一种鲁棒型Hausdorff距离图像匹配方法

图像匹配是图像处理的一项关键技术,传统方法受光照、噪声和遮挡的影响,使匹配过程变得困难。为了提高图像匹配的鲁棒性,提出了一种基于方向信息的鲁棒型Hausdorff距离匹配方法。该方法采用方向信息提取图像边缘,通过计算边缘匹配率(edge matching rate,EMR)获得候选匹配区域,然后采用修正后的Hausdorff距离构造相似性测度。实验结果表明,该方法加快了匹配过程,提高了抗噪性能,并能够准确匹配含有遮挡和伪边缘点的图像,从而解决了基于传统Hausdorff距离匹配方法因噪声点、伪边缘点和出格点而造成的误匹配问题。     

基于信息测度特征和Hausdorff距离的图像匹配策略

由于传统的图像匹配方法受到诸如景物的遮挡、光照和噪声的影响比较大，并且需要建立模板与图像间的对应关系，因而使实际图像的匹配变得困难。为了克服上述缺陷，以便快速地进行图像匹配，通过引入信息测度概念来提取边缘特征点，并基于修正后的：Hausdorff距离构造相似性测度，提出了一种基于信息测度和：Hausdorff距离的图像匹配策略。该策略不仅加快了匹配过程，提高了抗噪性能，而且能准确匹配遮挡图像，从而较好地解决了基于传统Hausdorff距离的图像匹配因噪声点、伪边缘和出格点而造成的误匹配问题。实验结果证明，该方法是正确有效的。     

角点和边缘结合的红外与可见光图像匹配

针对可见光与红外图像在复杂背景下匹配准确度低的缺点,提出一种新的基于边缘特征的匹配算法,算法首先对图像进行小波去噪,然后提取图像的边缘及其角点,在匹配测度上提出了新的CSS-EDGE-WHD距离,该距离测度在加权Hausdorff的基础上,结合了边缘角点的方向差异性,并推出相应权值,既利用了边缘点的位置信息,又利用了边缘上曲率极大值点(角点)的方向特性,对不同的边缘点采用不同的权值进行Hausdorff加权,最后将两种边缘点Hausdorff距离结合得到最终的匹配算法。仿真实验验证了该算法在背景复杂的红外与可见光图像匹配中也能保持较高的准确度。     

一种基于鲁棒Hausdorff距离的目标匹配算法

在传统的基于边缘位置的Hausdorff距离匹配的基础上,将边缘的梯度信息引入到距离度量当中,构造了一种新的三维距离函数。在此基础上,提出了一种鲁棒的三维Hausdorff距离及其目标匹配算法,采用粗匹配与精匹配相结合的两步匹配策略有效解决了由距离度量维数增加所导致的算法复杂性增大的问题。实验表明,该算法相对于传统的基于边缘位置的Hausdorff距离目标匹配算法在鲁棒性上有很大的提高。     

基于邻域灰度信息的Hausdorff距离图像匹配方法

针对可见光与红外图像由于成像机理不同引起的图像灰度值差异大、边缘轮廓不一致、传统基于灰度和基于特征的匹配方法匹配概率不高等问题,在分析了各种Hausdorff距离算法的前提下,引入可见光与红外图像的灰度信息,提出一种基于邻域灰度信息Hausdorff距离的图像匹配方法。该方法在计算图像边缘特征点相似性的基础上,增加了邻域归一化灰度方差计算,有效解决了由于边缘差异引起的Hausdorff距离算法对可见光/红外图像匹配概率不高的问题。经可见光与红外图像匹配的仿真实验表明,在各种条件下,该算法与传统Hausdorff距离算法相比,有效提高了在不同光照下图像的匹配效率以及对噪声的抗干扰性能。     

基于Hausdorff距离的图像配准快速算法

在图像配准过程中,传统Hausdorff距离算法的计算量较大。针对该问题,提出一种基于Hausdorff距离的图像配准快速算法。将参考图像和待配准图像进行边缘检测,在待配准图像上任意选取一个模板,通过设定一个变化的阈值对Hausdorff距离算法进行改进,以减少不必要点的计算,实现快速匹配,并根据匹配数据,对图像进行尺度变换及旋转操作,使2幅图像能在空间上配准。实验结果表明,与传统的配准算法相比,该算法的计算复杂度较低。     

一种用于辅助导航的快速图像匹配方法

为了提高辅助导航中多传感器图像匹配的精确性和实时性,首先提取图像的边缘特征,并用3-4距离变换(3-4DT)方法对边缘二值图像进行变换,以变换后的边缘距离图像为匹配特征;针对传统Hausdorff距离的局限性提出了一种融合点集重合数的Hausdorff距离,并以之为相似性度量;搜索策略根据人眼视觉系统的机制采用一种由远到近的分层匹配方法,同时使用一种改进的实数编码遗传算法来加快底层图像匹配的速度。实验结果为平均匹配时间为1283m s,平均误差值为1.036,表明匹配方法能满足导航要求。     

基于Hausdorff距离的插值方法评价

在图像放大处理中通常要将图像进行插值,在Hausdorff测度理论的基础上对Hausdorff距离进行了改进．然后将改进后的Haosdorff距离作为评价图像质量的一种标准，对放大后的图像进行量化评估,并与其他的评价方法进行比较．结果表明用Hausdorff距离作为评价标准得到的结果更接近图像的视觉效果.     

基于鲁棒迭代优化的图像拼接算法

针对保持直线边缘不畸变和改善拼接区域精度的需要,提出了一种基于鲁棒迭代优化的图像拼接算法。采用SIFT特征匹配算法提取不变特征,依据RANSAC鲁棒算法估计单应性矩阵获取精确内点匹配点对,参照内点匹配点对作为参数,利用最小二乘法重新估计单应性矩阵,最终通过鲁棒迭代优化重新计算更多内点直至内点收敛。实验结果表明,所提出的算法使得直线边缘维持直线化,同时拼接精度得到提高。     

一种新的直线特征提取和定位算法

提出了一种从图像中精确提取直线特征并定位其端点和方向的新方法，克服了传统边缘跟踪算法的局部性。算法首先跟踪边缘点，递归分割成离散直线段，然后从全局角度合并这些离散的直线段，最后给出了两种鲁棒的直线拟合策略。实验结果表明该算法具有良好的性能，能够适用于较广范围的真实图像。     

On the localization performance measure and optimal edge detection

The localization performance measure of edge detectors is addressed. A one-dimensional formulation of the problem is considered. A linear space-invariant filter is used for the detection. The locations of local maxima in the thresholded output of the filter are declared to be the edges. The limitations of conventional performance measures are shown, and a localization performance measure for edge detection is suggested. This performance measure is based on the theory of zero-crossings of stochastic processes. It is shown that the derivative of a Gaussian is the optimal edge detector for the measure     

A new cluster validity measure and its application to image compression

Many validity measures have been proposed for evaluating clustering results. Most of these popular validity measures do not work well for clusters with different densities and/or sizes. They usually have a tendency of ignoring clusters with low densities. In this paper, we propose a new validity measure that can deal with this situation. In addition, we also propose a modified K-means algorithm that can assign more cluster centres to areas with low densities of data than the conventional K-means algorithm does. First, several artificial data sets are used to test the performance of the proposed measure. Then the proposed measure and the modified K-means algorithm are applied to reduce the edge degradation in vector quantisation of image compression.     

基于相似度加权的自适应HD算法

Hausdorff距离(Hausdorff distance, HD)是一种点集与点集之间的距离测度, 常用于目标物体的匹配、跟踪和识别等. 本文在分析经典HD及改进算法的基础上, 提出了一种基于相似度加权的自适应HD (Adaptive Hausdarff distance, AHD)算法. AHD算法利用不同点到点集的最小距离的个数作为匹配相似度的测量, 并舍弃对判断匹配几乎没有作用的较大的点到点集的最小距离值; 同时根据点到点集的最小距离自适应选择权值, 从而得到一种基于相似度测量加权系数; 通过利用部分点到点集的最小距离和基于相似度的加权平均, 既增强了算法的鲁棒性, 又尽可能地保证了算法的精度. 实验结果显示, AHD算法在匹配准确性、抵抗噪声和遮挡干扰等方面性能良好.     

基于螺纹边缘图像的快速SSDA算法

序贯相似性检测算法（sSDA）是针对传统的模板匹配算法提出的一种高效的图像匹配算法,但是SSDA算法难以满足螺纹检测的实时性。针对传统SSDA算法的不足,提出了一种基于螺纹边缘图像的快速SSDA算法。该方法用形态学对螺纹图像进行了边缘提取,然后只在边缘处进行匹配,从而大大减少了匹配的次数,同时提出了减小匹配运算量的方法。     

An effective and intelligent Windows application filtering system using software similarity

As licensed programs are pirated and illegally spread over the Internet, it is necessary to filter illegally distributed or cracked programs. The conventional software filtering systems can prevent unauthorized dissemination of the programs maintained by their databases using an exact matching method where the feature of a suspicious program is the same as that of any program stored in the database. However, the conventional filtering systems have some limitations to deal with cracked or new programs which are not maintained by their database. To address the limitations, we design and implement an efficient and intelligent software filtering system based on software similarity. Our system measures the similarity of the characteristics extracted from an original program and a suspicious one (or, a cracked one) and then determines whether the suspicious program is a cracked version of the copyrighted original program based on the similarity measure. In addition, the proposed system can handle a new program by categorizing it using a machine learning scheme. This scheme helps an unknown program to be identified by narrowing the search space. To demonstrate the effectiveness of the proposed system, we perform a series of experiments on a number of executable programs under Microsoft Windows. The experimental results show that our system has achieved comparable performance.     

利用格式塔的高分辨率遥感影像建筑物提取

目的 格式塔心理学的理论基础为通过对事物的部分感知,实现对事物整体的认识。本文将该思想应用到建筑物提取中,提出一种兼顾目标细节及整体几何特征的高分辨率遥感影像建筑物提取方法。方法 首先,利用SIFT算法提取特征点作为候选边缘点;然后定义格式塔序列连续性原则判别边缘点,从而得到边缘点点集;并由边缘点点集拟合边缘,实现遥感影像建筑物提取。结果 利用提出算法,对WorldView-2遥感影像进行建筑物提取实验。通过与基于多尺度分割和区域合并的建筑物提取算法对比可以看出,提出算法能够更加准确、完整地提取出建筑物。采用分支因子、遗漏因子、检测率和完整性4个定量化指标对实验结果的定量评价,本文算法的检测率和完整性均大于对比算法,且本文算法的检测率均在95%以上,验证了提出基于格式塔理论的高分辨率遥感影像建筑物提取算法的有效性和准确性。结论 基于格式塔的高分辨率遥感影像建筑物提取算法能够准确刻画建筑物细节特征,同时兼顾建筑物整体几何轮廓,准确提取高分辨率遥感影像中的建筑物。本文算法针对高分辨率遥感影像,适用于提取边缘具有直线特征的建筑物。使用本文算法进行遥感影像建筑物提取时,提取精度会随分辨率降低而降低,建议实验影像分辨率在5 m以上。     

Measuring ambiguity in the evidence theory

In the framework of evidence theory, ambiguity is a general term proposed by Klir and Yuan in 1995 to gather the two types of uncertainty coexisting in this theory: discord and nonspecificity. Respecting the five requirements of total measures of uncertainty in the evidence theory, different ways have been proposed to quantify the total uncertainty, i.e., the ambiguity of a belief function. Among them is a measure of aggregate uncertainty, called AU, that captures in an aggregate fashion both types of uncertainty. But some shortcomings of AU have been identified, which are that: 1) it is complicated to compute; 2) it is highly insensitive to changes in evidence; and 3) it hides the distinction between the two types of uncertainty that coexist in every theory of imprecise probabilities. To overcome the shortcomings, Klir and Smith defined the TU/sub 1/ measure that is a linear combination of the AU measure and the nonspecificity measure N. But the TU/sub 1/ measure cannot solve the problem of computing complexity, and brings a new problem with the choice of the linear parameter /spl delta/. In this paper, an alternative measure to AU for quantifying ambiguity of belief functions is proposed. This measure, called Ambiguity Measure (AM), besides satisfying all the requirements for general measures also overcomes some of the shortcomings of the AU measure. Indeed, AM overcomes the limitations of AU by: 1) minimizing complexity for minimum number of focal points; 2) allowing for sensitivity changes in evidence; and 3) better distinguishing discord and nonspecificity. Moreover, AM is a special case of TU/sub 1/ that does not need the parameter /spl delta/.     

Fast ellipse detection based on three point algorithm with edge angle information

In this paper, we introduce a fast ellipse detection method that uses the geometric properties of three points on an ellipse. Many conventional ellipse detection methods carry out detection using five points, but a random selection of such points among candidate edges requires much redundant processing. To search for an ellipse with the minimum number of points, this study used the normal and differential equations of an ellipse, which requires three points based on their locations and edge angles. First, to reduce the number of candidate edges, the edges were divided into 8 groups depending on the edge angle, and then a new geometric constraint called the quadrant condition was introduced to reduce noisy candidate edges. Clustering was employed to find prominent candidates in the space of a few ellipse parameters. Experiments using many real images showed that the proposed method satisfies both reliability and computing speed for ellipse detection.