基于块金字塔的快速块匹配算法

在运动图象编码中，块匹配技术有着非常广泛的应用。在众多的匹配算法中，金字塔算法(blocks sum pyramid algorithm，BSPA)由于速度较快、算法比较简单而得到了广泛的应用。在金字塔算法的基础上，本文提出一种新的基于块金字塔的块匹配算法(NBSPA)。算法采用了预测初始中心，行更新等技术，实验结果表明，新的算法有效地降低了运算量。     

Super-resolution without dense flow

Super-resolution is a widely applied technique that improves the resolution of input images by software methods. Most conventional reconstruction-based super-resolution algorithms assume accurate dense optical flow fields between the input frames, and their performance degrades rapidly when the motion estimation result is not accurate enough. However, optical flow estimation is usually difficult, particularly when complicated motion is presented in real-world videos. In this paper, we explore a new way to solve this problem by using sparse feature point correspondences between the input images. The feature point correspondences, which are obtained by matching a set of feature points, are usually precise and much more robust than dense optical flow fields. This is because the feature points represent well-selected significant locations in the image, and performing matching on the feature point set is usually very accurate. In order to utilize the sparse correspondences in conventional super-resolution, we extract an adaptive support region with a reliable local flow field from each corresponding feature point pair. The normalized prior is also proposed to increase the visual consistency of the reconstructed result. Extensive experiments on real data were carried out, and results show that the proposed algorithm produces high-resolution images with better quality, particularly in the presence of large-scale or complicated motion fields.     

基于边界约束的样品视频非对称运动估计方法

针对常规运动估计方法应用到样品视频编码时存在无效搜索点冗余搜索、有效搜索点遗漏的问题,提出基于边界约束的非对称运动估计方法。首先,在原有预定搜索范围的基础上,采用定点统计运动参量的方法对科学仪器的样品视频运动性能进行测试统计,根据统计结果,对视频运动范围设定边界,减少运动搜索点数;然后提出了基于边界约束的非对称搜索模型,依据样品视频的运动特征,优化搜索算法。来自电子探针和电子显微镜的不同样品视频编码实验表明,与多方向搜索算法(MDS)比较,所提方法的运动估计时间缩短了约33%,编码性能保持甚至超过了多方向搜索算法。     

一种用于视频编码的块运动估计算法——块特征匹配预测搜索算法

本文提出了一种块特征匹配预测搜索ＢＦＭＰＳ算法,可以用于视频压缩的一些国际标准,如Ｈ．２６１,Ｈ．２６３,ＭＰＥＧ１,ＭＰＥＧ２,ＨＤＴＶ中,ＢＦＭＰＳ算法充分利用了序列图像的实际矢量与预测矢量之间距离的空间分布特征,中心偏置分布特性和时间上的相关特性,并采用了中止判决准则,可以明显地减少了运动搜索复杂度,ＢＦＭＰＳ算法在匹配运算中采用 简单有交的块特征匹配准则函数,相应的块匹配计算复杂度,数据读     

Image mosaic with relaxed motion

We propose a novel method to stitch images with relatively large roll or pitch called relaxed motion, which defies most existing mosaic algorithms. Our approach adopts a multi-resolution strategy, which combines the merits of both feature-based and intensity-based methods. The main contribution is a robust motion estimation procedure which integrates an adaptive multi-scale block matching algorithm called TV-BMA, a low contrast filter and a RANSAC motion rectification to jointly refine motion and feature matches. Based on TV ? L ¹ model, the proposed TV-BMA works on the coarsest layer to find a robust initial displacement field as the initial motion for source images. This motion estimation method can generate robust correspondences for further processing. In the subsequent camera calibration step, we also present two stable methods to estimate the camera matrix. To estimate the focal length, we combine the golden section search and the simplex method based on the angle invariance of feature vectors; to estimate the rotation matrix, we introduce a subspace trust region method, which matches features based on the rotation invariance. Extensive experiments show that our approach leads to improved accuracy and robustness for stitching images with relaxed motion.     

一种粗到精分块金字塔预测连续排除算法

提出一种粗到精分块金字塔连续排除算法用于减少块匹配运动估计所需计算量.金字塔可以方便地从当前搜索窗的积分帧构建,连续的排除从金字塔顶层到底层逐级进行,将最小SAD值与金字塔提供的一系列越来越紧的界值相比较,可以跳过不必要的SAD计算.这样,算法在保证获得与全搜索算法相同精度的前提下,计算复杂度大大降低.仿真实验结果验证了本文算法与全搜索算法和几种快速搜索算法相结合的有效性.     

Facial model adaptation from a monocular image sequence using a textured polygonal model

Although several algorithms have been proposed for facial model adaptation from image sequences, the insufficient feature set to adapt a full facial model, imperfect matching of feature points, and imprecise head motion estimation may degrade the accuracy of model adaptation. In this paper, we propose to resolve these difficulties by integrating facial model adaptation, texture mapping, and head pose estimation as cooperative and complementary processes. By using an analysis-by-synthesis approach, salient facial feature points and head profiles are reliably tracked and extracted to form a growing and more complete feature set for model adaptation. A more robust head motion estimation is achieved with the assistance of the textured facial model. The proposed scheme is performed with image sequences acquired with single uncalibrated camera and requires only little manual adjustment in the initialization setup, which proves to be a feasible approach for facial model adaptation.     

New adaptive partial distortion search using clustered pixel matching error characteristic.

In order to reduce the computation load, many conventional fast block-matching algorithms have been developed to reduce the set of possible searching points in the search window. All of these algorithms produce some quality degradation of a predicted image. Alternatively, another kind of fast block-matching algorithms which do not introduce any prediction error as compared with the full-search algorithm is to reduce the number of necessary matching evaluations for every searching point in the search window. The partial distortion search (PDS) is a well-known technique of the second kind of algorithms. In the literature, many researches tried to improve both lossy and lossless block-matching algorithms by making use of an assumption that pixels with larger gradient magnitudes have larger matching errors on average. Based on a simple analysis, it is found that, on average, pixel matching errors with similar magnitudes tend to appear in clusters for natural video sequences. By using this clustering characteristic, we propose an adaptive PDS algorithm which significantly improves the computation efficiency of the original PDS. This approach is much better than other algorithms which make use of the pixel gradients. Furthermore, the proposed algorithm is most suitable for motion estimation of both opaque and boundary macroblocks of an arbitrary-shaped object in MPEG-4 coding.     

Fast block matching algorithm based on the winner-update strategy

Block matching is a widely used method for stereo vision, visual tracking, and video compression. Many fast algorithms for block matching have been proposed in the past, but most of them do not guarantee that the match found is the globally optimal match in a search range. This paper presents a new fast algorithm based on the winner-update strategy which utilizes an ascending lower bound list of the matching error to determine the temporary winner. Two lower bound lists derived by using partial distance and by using Minkowski's inequality are described. The basic idea of the winner-update strategy is to avoid, at each search position, the costly computation of the matching error when there exists a lower bound larger than the global minimum matching error. The proposed algorithm can significantly speed up the computation of the block matching because (1) computational cost of the lower bound we use is less than that of the matching error itself; (2) an element in the ascending lower bound list will be calculated only when its preceding element has already been smaller than the minimum matching error computed so far; (3) for many search positions, only the first several lower bounds in the list need to be calculated. Our experiments have shown that, when applying to motion vector estimation for several widely-used test videos, 92% to 98% of operations can be saved while still guaranteeing the global optimality. Moreover, the proposed algorithm can be easily modified either to meet the limited time requirement or to provide an ordered list of best candidate matches.     

Fast full search motion estimation algorithm using early detection of impossible candidate vectors

To reduce the amount of computations for a full search (FS) algorithm for fast motion estimation, we propose a new and fast FS motion estimation algorithm. The computational reduction of our FS motion estimation algorithm comes from fast elimination of impossible motion vectors. We obtain faster elimination of inappropriate motion vectors using efficient matching units from localization of a complex area in image data. In this paper, we show three properties in block matching of motion estimation. We suggest two fast matching scan algorithms: one from adaptive matching scan and the other from fixed dithering order. Experimentally, we remove the unnecessary computations by about 30% with our proposed algorithm compared with the conventional fast FS algorithms.     

Fast full search motion estimation algorithm using various matchingscans in video coding

To reduce the amount of computation in a full search (FS) algorithm for fast motion estimation, we propose a novel and fast FS motion estimation algorithm. The computational reduction without any degradation in the predicted image comes from fast elimination of impossible motion vectors. We obtain faster elimination of inappropriate motion vectors using efficient matching units from localization of the complex area in image data. We show three properties in block matching of motion estimation. We suggest two fast matching scan algorithms, one from adaptive matching scan and the other from fixed dithering order. Experimentally, we remove the unnecessary computations by about 30% with our proposed algorithm compared to conventional fast matching scan algorithms     

基于矢量叠加和自适应中止的快速运动估计

提出了一种基于帧矢量叠加和自适应中止技术的快速运动估计方法.该方法在不改变搜索策略的前提下,通过采用帧矢量叠加技术,对运动矢量进行传递,等效扩大了搜索区域,显著减少了相邻帧之间对应像素最小绝对误差值的跳变,运算量减少至21.8%;引入中止判决准则,在匹配运算中,自适应的改变决定匹配次数的阈值,将运算量减少为40.2%.以全搜索块匹配算法为例的实验表明,该算法在保持精度不变的前提下,运算量最终减少到8.72%.     

基于自适应模板匹配的帧间全局运动估计算法

针对传统的匹配方法在匹配模板与待匹配图像间存在噪声影响、亮度等差异时导致匹配算法在时间和精度上得不到很好的统一,由此影响到红外图像帧间全局运动估计和补偿问题,提出了基于自适应模板匹配的方法进行帧间全局运动估计算法。该算法首先利用模板选择策略进行待匹配模板的选取,提高匹配的精度;然后提出自适应模板匹配准则,以达到较好的匹配效果,克服噪声等奇异点对误差函数值的影响;最后提出菱形搜索策略,以便搜索到最佳匹配点,使搜索不至于陷入局部最优,并提高了搜索速度。仿真实验结果表明,在红外图像背景变化较为缓慢的情况下,所提算法降低了帧间全局运动估计计算复杂度,同时具有很好的匹配精度和准确性。     

Visual attention guided video copy detection based on feature points matching with geometric-constraint measurement

In this paper, to efficiently detect video copies, focus of interests in videos is first localized based on 3D spatiotemporal visual attention modeling. Salient feature points are then detected in visual attention regions. Prior to evaluate similarity between source and target video sequences using feature points, geometric constraint measurement is employed for conducting bi-directional point matching in order to remove noisy feature points and simultaneously maintain robust feature point pairs. Consequently, video matching is transformed to frame-based time-series linear search problem. Our proposed approach achieves promising high detection rate under distinct video copy attacks and thus shows its feasibility in real-world applications.     

An efficient retinex-like brightness normalization method for coding camera flashes and strong brightness variation in videos

Conventional hybrid video coding systems rely on the assumption that the brightness is constant. This does not take inter-frame brightness variations into consideration during motion estimation and compensation processes. Under the influence of inter-frame lighting variations like camera flashes, video motion activities are not accurately estimated and the pixel prediction is poor which directly increases the bits for prediction error coding. In this paper, we propose an efficient algorithm based on the retinex-like system which allows inter-frame brightness being normalized before applying the conventional motion estimation and compensation. Experimental results show that our approach is superior to all similar approaches in the literature and demonstrate that our proposed system is very robust against the inter-frame brightness variations. Further experimental works have been done using the verification models of the MPEG-4 and the H.264 on sequences with brightness variations, results of which show that our proposed system outperforms these coding systems, including the weighted prediction feature in H.264, which were specifically designed for this purpose.     

A new block-based motion estimation algorithm

The conventional motion estimation algorithms used in digital television coding can roughly be classified into two categories, namely the block-matching method and the recursive method. Each of them has its own strong points. In this paper, a new type of block-based motion estimation algorithm is presented, which is based on the block-recursive (gradient) method and makes use of some of the merits of the block-matching method. For a moderate translational motion, motion estimation with a subpel precision can conveniently be obtained with only a couple of recursive searches, and for a violent or complicated motion which cannot be estimated by any block-based algorithm, the local minimum of prediction errors can always be found. Our experiments show that the proposed algorithm is efficient and reliable, and obviously superior to the conventional block-recursive algorithms and the fast block-matching algorithms. The performance of the proposed algorithm tends almost to the optimum of the full search algorithm with the same estimation precision, but the computational effort is much less than that of the full search algorithm.     

Efficient multilevel successive elimination algorithms for block matching motion estimation

The authors present fast algorithms to reduce the computations of block matching algorithms, for motion estimation in video coding. Efficient multilevel successive elimination algorithms are based on the multilevel successive elimination. Efficient multilevel successive elimination algorithms consist of four algorithms. The first algorithm is given by the sum of absolute difference between the sum norms of sub-blocks in a multilevel successive elimination algorithm (MSEA) using the partial distortion elimination technique. By using the first algorithm, computations of MSEA can be reduced further. In the second algorithm, the sum of absolute difference (SAD) is calculated adaptively from large value to small value according to the absolute difference values between pixels of blocks. By using the second algorithm, the partial distortion elimination in the SAD calculation can occur early, therefore the computations of MSEA can be reduced. The second algorithm is useful not only with MSEA, but also with all kinds of block matching algorithms. In the third algorithm, the elimination level of the MSEA can be estimated. Accordingly, the computations of the MSEA related to the level lower than the estimated level can be reduced. The fourth algorithm is, first of all, to search the motion vector over the half sampled search points. At the second search, the authors search the unsampled search points around the tested search points where the motion vector may exist from the first search results. The motion estimation accuracy of the fourth algorithm is nearly 100% and the computations can be greatly reduced.     

一种基于KLT-RANSAC全局运动估计的电子稳像算法研究

全局运动估计是电子稳像的核心,很大程度上影响整个稳像系统的性能.提出了一种基于KLT-RANSAC全局运动估计的电子稳像算法,该方法首先通过增加距离约束机制,并进行亚像素级定位,优化Harris特征点,选择KLT快速匹配算法实现特征点的粗匹配,之后引入了RANSAC算法剔除误匹配点,去除视频序列中分布在运动物体上的特征点,避免了局部运动对全局运动估计的影响.实验结果表明:本算法可以有效解决特征点匹配过程中的误匹配问题,避免局部运动对稳像的干扰,快速准确地计算出全局运动参数,最终可以实时处理存在平移和旋转抖动的视频,输出稳定高质量的视频.     

基于Hession优化梯度滤波的全局航拍视频配准

提出一种基于优化梯度滤波的全局航拍视频配准算法。该方法首先提出一种基于优化梯度滤波的Hession检测器,以实现特征点的精确定位,同时,为了保证在不同摄像机焦距下获得相同的特征点,保留3个分辨率下具有恒定不变的特征点。然后利用最小生成树方法对待配准点进行初始匹配。一致特征点建立后,通过利用非线性最小二乘(NLLS)和随机采样一致性(RANSAC)算法选取具有全局最小误差的变换参数对视频帧间实现配准。实验结果表明,通过利用优化梯度滤波和全局最优模型估计可实现帧间的精确配准,对不同动态场景和光照变换具有较强的适应性。     

单目图像序列中基于正则化的局部三维非刚体运动估计

通过局部的三维非刚体运动估计进而达到全局估计结果是三维非刚体运动估计中的重要方法。本文提出了在单目图像序列中利用正则化的手段解决局部的三维非刚体运动估计。首先，在帧间特征点匹配已确立的前提下，利用仿射运动模型并结合中心投影方式提出了运动估计的最小二乘模型；然后针对三维运动估计的不适定性提出了正则化的运动估计方法，以正则化的形式融入运动的先验知识，使运动估计的结果更具鲁棒性；最后利用Levenberg-Marquart方法实现运动参数的求解。仿真图像序列的实验反映了本文方法的有效性。