacian matrix for data ranking. In LRGA, for each data point, a local linear regression model is used to predict the ranking scores of its neighboring points. A unified objective function is then proposed to globally align the local models from all the data points so that an optimal ranking score can be assigned to each data point. Second, we propose a semi-supervised long-term Relevance Feedback (RF) algorithm to refine the multimedia data representation. The proposed long-term RF algorithm utilizes both the multimedia data distribution in multimedia feature space and the history RF information provided by users. A trace ratio optimization problem is then formulated and solved by an efficient algorithm. The algorithms have been applied to several content-based multimedia retrieval applications, including cross-media retrieval, image retrieval, and 3D motion/pose data retrieval. Comprehensive experiments on four data sets have demonstrated its advantages in precision, robustness, scalability, and computational efficiency.     

一种用于分类的改进Boosting算法

提出了一种新的Boosting算法LAdaBoost。LAdaBoost算法利用局部错误率更新样本被选用于训练下一个分类器的概率,当对一个新的样本进行分类时,考虑了该样本与其邻域内的每个训练样本的近似度;另外,提出了有效邻域的概念。根据不同的组合方法,得到了两种LAdaBoost算法,即LAdaBoost-1和LAdaBoost-2。在UCI上部分实验数据集的实验结果表明,LAdaBoost算法比AdaBoost和Bagging算法更有效,且鲁棒性更好。     

Estimating the focus of expansion in a video sequence using the trajectories of interest points

In this paper, we present a new algorithm for the computation of the focus of expansion in a video sequence. Although several algorithms have been proposed in the literature for its computation, almost all of them are based on the optical flow vectors between a pair of consecutive frames, so being very sensitive to noise, optical flow errors and camera vibrations. Our algorithm is based on the computation of the vanishing point of point trajectories, thus integrating information for more than two consecutive frames. It can improve performance in the presence of erroneous correspondences and occlusions in the field of view of the camera.The algorithm has been tested with virtual sequences generated with Blender, as well as some real sequences from both, the public KITTI benchmark, and a number of challenging video sequences also proposed in this paper. For comparison purposes, some algorithms from the literature have also been implemented. The results show that the algorithm has proven to be very robust, outperforming the compared algorithms, specially in outdoor scenes, where the lack of texture can make optical flow algorithms yield inaccurate results. Timing evaluation proves that the proposed algorithm can reach up to 15fps, showing its suitability for real-time applications.     

Improved algorithms for translation of pictures represented by leaf codes

The paper deals with the translation of linear quadtrees, which are pointerless data structures used to represent images. A modification of Gargantini's algorithm is proposed which will make it more efficient and new translation algorithms are proposed based on adjacency algorithms. The algorithms of this paper translate as many leaves as possible without splitting them.     

新的鲁棒模糊C-均值聚类分割算法及其应用

针对Krinidis和公茂果等提出的系列鲁棒模糊局部C-均值聚类算法存在聚类中心迭代公式缺乏严格数学理论基础的不足,于是将其聚类目标函数及其约束条件采用拉格朗日乘子法进行严格数学推导,从而获得最优解逼近的隶属度和聚类中心迭代表达式,并通过多次循环迭代实现图像聚类分割。实验结果表明,本文所建议的鲁棒模糊局部C-均值聚类分割算法是有效的,相比现有鲁棒模糊局部C-均值聚类分割算法更适合复杂遥感等图像的分割需要。     

New Genetic Algorithms Based Approaches to Continuous <Emphasis Type="Italic">p</Emphasis>-Median Problem

We proposed new genetic algorithms (GAs) to address well-known p-median problem in continuous space. Two GA approaches with different replacement procedures are developed to solve this problem. To make the approaches more efficient in finding near-optimal solution two hybrid algorithms are developed combining the new GAs and a traditional local search heuristic. The performance of the newly developed models is compared to that of the traditional alternating location-allocation heuristics by numerical simulation and it is found that the models are effective in finding optimum facility locations.     

基于密度与路径的稳健谱聚类

近年来,谱聚类在分类领域得到了广泛的研究,其中基于路径和基于密度的算法是两个重要的研究方向。虽然这两种算法在一些数据集上能取得较好的分类效果,但不能对一些特殊的数据集进行准确分类。融合了这两种方法的优点,通过多级密度约束来寻找路径,根据得到的路径建立新的相似性矩阵。为了加强对噪声的鲁棒性,根据数据集的局部信息加入鲁棒性系数,提出了基于路径与密度的稳健谱聚类算法。实验结果表明该方法在人工数据集和手写体数据集上能取得较理想的分类结果。     

基于拟Laplacian谱和点对拓扑特征的点模式匹配算法

针对谱匹配方法对噪声和出格点的鲁棒性较差的问题,提出了一种基于拟Laplacian谱和点对拓扑特征的点模式匹配算法。首先,用赋权图的最小生成树构造无符号Laplacian矩阵,通过对矩阵谱分解得到的特征值和特征向量表示点的特征,进而计算点的初始匹配概率;其次,利用点对拓扑特征的相似性测度来定义点对间的局部相容性,然后借助概率松弛的方法更新由拟Laplacian谱得到的匹配概率,得出匹配结果。对比实验结果表明,该方法在处理存在噪声和出格点的点集匹配上具有较高的鲁棒性。     

二维点模式图像的仿射变换配准

提出了一种图像特征点配准算法．该算法为二维点模式图像仿射配准建立了新的求解模型,此模型对参考图像的特征点集和浮动图像的特征点集分别进行Whitening变换,将点集间的一般仿射变换问题转换为刚性变换问题;在对刚性变换求解时,采用平滑性好、局部极值较少的新的目标函数;并引入了形变程度分量,使该算法更能符合实际应用．结合文中提出的新的仿射不变量,目标函数只需在平分法的基础上加入随机因素便能快速求解．实验结果证明,该算法在处理特征点仿射配准问题上具有速度快、精度高的特点．     

Algorithms for the maximum satisfiability problem

Old and new algorithms for the Maximum Satisfiability problem are studied. We first summarize the different heuristics previously proposed, i.e., the approximation algorithms of Johnson and of Lieberherr for the general Maximum Satisfiability problem, and the heuristics of Lieberherr and Specker, Poljak and Turzik for the Maximum 2-Satisfiability problem. We then consider two recent local search algorithmic schemes, the Simulated Annealing method of Kirkpatrick, Gelatt and Vecchi and the Steepest Ascent Mildest Descent method, and adapt them to the Maximum Satisfiability problem. The resulting algorithms, which avoid being blocked as soon as a local optimum has been found, are shown empirically to be more efficient than the heuristics previously proposed in the literature.     

Control of mobile platforms using a virtual vehicle approach

Two model independent solutions to the problem of controlling wheel-based mobile platforms are proposed. These two algorithms are based on a so called virtual vehicle approach, where the motion of the reference point on the desired trajectory is governed by a differential equation containing error feedback. This, combined with the fact that the proven stable control algorithms are basically proportional regulators with arbitrary positive gains, make the solutions robust with respect to errors and disturbances, as demonstrated by the experimental results     

一种基于正交设计的快速差分演化算法及其应用研究

为了进一步加快差分演化算法的速度和增强算法的鲁棒性,提出了一种基于正交设计的快速差分演化算法,并把它应用于函数优化问题的求解中.新算法在保持传统差分演化算法的简单、有效等特性的同时,具有以下特征：1）采用基于正交设计的杂交算子,并结合直观统计法产生最优子个体;2）采用决策变量分块策略,以减少正交实验次数,加快算法收敛速度;3）提出一种基于非凸理论的多父体混合自适应杂交变异算子,以增强算法的非凸搜索能力和自适应能力;4）简化基本差分演化算法的缩放因子,尽量减少算法的控制参数,方便工程人员的使用.通过对12个标准测试函数进行实验,并与其他演化算法的结果相比较,其结果表明,新算法在解的精度、稳定性和收敛性上表现出很好的性能.     

基于二次误差的点云配准算法

提出了一种基于二次误差的特征描述子,该特征描述子具有旋转不变性。通过提取点的二次误差和邻域点二次误差得到两种特征描述子。基于高斯混合模型的点云配准算法层出不穷,主要原因是概率模型在噪声和离群值方面具有更好的鲁棒性,然而该类方法对于尺度较大的旋转表现并不好,为此将二次误差特征描述子作为高斯混合模型的局部特征优化了高斯混合模型较大旋转中的配准效果,并提出基于双特征的配准策略优化了单一特征的缺陷。通过实验与鲁棒的ICP(iterative closest point)以及流行的基于特征的配准算法在配准效率和配准精度方面进行对比,效率是鲁棒性ICP的3～4倍。在大尺度的旋转中提出的算法具有良好的鲁棒性并且优于大多数流行的算法。     

Fusing bio-inspired vision data for simplified high level scene interpretation: Application to face motion analysis

This paper proposes to demonstrate the advantages of using certain properties of the human visual system in order to develop a set of fusion algorithms for automatic analysis and interpretation of global and local facial motions. The proposed fusion algorithms rely on information coming from human vision models such as human retina and primary visual cortex previously developed at Gipsa-lab. Starting from a set of low level bio-inspired modules (static and moving contour detector, motion event detector and spectrum analyser) which are very efficient for video data pre-processing, it is shown how to organize them together in order to achieve reliable face motion interpretation. In particular, algorithms for global head motion analysis such as head nods, for local eye motion analysis such as blinking, for local mouth motion analysis such as speech lip motion and yawning and for open/close mouth/eye state detection are proposed and their performances are assessed. Thanks to the use of human vision model pre-processing which decorrelates visual information in a reliable manner, fusion algorithms are simplified and remain robust against traditional video acquisition problems (light changes, object detection failure, etc.).     

Selective Refining Harmony Search: A new optimization algorithm

While music improvisation is an NP-hard problem, it has always been handled by musicians successfully. Harmony search is a class of meta-heuristics inspired by the improvisation process of musicians. Inexperienced musicians usually make several harmonies until they find the desired one. However, experienced musicians more rely on their knowledge and experience instead of brute-force searching for a desired harmony. When making a harmony, they are able to distinguish the undesired notes of the current harmony and just modify them instead of throwing away the total harmony and making a new one. This approach of experienced musicians was adopted in this paper to allow the harmony search algorithm to exploit the knowledge and experience accumulated in the harmony memory to refine current harmonies. The underlying algorithm is called Selective Refining Harmony Search in which a new harmony memory update has been utilized. The main differences between the proposed method and the original harmony search are the integration of selection in the improvisation step and introduction of refinement concept. The improvements provided by the proposed method originate from its superior ability to imitate the behavior of musicians in the sense that instead of total harmonies, decision variables are considered during the memory update process. This modification provided outstanding performance to minimize the objective functions of the underlying problems, while computational loads are also better than other methods in most cases. During refinement procedure, two new parameters were employed to make a trade-off between effectiveness and efficiency of the algorithm. During the experiments, the proposed method exhibited robust performance against its two new parameters. Several algorithms including original harmony search and its state-of-the-art variants were implemented to conduct comprehensive comparisons. All of the algorithms were evaluated over IEEE CEC 2010 suite, one of the well-known and challenging benchmark test sets. Several studies have evidenced that the utilization of harmony search has been fostered in diverse application fields. Thus, the improvements to harmony search algorithm provided by the proposed method makes it more efficient for those fields and possible to be employed for more challenging problems.     

利用半点计算椭圆曲线双标量乘法算法

实现椭圆曲线密码体制最主要的运算是椭圆曲线点群上的标量乘法(或点乘)运算。一些基于椭圆曲线的密码协议比如ECDSA签名验证,就需要计算双标量乘法kP+lQ,其中P、Q为椭圆曲线点群上的任意两点。一个高效计算kP+lQ的方法就是同步计算两个标量乘法,而不是分别计算每个标量乘法再相加。通过对域F2m上的椭圆曲线双标量乘法算法进行研究,将半点公式应用于椭圆曲线的双标量乘法中,提出了一种新的同步计算双标量乘法算法,分析了效率,并与传统的基于倍点运算的双标量乘法算法进行了详细的比较,其效率更优。     

An adaptive local range sampling method for reliability-based design optimization using support vector machine and Kriging model

Kriging model is an effective method to overcome huge computational cost for reliability-based design optimization (RBDO) problems. However, the results of RBDO usually depend on constraint boundaries within the local range that contains the RBDO optimum. Determining this local range and building adaptive response surfaces within it can avoid selecting samples in unrelated areas. In this research, a new RBDO process is proposed. In the first phase, Kriging models of constraints are built based on Latin Hypercube sampling method, and updated by two new samples in each iteration. One of these two samples is selected based on SVM and mean squared error to make sure it is located near constraint boundaries. Another one is the deterministic optimum point (DOP) of current Kriging models, which is obtained based on the deterministic optimization and specifies the direction to the RBDO optimum. And the RBDO design point is obtained by SORA. When consecutive RBDO design points are close enough to each other, the local range is determined based on the current RBDO design point and the current DOP. In the second phase, new samples are located on constraint boundaries within the local range to refine Kriging models. The location and the size of the local range is adaptively defined by the RBDO design point and the DOP during each iteration. Several optimization examples are selected to test the computation capability of the proposed method. The results indicate that the new method is more efficient and more accurate.     

Combinatorial and Experimental Methods for Approximate Point Pattern Matching

Point pattern matching is an important problem in computational geometry, with applications in areas like computer vision, object recognition, molecular modeling, and image registration. Traditionally, it has been studied in an exact formulation, where the input point sets are given with arbitrary precision. This leads to algorithms that typically have running times of the order of high-degree polynomials, and require robust calculations of intersection points of high-degree surfaces. We study approximate point pattern matching, with the goal of developing algorithms that are more efficient and more practical than exact algorithms. Our work is motivated by the observation that in practice, data sets that form instances of pattern matching problems are noisy, and so approximate formulations are more appropriate. We present new and efficient algorithms for approximate point pattern matching in two and three dimensions, based on approximate combinatorial distance bounds on sets of points, and via the use of methods from combinatorial pattern matching. We also present an average-case analysis and a detailed empirical study of our methods.     

Efficient algorithms for online tracking of set points in robust model predictive control

This paper presents some computationally efficient algorithms for online tracking of set points in robust model predictive control context subject to state and input constraints. The nonlinear systems are represented by a linear model along with an additive nonlinear term which is locally Lipschitz. As an unstructured uncertainty, this term is replaced in the robust stability constraint by its Lipschitz coefficient. A scheduled control technique is employed to transfer the system to desired set points, given online, by designing local robust model predictive controllers. This scheme includes estimating the regions of feasibility and stability of the related equilibriums and online switching among the local controllers. The proposed optimisation problems for calculating the regions of feasibility and stability are defined as linear matrix inequalities that can be solved in polynomial time. The effectiveness of the proposed algorithms is illustrated by an example.