水下人造目标线特征快速检测算法

为了从水下复杂场景中快速检测人造目标,提出了一种在图像小波变换低频子带上进行直线实时检测的算法。首先,利用小波变换确定显著线特征检测的合适尺度;然后在确定的低频子带小图像上进行边缘检测,利用梯度直方图和迭代法相结合自适应确定边缘检测的分割阈值,得到显著特征的边缘点;再利用改进的Hough变换检测人造目标的直线特征;最后在原始图像上标记出直线检测的结果。实验结果表明:提出的算法可以准确检测出水下复杂背景中人造目标的直线特征,并且具有良好的实时性,满足水下人造目标视频检测的应用要求。     

A simple randomized algorithm for two-dimensional strip packing

Two-dimensional strip packing problem is to pack given rectangular pieces on a strip of stock sheet having fixed width and infinite height. Its aim is to minimize the height of the strip such that non-guillotinable and fix orientation constraints are meet. In this paper, an improved scoring rule is developed and the least waste priority strategy is introduced, and a randomized algorithm is presented for solving this problem. This algorithm is very simple and does not need to set any parameters. Computational results on a wide range of benchmark problem instances show that the proposed algorithm obtains a better or matching performance as compared to the most of the previously published meta-heuristics.     

一种快速霍夫变换算法

霍夫变换是图像处理中的一种常用的检测算法,能够有效地在较大的噪声环境中提取图像中的特定信息。但标准的霍夫变换算法运算量大,处理速度慢,有较大的局限性。该文讨论了一种快速霍夫变换算法,该算法有效地降低了传统霍夫变换算法的时间复杂度,提高了计算效率和运算速度,对于提高图像处理的速度,增强图像处理的实时性有着显著的作用。     

广义Hough变换:多个圆的快速随机检测

以随机采样到的2个图像点及在此2点的中垂线上搜索第3个图像点来确定候选圆．当随机采样2个图像点时,通过剔除孤立、半连续噪声点减少了无效采样;当搜索候选圆的第3点时,剔除上述2种噪声点、非共圆点并给出快速确认候选圆是否为真圆的方法,尽可能减少无效计算．数值实验结果表明：文中算法能快速检测多个圆．在检测多个圆并且具有噪声的情况下,与随机圆检测算法相比,其检测速度快一个数量级．     

A Fast Efficient Parallel Hough Transform Algorithm on LARPBS

A parallel algorithm for Hough transform on a linear array with reconfigurable pipeline bus system (LARPBS) is presented. Suppose the number of -values to be considered is m, for an image with n × n pixels, the algorithm can complete Hough transform in O(1) time using mn ² processors and achieve optimal speed and efficiency. We also illustrate how to partition data and perform the algorithm on a LARPBS with fewer than mn ² processors, and hence show that the algorithm is highly scalable.     

Fast and accurate map merging for multi-robot systems

We present a new algorithm for merging occupancy grid maps produced by multiple robots exploring the same environment. The algorithm produces a set of possible transformations needed to merge two maps, i.e translations and rotations. Each transformation is weighted, thus allowing to distinguish uncertain situations, and enabling to track multiple cases when ambiguities arise. Transformations are produced extracting some spectral information from the maps. The approach is deterministic, non-iterative, and fast. The algorithm has been tested on public available datasets, as well as on maps produced by two robots concurrently exploring both indoor and outdoor environments. Throughout the experimental validation stage the technique we propose consistently merged maps exhibiting very different characteristics.

基于夹角的直线检测算法

为了能有效解决Hough变换计算量大、处理速度慢等问题,提出了一种基于夹角的直线提取算法.该算法直接在图像空间提取直线,通过判断图像中任意三点形成的直线夹角,获得一条可能的直线,然后再在数据空间中进一步判定这条直线的真实性.实验证明,该算法具有较高的直线检出率、检测精度和运行速度,与具有类似检出率的算法相比虚假直线数较少,综合性能具有优势.     

梯度估计在眼睛特征提取中的应用

眼睛特征的提取在表情分析、人脸合成和人脸识别等应用中起着非常重要的作用.目前广泛使用的眼睛特征提取算法是基于模板匹配的算法和基于特征点的算法.然而基于模板匹配的算法通常需要进行多个参数的选择,而且匹配过程非常耗时;特征点的算法则存在准确度较低的问题.针对以上问题提出了一种基于梯度信息提取虹膜中心、虹膜外轮廓和眼睛角点等眼睛特征的算法,算法用改进霍夫变换提取虹膜中心和外轮廓.在获得虹膜中心点后,参照中心位置选择眼睛角点候选区域并对其梯度进行分析.依据角点附近梯度分布与其它区域梯度分布不同的性质定位角点.仿真结果表明提出的算法能有效的提取眼睛特征,在测试数据上得到了比其它算法更高的准确率和更快的速度.     

快速霍夫变换算法

二值图像的直线检测过程中,标准霍夫变换算法的计算量为O（N^3)。该文提出一种快速霍夫变换算法,其计算量仅为O（N^2log2N)。该快速算法可以并行实现;处理器阵列规模为O（N^2)时,计算量为O（log2N)。文中还分析得到快速算法的误差上界,并提出一种改进的快速霍夫变换算法以获得更高的计算精度。最后,给出算法的数值算例。理论分析及数值算例都表明,该文的快速霍夫变换算法在直线检测过程中有着更高的计算效率,并且具有良好的计算精度。     

基于相位编组图像分块的快速Hough变换直线检测

在分析Hough变换直线检测算法和相位编组法直线检测算法的基础上,针对这两个直线检测算法的不足,结合它们的优点,设计并实现了基于相位编组图像分块的快速Hough变换直线检测算法,对算法进行了详细描述和算法优点分析,并通过实验验证了算法的有效性,实验表明所设计的直线检测算法运算速度快,参数易于选择,鲁棒性强,有一定的应用价值。     

基于改进概率霍夫变换的车道线快速检测方法

车道线是行车安全的重要参考。为提高无人驾驶行车过程中车道线检测的准确性和实时性,提出一种基于改进概率霍夫变换的车道线快速检测方法。首先对获取的图像进行感兴趣区域提取,根据车道线颜色的特殊性,合理选取三色通道的比值对图片进行灰度化,为增强阈值处理的鲁棒性,采用大津二值化法对灰度图像进行二值化,由于Canny算子具有良好的定位边缘的能力,本次边缘提取算子选取为Canny。接着分别从车道线长度、角度、车体和车道宽度4个方面提出4点约束条件对该算法加以改进,剔除干扰线和伪车道线,最后通过线性回归法拟合出正确车道线。实验结果表明,该算法在快速检测车道线的同时保证了检测的准确率,并将实验结果与其他算法进行比较,证明了该算法的实时性和准确性优于其他算法。     

A randomized approximation algorithm for computing bucket orders

We show that a simple randomized algorithm has an expected constant factor approximation guarantee for fitting bucket orders to a set of pairwise preferences.     

Deformed template matching by the Viterbi algorithm

We consider a template matching algorithm that aims to deform a given template and place it onto a target-image to match as many edges as possible. To simplify this optimization problem, we will introduce a particular class of deformations that makes the Viterbi Algorithm applicative. To be specific, we will show how to describe a deformation by a a state-sequence, and how to find the optimally deformed template by the Viterbi Algorithm.     

基于梯度的随机Hough快速圆检测方法*

针对随机Hough变换(RHT)在复杂图像中检测圆时产生随机采样的大量无效累积,提出了一种改进的RHT用于圆检测,方法利用梯度方向信息来判定是否对采样到的三点进行参数累积,从而较好地解决了无效累积问题。实验表明改进后的算法比原算法计算速度快,占用的内存小,检测性能有较大提高。     

Comparing the performance of neural networks developed by using Levenberg-Marquardt and Quasi-Newton with the gradient descent algorithm for modelling a multiple response grinding process

Monitoring and control of multiple process quality characteristics (responses) in grinding plays a critical role in precision parts manufacturing industries. Precise and accurate mathematical modelling of multiple response process behaviour holds the key for a better quality product with minimum variability in the process. Artificial neural network (ANN)-based nonlinear grinding process model using backpropagation weight adjustment algorithm (BPNN) is used extensively by researchers and practitioners. However, suitability and systematic approach to implement Levenberg-Marquardt (L-M) and Boyden, Fletcher, Goldfarb and Shanno (BFGS) update Quasi-Newton (Q-N) algorithm for modelling and control of grinding process is seldom explored. This paper provides L-M and BFGS algorithm-based BPNN models for grinding process, and verified their effectiveness by using a real life industrial situation. Based on the real life data, the performance of L-M and BFGS update Q-N are compared with an adaptive learning (A-L) and gradient descent algorithm-based BPNN model. The results clearly indicate that L-M and BFGS-based networks converge faster and can predict the nonlinear behaviour of multiple response grinding process with same level of accuracy as A-L based network.     

宽线段Hough变换及其在箭靶识别上的应用

Hough变换是用于检测图像中直线段的有力工具。论文提出的宽线段Hough变换针对传统Hough变换进行了改进,使之适用于多条宽线段同时存在的情况,并且解决了端点提取的问题。该方法应用于箭靶识别取得了很好的效果,实验表明对比传统方法具有较大优势。     

车牌识别中倾斜牌照的快速矫正算法

在车辆牌照自动识别(LPR)过程中,车牌图像的倾斜会给下一步字符切分和识别带来困难。利用传统Hough变换进行矫正的运算量很大,文中提出采用轮廓跟踪方法去除牌照图像中和矫正无关的所有冗余信息并采用分级检测的策略,在最大程度上减少矫正过程的运算量。实验结果表明,该方法可以准确地检测出车牌的倾斜角度,同时矫正速度比传统Hough变换方法提高10倍左右。     

基于级联霍夫变换的车道线快速检测算法

车道线检测是智能辅助驾驶算法中的核心算法之一。为了解决基于传统霍夫变换的车道线检测算法检测效率低下等问题,提出一种基于级联霍夫变换的快速车道线检测算法。该算法首先对视频帧进行ROI选取、滤波、边缘检测、非极大值抑制等预处理,然后使用基于平行坐标系的映射将原始图像转换到参数空间,完成点到线、线到点的映射,接着再使用一次映射,最终实现点到点、线到线的映射,以此快速提取车道线消失点,并根据消失点位置扫描实际车道线,实现车道线的提取。该算法在点的映射过程中,坐标值始终是线性变换,克服了传统霍夫变换在映射过程时需对每一个点进行极坐标转换的缺点,计算更简单,运算效率更高。仿真实验表明,文中提出的改进算法比传统霍夫变换运算速度提高了31%,准确率提高了6.2%,检测效果有明显提高,可广泛应用于智能辅助驾驶中。