基于网格状模板的实时图像跟踪算法

传统相关跟踪算法存在运算量大和实时性差的缺陷.针对这一问题,提出了一种基于网格状模板的实时图像跟踪算法.该算法首先从减少参与匹配运算的像素数目的角度出发,剔除部分冗余像素,将匹配模板设置为网格状.然后在成像跟踪仿真实验平台下,用归一化互相关匹配方法实现了对运动目标的跟踪.仿真实验结果表明,该算法不仅在很大程度上减少了图像匹配的计算量,提高了跟踪实时性,而且跟踪快速准确,具有一定的实用价值.     

基于互相关边界特性和图像积分的快速模板匹配算法

基于归一化算法求解相似度原理,提出了综合利用互相关的边界条件和图像积分计算相似度的快速算法,在不降低匹配精度的前提下较大地提高了匹配速度。计算相似度时,归一化算法需要计算各位置的自相关值和互相关值,本算法先只计算自相关值,再利用Holder不等式原理,结合给定的边界阈值,剔除不满足条件的位置,减少其对应的互相关值计算。应用图像的积分进行匹配在于整个图像的积分可以在匹配之前进行计算,而在匹配过程中每一个子区域的自相关可以通过图像积分快速求得。本算法已在焊线机芯片识别系统中应用,结果表明该算法匹配的速度快而又不降低匹配精度,具有实际应用价值。     

基于主成分分析的快速图像匹配研究

针对传统图像灰度匹配算法计算量较大的问题,根据特征匹配算法抗干扰性好、速度快的特点,提出一种改进的融合匹配算法。通过实验计算,匹配时间减少到单一使用归一化互相关灰度匹配的60%,甚至更短,满足了快速、准确的图像匹配要求。     

SMD缺陷检测中快速图像匹配算法研究

传统灰度归一化互相关匹配算法存在匹配时间太长及不适合旋转情况匹配的缺点.提出改进的层叠二分互相关匹配算法,通过将图像多次缩小一半后进行粗匹配,再依次返回到缩小之前的图像上进行小范围的精匹配来减小匹配时间;并提出旋转搜索窗口的方法来使算法适合旋转的匹配;通过使用增量法计算互相关值来进一步减小匹配时间;最后研究了改进的算法在SMD缺陷检测中的应用并进行实验测试,结果表明算法能够快速准确检测元件的位置偏移和角度偏转.     

归一化互相关灰度图像匹配的多核信号处理器实现

为了满足图像处理对处理器性能的高要求,以基于灰度的归一化互相关(NCC)匹配算法为例,采用高性能、低功耗的多核数字信号处理器(DSP)系统,根据归一化互相关算法中模板图像在源图像中逐个像素搜索并计算相关性的特点,将搜索区域分成六个部分并使TMS320C6472的六个核并行搜索计算这六个区域,并在不同图像存储位置采用不同图像和模板大小实现了多核DSP归一化互相关图像匹配算法。实验结果表明,多核DSP具有作为数字信号处理器的高速信号和图像处理的特点,同时可以根据不同算法通过核间任务分配实现多核并行处理。对于归一化互相关灰度图像匹配算法,TMS320C6472六核DSP和单核DSP比较获得接近单核DSP六倍的性能,对于较大尺寸的图像和PC相比也具有一定的性能加速。     

基于奇异值分解结合Harris的快速匹配方法研究

针对室内环境下视觉图像匹配速度慢、精度低等问题,提出一种基于奇异值分解结合Harris的快速匹配新方法.随机采集两组相邻的视觉图像作为研究对象,利用奇异值分解(SVD)对视觉图像进行压缩与重构.利用Harris角点检测算法对重构后的视觉图像进行特征角点的检测,然后结合归一化互相关(NCC)算法对视觉图像的特征角点进行一次粗匹配,最后采用随机抽样一致性(RANSAC)方法对粗匹配结果进行校正,实现特征点对的精匹配.实验表明:与传统的归一化互相关模板匹配算法相比,该算法不仅将视觉图像在室内环境下的误匹配率降低至2.35％,而且图像匹配的速率提升了3倍.     

边缘增强图像互相关模板匹配的并行架构

基于归一化互相关测度(NCC)的模板匹配已经在图像处理领域得到了广泛的应用。对图像进行边缘检测然后进行模板匹配,可充分利用图像的空间相关性,锐化模板匹配结果的相关峰,提高匹配的准确度,可以获得更高的定位精度。为了有效提高定位精度,考虑到导弹制导系统的算法实时性、体积以及为适应战场不同任务阶段采用不同匹配策略的灵活性要求,基于FPGA,通过结合Sobel边缘检测,进一步改进了提出的图像归一化互相关模板匹配高速并行实现架构。对边缘检测前后图像模板匹配的仿真比较结果表明,边缘检测处理可有效锐化相关峰;基于Altera的FPGA芯片EP2S90和开发软件Quartus Ⅱ 8.0的并行实现架构功能与时序仿真及在实际目标识别系统中的应用表明,这种方案可有效地提高系统的运算速度和定位精度,FPGA实现本身也进一步缩小了系统的体积。     

基于广义掩膜积分图像的快速模板匹配

目标检测中模板匹配常需用掩膜排除待匹配窗口中的一些不相关区域.模板匹配的相关算法计算过程十分耗时,但由于掩膜的区域往往不规则,适用的快速算法较少.提出广义掩膜积分图像,实现了一种有掩膜的快速模板匹配算法.该算法不仅只需遍历待匹配窗口和模板的像素一次,而且在不改变匹配精度的同时简化了计算过程.人脸检测实验证明,该算法能显著提高有掩膜的模板匹配计算效率,特别适于需对目标和模板进行整体与局部多次匹配的应用场合.     

基于Harris角点匹配的工具识别方法

图像匹配对于工具识别过程具有重要意义,传统的图像匹配算法匹配过程复杂导致匹配速度慢,在实际应用中有局限。为了提高工具识别的效率,通过分析工具图像特征,设计出一种基于Harris角点特征的图像匹配方法进行工具识别。首先用Harris算法提取工具模板图像与搜索图像角点特征,然后使用归一化互相关匹配算法计算工具模板图像与搜索图像角点特征的相关值,进而确定匹配点对,最后采用RANSAC去除错误匹配点对。通过实际数据验证,基于Harris角点特征的图像匹配方法与传统方法相比,不仅识别速度快,鲁棒性好,而且在实际工程应用中更具适用性。     

景象匹配算法在多DSP系统中的并行实现

灰度互相关匹配是一种有效的景象匹配算法,其具有适应性强和匹配精度高的特点。但互相关匹配的计算量很大,在实时性要求很高的精确末制导中,匹配速度往往很难满足要求。基于空间并行方法实现了互相关匹配算法的并行处理,通过不同数量处理器的并行处理实验,结果表明:空间并行方法具有很高的加速比和并行效率,是一种适合互相关匹配算法的有效的并行处理方法。当图像尺寸增大计算量增加时,可以采取增加处理器提高系统处理能力的办法使算法依然满足实时性要求。     

一种图像匹配中SSD和NCC算法的改进

论文针对图像匹配计算中的SSD和NCC算法提出一种基于递推增量计算的改进算法。分析和利用计算图像每个像素的SSD和NCC值时的模板在水平方向和竖直方向上具有的平移特性,以及前后上下像素模板的相互关系,利用已计算的值,来计算新的像素点的SSD和NCC值。定量分析和仿真结果显示,该算法能够有效地降低计算量。另外由于只是从计算方法上对这两种算法进行了改进,并未改变其原理,所以该方法能够保证SSD和NCC算法在立体匹配计算时本身的特性没有改变。     

一种快速的具有旋转不变性的模板匹配方法

传统的基于相关的匹配方法计算量相当大,而且当模板相对于搜索图有角度旋转时,匹配的计算量更大。用圆警影和zemike矩的方法对快速的且具有旋转不变性的模板匹配方法进行了研究,圆投影将图像由二维变换成一维,这样就降低了计算复杂度,通过相似性度量可进行快速地粗匹配,然后在可能的匹配点中,再用Zemike矩实现精匹配。     

A novel evolutionary algorithm inspired by the states of matter for template matching

Template matching (TM) plays an important role in several image processing applications such as feature tracking, object recognition, stereo matching and remote sensing. The TM approach seeks the best possible resemblance between a sub-image, known as template, and its coincident region within a source image. TM has two critical aspects: similarity measurement and search strategy. The simplest available TM method finds the best possible coincidence between the images through an exhaustive computation of the Normalized Cross-Correlation (NCC) value (similarity measurement) for all elements in the source image (search strategy). Unfortunately, the use of such approach is strongly restricted since the NCC evaluation is a computationally expensive operation. Recently, several TM algorithms that are based on evolutionary approaches, have been proposed to reduce the number of NCC operations by calculating only a subset of search locations. In this paper, a new algorithm based on the states of matter phenomenon is proposed to reduce the number of search locations in the TM process. In the proposed approach, individuals emulate molecules that experiment state transitions which represent different exploration–exploitation levels. In the algorithm, the computation of search locations is drastically reduced by incorporating a fitness calculation strategy which indicates when it is feasible to calculate or to only estimate the NCC value for new search locations. Conducted simulations show that the proposed method achieves the best balance in comparison to other TM algorithms considering the estimation accuracy and the computational cost.     

Template matching using an improved electromagnetism-like algorithm

Template matching (TM) plays an important role in several image-processing applications such as feature tracking, object recognition, stereo matching, and remote sensing. The TM approach seeks for the best-possible resemblance between a subimage known as template and its coincident region within a source image. TM involves two critical aspects: similarity measurement and search strategy. The simplest available TM method aims for the best-possible coincidence between the images through an exhaustive computation of the normalized cross-correlation (NCC) values (similarity measurement) for all elements of the source image (search strategy). Recently, several TM algorithms that are based on evolutionary approaches have been proposed to reduce the number of NCC operations by calculating only a subset of search locations. In this paper, a new algorithm based on the electromagnetism-like algorithm (EMO) is proposed to reduce the number of search locations in the TM process. The algorithm uses an enhanced EMO version, which incorporates a modification of the local search procedure to accelerate the exploitation process. As a result, the new EMO algorithm can substantially reduce the number of fitness function evaluations while preserving the good search capabilities of the original EMO. In the proposed approach, particles represent search locations, which move throughout the positions of the source image. The NCC coefficient, considered as the fitness value (charge extent), evaluates the matching quality presented between the template image and the coincident region of the source image, for a determined search position (particle). The number of NCC evaluations is also reduced by considering a memory, which stores the NCC values previously visited to avoid the re-evaluation of the same search locations (particles). Guided by the fitness values (NCC coefficients), the set of candidate positions are evolved through EMO operators until the best-possible resemblance is determined. The conducted simulations show that the proposed method achieves the best balance over other TM algorithms in terms of estimation accuracy and computational cost.     

快速注意力选择计算及其在图像质量评价中的应用

针对现有的模拟视觉注意力选择模型计算复杂度高、参数选取困难的问题,提出一种快速的频率域算法.将各频点上的幅度设为同一常数,保留其相位信息;对反变换到空间域的图像滤波后,得到视觉注意力显著图,其效果与现有的模型相似;并将显著图信息作为权重加入到几种主要的图像质量评价标准中.实验结果表明:带有注意力选择的图像质量评价标准更接近于人的主观评价,便于实时应用.     

Fast normalized cross correlation for motion tracking using basis functions

Digital image-based elasto-tomography (DIET) is an emerging method for non-invasive breast cancer screening. Effective clinical application of the DIET system requires highly accurate motion tracking of the surface of an actuated breast with minimal computation. Normalized cross correlation (NCC) is the most robust correlation measure for determining similarity between points in two or more images providing an accurate foundation for motion tracking. However, even using fast Fourier transform (FFT) methods, it is too computationally intense for rapidly managing several large images. A significantly faster method of calculating the NCC is presented that uses rectangular approximations in place of randomly placed landmark points or the natural marks on the breast. These approximations serve as an optimal set of basis functions that are automatically detected, dramatically reducing computational requirements. To prove the concept, the method is shown to be 37-150 times faster than the FFT-based NCC with the same accuracy for simulated data, a visco-elastic breast phantom experiment and human skin. Clinically, this approach enables thousands of randomly placed points to be rapidly and accurately tracked providing high resolution for the DIET system.     

A real-time two-stage and dual-check template matching algorithm based on normalized cross-correlation for industrial vision positioning

Pattern Analysis and Applications - In this paper, a fast template matching algorithm of two-stage and dual-check bounded partial correlation (TDBPC) based on normalized cross-correlation (NCC) of...     

利用多基数系统的高效椭圆曲线多标量乘算法

针对椭圆曲线密码体制中标量乘与多标量乘运算耗时过长的问题,设计以2、3、7为基元的多基整数表示方法,并结合多基数系统(MBNS)及滑动窗口算法,提出基于MBNS滑动窗口(Sliding MBNS)和交错MBNS滑动窗口(I-MBNS)的多标量乘快速算法,分析并比较两种多标量乘快速算法在二元域和素域及不同窗口宽度下的平均...     

Isogeometric computation reuse method for complex objects with topology-consistent volumetric parameterization

Volumetric spline parameterization and computational efficiency are two main challenges in isogeometric analysis (IGA). To tackle this problem, we propose a framework of computation reuse in IGA on a set of three-dimensional models with similar semantic features. Given a template domain, B-spline based consistent volumetric parameterization is first constructed for a set of models with similar semantic features. An efficient quadrature-free method is investigated in our framework to compute the entries of stiffness matrix by Bézier extraction and polynomial approximation. In our approach, evaluation on the stiffness matrix and imposition of the boundary conditions can be pre-computed and reused during IGA on a set of CAD models. Examples with complex geometry are presented to show the effectiveness of our methods, and efficiency similar to the computation in linear finite element analysis can be achieved for IGA taken on a set of models.     

二维Otsu自适应阈值选取算法的快速实现

Otsu 自适应阈值算法作为图像阈值分割的经典算法, 在图像领域得到了广泛的应用, 在此基础上发展起来的二维阈值法因为计算时间长而制约了其应用. 针对二维 Otsu 自适应阈值算法计算复杂度高的缺点, 通过消除二维自适应阈值算法中的冗余计算, 用迭代的方式得到查询表, 从而大大提高了二维阈值算法的计算速度. 实验结果表明, 该算法不仅计算时间远远小于原始二维 Otsu 算法, 并且求得的阈值跟原始的算法一样.