Noisy logo recognition using line segment Hausdorff distance

Logo recognition is of great interest in the document and shape analysis domain. In order to develop a recognition method that is robust to employ under adverse conditions such as different scale/orientation, broken curves, added noise and occlusion, a modified line segment Hausdorff distance is proposed in this paper. The new approach has the advantage to incorporate structural and spatial information to compute dissimilarity between two sets of line segments rather than two sets of points. The proposed technique has been applied on line segments generated from logos with encouraging results. Clear cut distinction between the correct and incorrect matches has been observed. This suggests a strong potential for logo and shape recognition system.     

Spatially eigen-weighted Hausdorff distances for human face recognition

Hausdorff distance is an efficient measure of the similarity of two point sets. In this paper, we propose a new spatially weighted Hausdorff distance measure for human face recognition. The weighting function used in the Hausdorff distance measure is based on an eigenface, which has a large value at locations of importance facial features and can reflect the face structure more effectively. Two modified Hausdorff distances, namely, “spatially eigen-weighted Hausdorff distance” (SEWHD) and “spatially eigen-weighted ‘doubly’ Hausdorff distance” (SEW2HD) are proposed, which incorporate the information about the location of important facial features such as eyes, mouth, and face contour so that distances at those regions will be emphasized. Experimental results based on a combination of the ORL, MIT, and Yale face databases show that SEW2HD can achieve recognition rates of 83%, 90% and 92% for the first one, the first three and the first five likely matched faces, respectively, while the corresponding recognition rates of SEWHD are 80%, 83% and 88%, respectively.     

A new algorithm for computing the minimum Hausdorff distance between two point sets on a line under translation

To determine the similarity of two point sets is one of the major goals of pattern recognition and computer graphics. One widely studied similarity measure for point sets is the Hausdorff distance. So far, various computational methods have been proposed for computing the minimum Hausdorff distance. In this paper, we propose a new algorithm to compute the minimum Hausdorff distance between two point sets on a line under translation, which outperforms other existing algorithms in terms of efficiency despite its complexity of O((m+n)lg(m+n)), where m and n are the sizes of two point sets.     

Hybrid image matching combining Hausdorff distance with normalized gradient matching

Image matching has been a central problem in computer vision and image processing for decades. Most of the previous approaches to image matching can be categorized into the intensity-based and edge-based comparison. Hausdorff distance has been widely used for comparing point sets or edge maps since it does not require point correspondences. In this paper, we propose a new image similarity measure combining the Hausdorff distance with a normalized gradient consistency score for image matching. The normalized gradient consistency score is designed to compare the normalized image gradient fields between two images to alleviate the illumination variation problem in image matching. By combining the edge-based and intensity-based information for image matching, we are able to achieve robust image matching under different lighting conditions. We show the superior robustness property of the proposed image matching technique through experiments on face recognition under different lighting conditions.     

Robust Hausdorff distance measure for face recognition

Face is considered to be one of the biometrics in automatic person identification. The non-intrusive nature of face recognition makes it an attractive choice. For face recognition system to be practical, it should be robust to variations in illumination, pose and expression as humans recognize faces irrespective of all these variations. In this paper, an attempt to address these issues is made using a new Hausdorff distance-based measure. The proposed measure represent the gray values of pixels in face images as vectors giving the neighborhood intensity distribution of the pixels. The transformation is expected to be less sensitive to illumination variations besides preserving the appearance of face embedded in the original gray image. While the existing Hausdorff distance-based measures are defined between the binary edge images of faces which contains primarily structural information, the proposed measure gives the dissimilarity between the appearance of faces. An efficient method to compute the proposed measure is presented. The performance of the method on bench mark face databases shows that it is robust to considerable variations in pose, expression and illumination. Comparison with some of the existing Hausdorff distance-based methods shows that the proposed method performs better in many cases.     

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。     

基于改进Hausdorff距离的人脸匹配方法

提出了一种基于改进Hausdorff距离的人脸相似度匹配的方法,该方法首先将人脸划分为脸型、双眼、鼻、嘴等几个特征点集,分别计算各部分的改进Hausdorff距离,然后进行加权计算相似度。利用该方法,在ASM（主动形状模型）定位人脸的基础上进行了人脸检索。实验表明,利用人脸相似度计算方法对人脸特征库进行搜索,达到了较好的效果。同时结合ASM自动人脸检测,本方法可以全自动完成人脸匹配,应用于人脸识别及数字娱乐等领域。     

COMPARING FACE IMAGES USING THE MODIFIED HAUSDORFF DISTANCE

We introduce a novel methodology applicable to face matching and fast screening of large facial databases. The proposed shape comparison method operates on edge maps and derives holistic similarity measures, yet, it does not require solving the point correspondence problem. While the use of edge images is important to introduce robustness to changes in illumination, the lack of point-to-point matching delivers speed and tolerance to local non-rigid distortions. In particular, we propose a face similarity measure derived as a variant of the Hausdorff distance by introducing the notion of a neighborhood function (N) and associated penalties (P). Experimental results on a large set of face images demonstrate that our approach produces excellent recognition results even when less than 3% of the original grey-scale face image information is stored in the face database (gallery). These results implicate that the process of face recognition may start at a much earlier stage of visual processing than it was earlier suggested. We argue, that edge-like retinal images of faces are initially screened “at a glance” without the involvement of high-level cognitive functions thus delivering high speed and reducing computational complexity.     

基于LEM的在线掌纹识别

主要研究利用掌纹对人进行身份鉴定。在掌纹的各种特征中，线特征是一种非常重要的特征，但由于掌纹线不规则，几乎不能用数学进行精确的刻画，因此提出了一种用直线段去近似掌纹线的方法，利用线段Hausdorff距离方法去匹配这些线段集，并对其进行了改进。实验的结果表明了该方法的有效性。     

Human face recognition based on spatially weighted Hausdorff distance

The edge map of a facial image contains abundant information about its shape and structure, which is useful for face recognition. To compare edge images, Hausdorff distance is an efficient measure that can determine the degree of their resemblance, and does not require a knowledge of correspondence among those points in the two edge maps. In this paper, a new modified Hausdorff distance measure is proposed, which has a better discriminant power. As different facial regions have different degrees of significance for face recognition, a new modified Hausdorff distance is proposed which is weighted according to a weighted function derived from the spatial information of the human face; hence crucial regions are emphasized for face identification. Experimental results show that the distance measure can achieve recognition rates of 80%, 87%, and 91% for the first, the first five, and the first seven likely matched faces, respectively.     

Development and evaluation of fast branch-and-bound algorithm for feature matching based on line segments

By extending the previously proposed geometric branch-and-bound algorithm with bounded alignment for point pattern matching, the paper presents the development and evaluation of a new and fast algorithm for image registration based on line segments. Using synthetically generated data sets with randomly distributed line segments and hard test cases with highly symmetric line patterns, as well as real remote sensing images, the developed algorithm is shown to be computationally fast, highly robust, capable of handling severely corrupted data sets with considerable line segment position errors as well as significant fragmented and spurious line segments in the images to be matched.     

Face recognition using line edge map

The automatic recognition of human faces presents a significant challenge to the pattern recognition research community. Typically, human faces are very similar in structure with minor differences from person to person. They are actually within one class of "human face". Furthermore, lighting conditions change, while facial expressions and pose variations further complicate the face recognition task as one of the difficult problems in pattern analysis. This paper proposes a novel concept: namely, that faces can be recognized using a line edge map (LEM). The LEM, a compact face feature, is generated for face coding and recognition. A thorough investigation of the proposed concept is conducted which covers all aspects of human face recognition, i.e. face recognition under (1) controlled/ideal conditions and size variations, (2) varying lighting conditions, (3) varying facial expressions, and (4) varying pose. The system performance is also compared with the eigenface method, one of the best face recognition techniques, and with reported experimental results of other methods. A face pre-filtering technique is proposed to speed up the search process. It is a very encouraging to find that the proposed face recognition technique has performed better than the eigenface method in most of the comparison experiments. This research demonstrates that the LEM, together with the proposed generic line-segment Hausdorff distance measure, provides a new method for face coding and recognition     

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.     

区间复模糊软集的距离测度及其应用

基于区间复模糊软集的概念,定义了多种区间复模糊软集的距离测度公式,包含Hausdorff距离、Hamming距离、Euclidean距离、广义Hausdorff距离、广义Euclidean距离、广义加权Hausdorff距离、广义加权Euclidean距离、加权Hausdorff距离、加权Hamming距离、加权Euclidean距离。提出了除交、并、补运算外的区间复模糊软集的加法、乘法、部分隶属度和部分非隶属度运算以及距离测度之间的运算性质。基于区间复模糊软集距离测度构造了一种TOPSIS决策方法,并将这种决策方法应用于经济分析中,验证了所提方法的可行性。     

Polyline approach for approximating Hausdorff distance between planar free-form curves

This paper presents a practical polyline approach for approximating the Hausdorff distance between planar free-form curves. After the input curves are approximated with polylines using the recursively splitting method, the precise Hausdorff distance between polylines is computed as the approximation of the Hausdorff distance between free-form curves, and the error of the approximation is controllable. The computation of the Hausdorff distance between polylines is based on an incremental algorithm that computes the directed Hausdorff distance from a line segment to a polyline. Furthermore, not every segment on polylines contributes to the final Hausdorff distance. Based on the bound properties of the Hausdorff distance and the continuity of polylines, two pruning strategies are applied in order to prune useless segments. The R-Tree structure is employed as well to accelerate the pruning process. We experimented on Bezier curves, B-Spline curves and NURBS curves respectively with our algorithm, and there are 95% segments pruned on approximating polylines in average. Two comparisons are also presented: One is with an algorithm computing the directed Hausdorff distance on polylines by building Voronoi diagram of segments. The other comparison is with equation solving and pruning methods for computing the Hausdorff distance between free-form curves.     

基于排除算法的快速三维人脸识别方法

提出了一种基于排除算法的快速三维人脸识别方法. 首先, 利用主成分分析(Principal component analysis, PCA)对自动切割的不同姿态人脸进行校正, 将所有人脸转换到统一的坐标系下; 然后提取人脸侧面轮廓线, 利用基于LTS-Hausdorff距离的轮廓线对齐方法对库集对象进行排除; 最后, 采用基于刚性区域的改进迭代最近点(Iterative closest point, ICP)算法对剩余的库集模型进行精确匹配, 给出最终识别结果. 在FRGC V2.0人脸数据库的实验结果表明, 该方法具有较好的实时性和鲁棒性.     

基于Voronoi图的线段最近对查询

最近对查询是空间数据库中的重要查询之一。已有的关于最近对查询的研究基本集中在点对象上,对空间对象无法抽象为点的对象则研究较少。提出基于平面线段的最近对查询,即找出两个平面线段集中距离最近的线段对。提出基于Voronoi图的线段最近对查询算法,该方法构造两个线段集的Voronoi图,利用Voronoi图的最近邻近特性和局域动态特性找到互为最近邻的线段对,从中找到结果,以缩减大量的计算代价。对线段集中增加线段和删除线段的情况做了相应的处理。实验证明,该算法具有较高的查询效率。     

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。     

利用Hausdorff距离人脸图像定位算法

利用 Hausdorff距离的人脸图像定位算法是动态人脸识别系统的预处理工作 .定位算法与识别系统相结合 ,可提高人脸识别率 .由于动态人脸图像在移动过程中成像大小不是完全相同的 ,这样对进一步运动中人脸图像相互关系有较大的影响 ,提出了利用 Hausdorff距离来对图像中人脸进行定位 ,将图像中人脸部分提取出来并对其大小做调整 ,过滤掉背景信息 ,一定程度上简化了后续识别工作