The thin plate spline robust point matching (TPS-RPM) algorithm: A revisit

This paper reviews the TPS-RPM algorithm (Chui and Rangarajan, 2003) for robustly registering two sets of points and demonstrates from a theoretical point of view its inherent limited performance when outliers are present in both point sets simultaneously. A double-sided outlier handling approach is proposed to overcome this limitation with a rigorous mathematical proof as the underlying theoretical support. This double-sided outlier handling approach is proved to be equivalent to the original formulation of the point matching problem. For a practical application, we also extend the TPS-RPM algorithms to non-rigid image registration by registering two sets of sparse features extracted from images. The intensity information of the extracted features are incorporated into feature matching in order to reduce the impact from outliers. Our experiments demonstrate the double-sided outlier handling approach and the efficiency of intensity information in assisting outlier detection.     

On the efficiency of bootstrap method into the analysis contingency table

The bootstrap method is a computer intensive statistical method that is widely used in performing nonparametric inference. Categorical data analysis, in particular the analysis of contingency tables, is commonly used in applied field. This work considers nonparametric bootstrap tests for the analysis of contingency tables. There are only a few research papers which exploit this field. The p-values of tests in contingency tables are discrete and should be uniformly distributed under the null hypothesis. The results of this article show that corresponding bootstrap versions work better than the standard tests. Properties of the proposed tests are illustrated and discussed using Monte Carlo simulations. This article concludes with an analytical example that examines the performance of the proposed tests and the confidence interval of the association coefficient.     

Inexact graph matching based on kernels for object retrieval in image databases

In the framework of online object retrieval with learning, we address the problem of graph matching using kernel functions. An image is represented by a graph of regions where the edges represent the spatial relationships. Kernels on graphs are built from kernel on walks in the graph. This paper firstly proposes new kernels on graphs and on walks, which are very efficient for graphs of regions. Secondly we propose fast solutions for exact or approximate computation of these kernels. Thirdly we show results for the retrieval of images containing a specific object with the help of very few examples and counter-examples in the framework of an active retrieval scheme.     

基于SoPC的网络入侵检测中模式匹配系统设计

设计了一种基于FPGA的模式匹配系统,通过Verilog HDL语言实现系统主体;采用开源的Snort规则,选用由“异或”运算组成的适合FPGA处理的HashMem函数进行模式匹配;通过软件预处理找出Snort中的冲突模式串进行单独匹配从而用硬件方法解决冲突.硬件电路采用DM9000A网络控制器接收网络数据.实验结果显...     

A quick tour on suffix arrays and compressed suffix arrays

Suffix arrays are a key data structure for solving a run of problems on texts and sequences, from data compression and information retrieval to biological sequence analysis and pattern discovery. In their simplest version, they can just be seen as a permutation of the elements in {1,2,…,n}, encoding the sorted sequence of suffixes from a given text of length n, under the lexicographic order. Yet, they are on a par with ubiquitous and sophisticated suffix trees. Over the years, many interesting combinatorial properties have been devised for this special class of permutations: for instance, they can implicitly encode extra information, and they are a well characterized subset of the n! permutations. This paper gives a short tutorial on suffix arrays and their compressed version to explore and review some of their algorithmic features, discussing the space issues related to their usage in text indexing, combinatorial pattern matching, and data compression.     

Strong matching preclusion

The matching preclusion problem, introduced by Brigham et al. [R.C. Brigham, F. Harary, E.C. Violin, and J. Yellen, Perfect-matching preclusion, Congressus Numerantium 174 (2005) 185-192], studies how to effectively make a graph have neither perfect matchings nor almost perfect matchings by deleting as small a number of edges as possible. Extending this concept, we consider a more general matching preclusion problem, called the strong matching preclusion, in which deletion of vertices is additionally permitted. We establish the strong matching preclusion number and all possible minimum strong matching preclusion sets for various classes of graphs.     

大方位失准角传递对准非线性模型研究

针对惯导系统大方位失准角传递对准的非线性误差方程不准确问题,同时考虑杆臂效应和挠曲变形两种主要误差因素来建立挠曲变形和杆臂效应加速度一体化误差模型,从而完善惯导系统大方位失准角传递对准非线性模型.针对非线性滤波的稳定性和快速性问题,采用比例修正无味卡尔曼(UKF)滤波模型估计姿态失准角,并采用速度匹配算法对模型的正确性和滤波的有效性进行仿真验证.结果表明,该模型在大方位失准角传递对准时可以满足对准精度和时间的要求.     

基于Lucene的中文分词器的设计与实现

针对Lucene自带中文分词器分词效果差的缺点,在分析现有分词词典机制的基础上,设计了基于全哈希整词二分算法的分词器,并集成到Lucene中,算法通过对整词进行哈希,减少词条匹配次数,提高分词效率。该分词器词典文件维护方便,可以根据不同应用的要求进行定制,从而提高了检索效率。     

复杂背景下的多阈值人眼定位新算法*

提出了一种基于复杂背景下的多阈值人眼定位算法。该算法首先对图像进行光线补偿,减小由于光照不均等因素造成的影响;然后根据对人眼灰度特征的分析来确定3个人眼二值化阈值,从而分离出人眼候选区域;最后根据人脸的几何特征、人眼位置判定准则以及计算二维相关系数来排除非人眼区域,从而定位双眼。实验结果表明,该算法能在不同条件下准确定位人眼,且具有较快的定位速度和较高的检测率。     

面向新型业务的多维快速包分类算法研究*

网络中新兴的新型业务例如P2P,VoIP,Worm等日益增多,在整个网络流量中占有越来越多的比重。对这些新型业务的监测和控制要求路由器等核心设备必须有能力对数据包进行快速和准确的分类。本文从这些新型业务包分类规则库的特点着手,提出在多维多模式匹配情况下的三种包分类决策树,通过实验说明这些决策树应用在新型业务的包分类上与传统的串行决策树性能各自的差异,进而得出适合新型业务包分类的最佳算法。