一种用于MPEG-4形状编码的快速运动估计算法

提出了一种新的形状快速运动估计算法,用于加快MPEG-4视频对象的形状编码过程。该算法充分利用了邻近形状块的运动向量之间的相关性、边界形状块的匹配特性。与MPEG-4验证模型中的形状运动估计算法以及现有的改进算法相比,该算法大大提高了形状运动估计的速度,而形状的编码效率并没有降低。     

块匹配与光流技术在微结构动态特性检测中的应用

在微结构动态特性的检测中引入块匹配技术和光流技术.结合微结构的运动特性,分别提出了基于块匹配技术和基于光流技术的两种微结构二维运动估计算法.利用这两种二维运动估计算法对平面微谐振器做动态特性分析,均取得了较好的测量结果.对这两种二维运动估计算法在微谐振器动态特性中的测量应用做比较,结果表明:块匹配算法适合简单的平移运动测量,计算量小,但它要求被测器件在结构上有较为特殊或显著的运动特征区域;光流算法通过分析单个像素的灰度分布特性来测量被测器件的运动特性.与块匹配法相比,它并不要求被测器件有较为特殊的运动特征结构.由于光流法以单个像素为计算单位,且其中包含有较多的差分计算,计算过程较为复杂,因此测量分析所需的时间长.     

波导不变量和航向角联合估计方法研究

波导不变量是一个与环境特性和传播特性有关的物理量,对其准确估计有重要意义.结合LOFAR图和方位-时间历程图,提出了利用Hough变换进行波导不变量和目标航向角联合估计算法,该算法无需知道运动目标的距离信息,不要求目标有最近通过距离.提出了综合Hough变换参数估计算法,较提取单条纹参数估计算法有更好的稳健性,但增加了计算开销.仿真研究和海试数据分析均验证了算法的可行性,并表明该参数估计算法有较高的估计精度.     

一种用于AVS视频编码的块运动估计算法

通过对现有的视频图像编码中的帧间预测技术所用的运动估计算法的分析,提出了一种适用于AVS视频编码标准的快速而有效的块运动估计算法,该算法采用5个检测点的大菱形和9个检测点的小田字形双模板结构,通过调用大菱形模板和小田字形模板进行块运动匹配搜索,随着搜索的进行,大菱形模板可以被循环调用,但约定小田字形模板仅被调用一次.通过与全搜索算法和菱形搜索算法比较,这种新的块运动估计算法在保证编码性能几乎不变的情况下,编码复杂性能够得到有效的降低.     

基于半八边形模板的运动估计算法?

H.264视频编码标准中,运动估计占用了编码器极大的运算量.针对 H.264快速块匹配,本文提出了一种基于半八边形搜索模板的运动估计算法.该方法使用一个十字形模板来预测搜索方向,依据绝对差和(Sum of Absolute Differences, SAD)分布的方向性自适应地选取半八边形模板进行搜索,在搜索过程中,判断 SAD 值下降最快的方向精确定位匹配点,减少搜索点之间的冗余,提高搜索速度.理论分析表明该算法同经典六边形算法相比可以减少搜索点数.实验结果显示,在重建图像质量接近全搜索的同时,新算法减少了运动估计20%–30%的运算量.     

面向运动估计器硬件实现的加权块匹配检测算法

在各种视频处理算法中，运动补偿型算法大大提高了各种视频处理效果。运动估计器的硬件实现是各种运动补偿视频处理算法在实际系统中运用的关键。由于块匹配运动估计算法较低的运算复杂度和硬件实现难度以及块匹配检测标准函数很高的调用频率，已经广泛应用到各种实际系统中。文中提出了加权最小最大误差的匹配检测标准，能够降低运动估计器的运算复杂度，减少估计器的硬件面积，提高硬件速度，而且能够降低递归搜索块匹配运动估计算法固有误差传递带来的负面影响。     

用于电话语音识别系统的置信度估计方法的研究

提出了一种用于电话语音识别系统的置信度快速估计算法,此算法是在语音识别器帧同步束搜索的过程中基于状态图的同步估计算法,使用同识别器解码相同的声学模型进行置信度估计,此算法取得了比传统的两遍解码估计置信度算法更好的性能,而且计算复杂度较低,运行速度快,解决了计算置信度时使用模型的区分度与计算速度之间的矛盾。     

基于HUBER函数的序列图像位移场估计算法

提出了一种基于Huber函数的序列图像位移场估计算法，与光流法不同的是，它能直接计算位移场，还能在一定程度上减轻对于变化剧烈的“运动边界”过渡平滑的问题，该算法采用共轭梯度法优化能量方程，并利用小波分解进行分级计算，能快速可靠地得到稳定的位移场，实验表明，对有较大弹性形变的图像，与块匹配法相比，该算法得到的平均匹配残差明显减少。     

一种k/n卷积码参数盲估计的鲁棒性方法

针对信道编码参数的盲估计问题,提出了一种k/n卷积码参数盲估计的鲁棒性方法.首先,将卷积码的参数估计问题转换为求解二元域上线性方程组的问题,然后,应用Walsh-Hadamard变换对该问题求解,设计了卷积码编码参数的估计算法,并对算法中的门限选取进行了分析.仿真实验表明,相较于现有的算法,本文提出的方法具有更好的抗噪性能,在信道传输的错误率小于等于0.07时,本文方法对不同编码器参数的识别正确率均达到了95%以上.     

利用帧间差探测函数模型的运动目标跟踪算法

针对基于模板匹配的目标跟踪算法中,匹配环节在算法中耗费大量的处理时间,使实时性较差的问题,提出了一种基于帧间差探测函数模型的运动目标跟踪算法.算法将"虚拟边缘"引入目标运动事件中,建立了帧间差探测函数模型,设计了几种新的模块用于解决目标跟踪中的方向纠错和实时性问题.实验结果表明,不仅可以有效地对在复杂背景下运动的小目标进行跟踪,而且能够获得相比于块匹配法更好的实时性能.     

A probabilistic approach for video stabilisation in compressed domain

Machine vision systems, which are being extensively used for intelligent transportation applications, such as traffic monitoring and automatic navigation, suffer from image instability caused by environment unstable conditions. On the other hand, by increasing the use of home video cameras which sometimes need to remove unwanted camera movement, which is created by cameraman shaking hands, video stabilisation algorithms are being considered. The video stabilisation process consists of three essential phases: global motion estimation, intentional motion estimation and motion compensation. Motion estimation process is the main time consuming part of global motion estimation phase. Using motion vectors extracted directly from MPEG compressed video, instead of any other special feature, can increase the algorithm generality. In addition, it provides the facility for integrating video stabilisation and video compression subsystems and removing the block matching phase from video stabilisation procedure. Elimination of any iterative outlier removal preprocessing and adaptive selection of motion vectors has increased speed of the algorithm. Although deterministic approaches are faster than the related probabilistic methods, they have essential problems in escaping from local optima. For this purpose, particle filters, the ability of which is considerable when submitted to non-linear systems with non-Gaussian noises, are utilised. Setting the parameters of the particle filter using a fuzzy control system reduces the incorrect intentional camera motion removal. The proposed method is simulated and applied to video stabilisation problem and its high performance on various video sequences is demonstrated.     

基于特征光流的角点匹配快速算法

为了解决视频序列点匹配过程中的实时性和误匹配问题,本文提出了一种基于特征光流技术的快速角点匹配方法。该方法通过多分辨率策略,求出特征光流场概略运动矢量,然后依据匹配准则做相应特征点匹配处理,得到的精确运动矢量用作下一步的光流场计算。这样,既消除了误匹配点,又克服了光流计算的迭代负担。实验结果表明,算法正确匹配率达到98%以上,平均处理帧率24,能够满足系统实时性要求。     

Efficient architecture for global elimination algorithm for H.264 motion estimation

This paper presents a fast block matching motion esti mation algorithm and its architecture. The proposed architecture is based on Global Elimination (GE) Algorithm, which uses pixel averaging to reduce complexity of motion search while keeping performance close to that of full search. GE uses a preprocessing stage which can skip unnecessary Sum Absolute Difference (SAD) calculations by comparing minimum SAD with sub-sampled SAD (SSAD). In the second stage SAD is computed at roughly matched candidate positions. GE algorithm uses fixed sub-block sizes and shapes to compute SSAD values in preprocessing stage. Complexity of this GE algorithm is further reduced by adaptively changing the sub-block sizes depending on the macro-block features. In this paper adaptive Global Elimination algorithm has been implemented which reduces the computational complexity of motion estimation algorithm and thus resulted in low power dissipation. Proposed architecture achieved 60% less number of computations compared to existing full search architecture and 50% high throughput compared to existing fixed Global Elimination Architecture.     

分块特征匹配与局部差分结合的运动目标检测

针对摄像机在运动情况下运动目标检测问题的研究,提出了一种尺度不变特征变换图像分块特征匹配和局部差分算法相结合的运动目标检测方法。该方法不仅可以减少图像配准参数求解所需要的时间,弥补全局运动补偿对图像完整性的影响,并且可以在摄像机运动的情况下准确地检测出运动目标。 实验证明了该方案的有效性。     

Adaptive Order Cross–Square–Hexagonal search and fuzzy tangential-weighted trade-off for H.264 in motion estimation

In recent years, various algorithms have been proposed to attain low computational complexity in motion estimation of the image sequence coding based on block matching. This paper presents an Adaptive Order Cross–Square–Hexagon (AOCSH) search algorithm, which employs a smaller cross-shaped pattern before the first step of a square pattern and replaces the square-shaped pattern with the hexagon search patterns in subsequent steps. The proposed search patterns aid in finding the best matching block, without much consideration of the vast number of search points. Here, fuzzy tangent-weighted function is also proposed to evaluate the matching points using the rate and the distortion parameters. The proposed methods are effectively applied to the block estimation process to handle the objectives of visual quality and distortion. The performance of the proposed AOCSH approach is compared to the existing methods, such as AOSH, H.264 and elastic models, using the structural similarity index (SSIM) and the peak signal to noise ratio (PSNR). From the analysis, it can be seen that the proposed approach attains the maximum SSIM of 0.99 and maximum PSNR of 40. 92 dB with reduced computation time of 3.28 s.     

A new fast block matching algorithm based on complexity‐distortion optimization

Most fast block matching algorithms ignore the efficiency in motion compensation within each checking step. In order to achieve better‐compensated performance, the limited computational complexity should be allocated more carefully into each block. It means that the fast block matching algorithm can be viewed as a kind of rate‐distortion optimization problem. The complexity‐distortion optimal fast block matching algorithm should find the maximized quality of the compensated image under a target computational complexity. In order to approach the optimal complexity‐distortion solution, some strategies are developed. For example, a domination‐based motion vector prediction technique is developed to set the initial motion vector for each block. A predictive complexity‐distortion benefit list is established to predict the compensated benefit for each block. Also, a three‐level pattern searching is employed to check the candidate motion vector. Experimental results show that our proposed algorithm outperforms significantly the three‐step search. For example, in “Salesman,” the average checkpoints for one block is 33 by using the three‐step search. The average checkpoint is 1.75 by using our proposal algorithm under the same average PSNR condition. © 2002 Wiley Periodicals, Inc. Int J Imaging Syst Technol 12, 63–67, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.10012     

基于LMedS估计的快速稳像方法

为了提高数字稳像的快速性和鲁棒性,研究了一种基于LMedS 估计的图像稳定方法.在图像的运动估计中,提出了预判局部模块的算法:为避免误匹配,在计算运动矢量前对模块的梯度信息进行预分析,具有独特纹理特性的模块才被选用,通过减少参与计算的模块数目提高处理速度;采用改进的快速序贯相似性算法(SSDA)进行块匹配,提高运动矢量的计算速度;采用LMedS 估计法去除不精确的运动向量,然后用最小二乘法得到全局运动模型的参数.仿真结果表明该方法在干扰下能保持1/4 像素以内的稳像精度,一次稳像时间小于7 ms .     

基于视差空间运动估计的高精度立体视觉定位

立体视觉定位算法的运动估计通常在3D欧式空间中进行,但由于特征点3D坐标的噪声各向异性且分布不均匀,3D重建在深度方向上比另两个方向上的准确性差,从而导致3D欧式空间运动估计精确不高.本文提出了一种新的基于视差空间运动估计的高精度立体视觉定位算法.算法首先采用视差空间4点闭环线性解法和RANSAC算法得到初始鲁棒运动估计和匹配内点.接着,利用新的视差空间再投影误差函数提出了基于LM算法的视差空间运动参数非线性优化方法,对初始运动参数进一步优化.视差空间噪声分布均匀且各向同性,本文的初始运动参数线性估计和非线性优化都在视差空间中进行且能达到全局最小.仿真实验和真实实验结果表明,本文算法能得到高精度的立体视觉定位结果,优于传统的3D欧式空间运动估计方法.     

Classified variable-block-size motion estimation algorithm for image sequence coding

In recent years, the variable-block-size (VBS) motion estimation technique has been widely employed to improve the performance of the block-matching algorithm (BMA). In VBS, the block size is varied according to the type of motion. The VBS is known to be very efficient for areas containing complex motions. However, it requires a large number of computations. In this article, a new VBS motion estimation algorithm, called the classified variable block size (CVBS), is proposed to overcome this problem. The algorithm classifies the input blocks into three categories: background, shade motion and edge motion. According to the characteristics of the classified blocks, various motion estimation techniques are then used to improve the coding performance. The performance of VBS and CVBS based on the coding efficiency is investigated. It is shown that the CVBS algorithm requires about one fifth to one seventh of the computations needed by the conventional VBS algorithm. © 1996 John Wiley & Sons, Inc.