一种改进的虹膜边界定位算法

虹膜边界定位是虹膜识别系统的重要组成部分,定位的速度、准确度和定位算法的鲁棒性至关重要。针对传统虹膜定位算法的不足,提出了一种改进的虹膜定位算法,充分利用虹膜图像的灰度分布特征,合理地设定相关参数,实现虹膜内边界和外边界快速准确的定位。实验结果表明,该算法与经典的虹膜定位算法相比,定位准确度更高,定位速度更快。     

一种改进的虹膜定位算法

在以往虹膜定位的基础上提出一种改进的虹膜定位算法,主要体现在虹膜的粗定位上,运用Daugman提出的虹膜定位算法能更有效地定位虹膜.实验表明,具有更好的定位效果.     

一种基于圆几何特征的虹膜定位算法

虹膜定位是虹膜识别中基础性环节,其精度和速度决定了虹膜识别系统的性能,为提高虹膜定位的速度,提出一种基于圆几何特征的虹膜内边缘定位算法,利用内外边缘中心的耦合特性缩小微积分方法搜索外边缘的范围。试验结果表明,与经典虹膜定位算法相比,本算法快速、精确、鲁棒。     

基于分块搜索的虹膜定位算法

针对传统虹膜定位算法识别效果不稳定,鲁棒性低的问题,提出基于分块搜索的虹膜定位算法。首先利用虹膜图像灰度变化差异将虹膜图像转换为二值图像,用基于边缘检测的hough圆检测法粗略定位虹膜内圆,再利用分块搜索二值图像对内圆进行精确定位。之后利用卷积运算粗略定位外圆,再对原图像进行分块搜索,观察截图的灰度直方图中的灰度变化精确定位外圆。将得到的虹膜与传统定位算法得到的虹膜用相同的虹膜识别算法处理,结果表明,该算法定位出的图像识别上效果更明显,并且具有很好的鲁棒性。     

虹膜定位

为了提高虹膜定位的速度以及虹膜定位算法的健壮性,提出了一种粗定位与精定位相结合的两步定位法,用以进行虹膜定位,并对现有的虹膜定位算法进行了一些改进,实验结果表明,用两步法进行虹膜定位可以加快定位速度,减少搜索计算的盲目性。     

活体虹膜图像的定位与分割

介绍了一种活体虹膜的定位与分割算法。算法主要分为两部分：圆环的定位与非虹膜区域的去除。本算法根据眼睛的生理特点和数字虹膜图像的实际情况,利用传统定位方法与数学形态学相结合对虹膜区域进行快速而准确的定位,并分别提出了去除眼睑、睫毛和光斑影响的解决方案。算法中也考虑到实际应用可能遇到的影响虹膜定位与分割的问题。实验表明,该算法取得较好的分割结果,并且具有鲁棒性。     

一种基于几何原理的虹膜定位算法

虹膜定位是虹膜识别系统中很重要的组成部分,其中定位的速度及准确率是重要的因素。结合人眼的结构特征设计了虹膜定位算法。算法先定位出瞳孔内任意一点,再用边缘检测模板检测出不共线三点定位出内边缘。算法提出用比较四点两次的定位结果来确定外边缘。对已有的755幅虹膜图片进行定位,准确率达98.15%。并且应用简单的几何原理使得算法的鲁棒性能得到保证。     

一种利用分块统计的虹膜定位算法

虹膜识别是一种新兴的生物特征识别技术，而虹膜定位是虹膜识别的重要步骤，因而精确而快速地进行虹膜定位是有效地进行虹膜识别的重要前提。为了能够快速地进行虹膜定位，在简要介绍现有的虹膜定位算法的基础上，提出了一种新的利用分块统计的虹膜定位算法。由于虹膜边缘可以简单地用圆周描述，因此，该算法第1步先阈值化分割图像，以分别建立虹膜和瞳孔的二进制位图；第2步用游长编码的方法来寻找最大色块的质心，并计算边界点到质心距离的均值。实验结果表明，对于虹膜定位而言，该算法是实用而且有效的。     

采用有向梯度与RANSAC的虹膜定位算法

针对虹膜图像中有较多光斑的情况,提出一种基于有向梯度和随机抽样一致性（RANSAC）相结合的虹膜定位算法。该算法根据瞳孔内某点利用有向梯度提取内缘像素点,采用RANSAC定位虹膜内缘;下采样虹膜图像,利用圆差分算子在瞳孔左右两侧拟合出两个圆,进而合并为一个圆;根据圆的参数在虹膜图像中快速精确定位外缘。实验结果表明：该算法在正确率、定位速度和鲁棒性方面均优于传统的虹膜定位算法。     

最小方差搜索圆心的快速虹膜定位算法

为了改善虹膜识别的实时性,提出一种新的快速虹膜定位方法。首先对虹膜图像进行去噪处理,然后采用类间方差法对图像进行阈值分割,再运用投影方法粗略得到虹膜内边缘圆心和半径,最后根据粗定位得到内边缘圆周参数,采用所提出的算法对虹膜内边缘进行精定位;对于外边缘定位,依据先验知识以及内边缘圆周参数去掉虹膜图像多余的边缘点及噪声点,缩小搜索范围,然后采用同样的算法对外边缘进行精定位。实验结果表明,该方法能够准确快速的定位出虹膜内外边缘,定位速度较传统算法提高了十倍左右,并且减少了传统定位算法搜索的盲目性。     

Comparative Investigations of Algorithms for the Detection of Breaths in Newborns with Disturbed Respiratory Signals

The correct detection of the beginning of inspiration and expiration in the respiratory signals is an essential prerequisite for accurate lung function testing in newborns. Five algorithms for breath detection using pneumotachographically measured flow and volume signals were investigated with regard to the error rate. To compare and to evaluate the reliability of these algorithms 12 minimally and 12 severely disturbed flow and volume signals from spontaneously breathing newborns were used. With the exception of an algorithm based on Walsh-transformed signals, all algorithms work reliably (error rate <1.1%) if disturbances are minimal. In severely disturbed signals there is a great difference between the algorithms. The most robust algorithm tested (trigger of the flow signal with an additional plausibility check of the recognized breath) resulted in an error rate of <3.4%. Not all algorithms tested are suitable for real-time applications because they differ considerably in delay time for breath detection.     

视频数据压缩中的一种运动估值法

利用动态视频的帧间相关性,有效减少序列图象的时间冗余是实现动态视频高效压缩的重要环节,运动补偿法是帧间编码的基本方法。文章在研究运动估值快速算法的基础上,提出了子块象素和螺旋式搜索法,并对其性能作了简单分析。     

基于符号特征的QR码识别算法

在传统QR码识别算法基础上,提出一种利用QR码自身的符号特征进行识别定位的改进算法。对采集到的图像进行二值化处理,由符号特征定位QR码在采集图像中的位置,通过改进的双线性变换校正在采集过程中引起的图像几何形变,并通过逐模块垂直投影提取条码信息。实验结果表明,该算法具有识别时间短、识别率高等特点。     

I-GWO and Ex-GWO: improved algorithms of the Grey Wolf Optimizer to solve global optimization problems

In this paper, two novel meta-heuristic algorithms are introduced to solve global optimization problems inspired by the Grey Wolf Optimizer (GWO) algorithm. In the GWO algorithm, wolves are likely to be located in regions close to each other. Therefore, as they catch the hunt (approaching the solution), they may create an intensity in the same or certain regions. In this case, the mechanism to prevent the escape of the hunt may not work well. First, the proposed algorithm is the expanded model of the GWO algorithm that is called expanded Grey Wolf Optimizer. In this method, the same as GWO, alpha, beta, and delta play the role of the main three wolves. However, the next wolves select and update their positions according to the previous and the first three wolves in each iteration. Another proposed algorithm is based on the incremental model and is, therefore, called incremental Grey Wolf Optimizer. In this method, each wolf updates its own position based on all the wolves selected before it. There is the possibility of finding solutions (hunts) quicker than according to other algorithms in the same category. However, they may not always guarantee to find a good solution because of their act dependent on each other. Both algorithms focus on exploration and exploitation. In this paper, the proposed algorithms are simulated over 33 benchmark functions and the related results are compared with well-known optimization algorithms. The results of the proposed algorithms seem to be good solutions for various problems.

     

基于样条的矢量数据多尺度表达与压缩算法

传统矢量曲线压缩算法不能解决曲线压缩后的平滑问题,并可能产生拓扑异化。为此,提出基于样条的矢量数据多尺度表达与压缩算法。选择样条的光滑因子作为阈值对曲线进行多尺度表达与压缩,保持压缩前后曲线的光滑特性,利用少量的控制点描述整条曲线,从而提高矢量曲线处理的速度和质量。实验结果表明,该算法仅需9个控制点就能逼近给定的一条原始曲线,验证了其高效性。     

一种基于免疫原理的多目标组播路由算法

在分析传统多目标组播路由算法存在问题的基础上,通过将每个QoS参数分别作为一个独立的优化目标进行优化,构造了一个基于免疫原理的多目标组播路由算法,并对算法的参数敏感性进行了分析。实验结果表明所设计的算法能够同时优化多个QoS参数,有效地协调了各参数之间的矛盾。     

The correctness proof of Ben-Or’s randomized consensus algorithm

In a ground-breaking paper that appeared in 1983, Ben-Or presented the first randomized algorithm to solve consensus in an asynchronous message-passing system where processes can fail by crashing. Although more efficient randomized algorithms were subsequently proposed, Ben-Or’s algorithm is still the simplest and most elegant one. For this reason, it is often taught in distributed computing courses and it appears in several textbooks. Even though Ben-Or’s algorithm is widely known and it is very simple, surprisingly a proof of correctness of the algorithm has not yet appeared: previously published proofs make some simplifying assumptions—specifically, they either assume that f < n/3 (n is the total number of processes and f is maximum number of processes that may crash) or that the adversary is weak, that is, it cannot see the process states or the content of the messages. In this paper, we present a correctness proof for Ben-Or’s randomized consensus algorithm for the case that f < n/2 process crashes and the adversary is strong (i.e., it can see the process states and message contents, and schedule the process steps and message receipts accordingly). To the best of our knowledge, this is the first full proof of this classical algorithm. We also demonstrate a counterintuitive problem that may occur if one uses the well-known abstraction of a “global coin” to modularize and speed up randomized consensus algorithms, such as Ben-Or’s algorithm. Specifically, we show that contrary to common belief, the use of a global coin can sometimes be deleterious rather than beneficial: instead of speeding up Ben-Or’s algorithm, the use of a global coin in this algorithm may actually prevent termination.     

Supporting comprehensible presentation of clone candidates through two-dimensional maximisation

Duplicated code detection has been an active research field for several decades. Although many algorithms have been proposed, only a few researches have focussed on the comprehensive presentation of the detected clones. During the evaluation of clone detectors developed by the authors, it was observed that the results of the clone detectors were hard to comprehend. Therefore, in this paper a broadly suitable grouping method with which clone pairs can be easily grouped together to provide a more compact result is presented. The grouping algorithm is examined and a more precise revised algorithm is proposed to present all of the candidates to the user.     

基于在线光度标定的半直接视觉SLAM算法

为解决视觉SLAM（同时定位与地图创建）算法依赖图像亮度而对光照变化场景敏感的问题,提出一种基于在线光度标定的半直接视觉SLAM算法。首先,根据相机成像原理,提出基于光度标定的帧间位姿估计方法,在求解位姿的同时对原始的输入图像进行光度校正。其次,在特征追踪环节采取最近共视关键帧匹配策略,以提升特征点匹配效率。最后,对后端重投影迭代优化策略进行改进,降低光照变化对视觉SLAM算法的精度和鲁棒性的影响。在TUM、EuRoC数据集上的实验结果表明,本算法的轨迹估计精度优于LSD-SLAM和SVO 2.0算法,尤其是在中等难度、高难度的数据集序列上。在真实环境测试中,通过对比本算法与激光方法的轨迹估计结果,证明本算法有效提高了传统视觉SLAM方法在光照不均匀场景下的定位精度与鲁棒性。     

RSA公钥密码体制中安全大素数的生成

RSA算法是基于数论的公钥密码体制,是公钥密码体制中最优秀的加密算法。由于RSA算法中大素数的生成对RSA加密算法的安全性有直接的影响,其寻找大素数的实现难度大,运算时间长。文中在研究了密钥生成的一般算法的基础上．即确定性素数产生和概率性素数产生方法．给出了利用Montgomery算法优化的Miller-Rabin算法和Pocldington定理算法实现．构造了大素数的生成算法,以提高RSA算法的安全性和运行速度。