立体视频编码中的目标分割与立体匹配算法

视频目标分割与立体匹配是目标基立体视频编码中的核心技术.首先在单通道视频目标分割的基础上提出一种轮廓跟踪匹配算法,以提取立体视频目标对,然后提出一种基于目标的视差估计算法.它以具有特征信息的像素为匹配基元,结合视差匹配约束进行视差估计,克服了传统块匹配算法视差场不连续、视差精度差的缺点,可以获得较为精确和平滑的视差场.     

视差互信息引导下的立体航空影像与LiDAR点云自动配准

目的 从视差图反映影像景物深度变化并与LiDAR系统距离量测信息"同源"这一认识出发,提出一种基于视差互信息的立体航空影像与LiDAR点云自动配准方法.方法 本文方法分为3个阶段:第一、通过半全局匹配SGM(semi-gdabal matching)生成立体航空影像密集视差图;第二、利用航空影像内参数及初始配准参数(外方位元素)对LiDAR点云进行"针孔"透视成像,生成与待配准的立体航空影像空间分辨率、几何形变相接近且具有相同幅面大小的模拟灰度影像-LiDAR深度影像,以互信息作为相似性测度依据估计航空影像视差图与LiDAR深度影像的几何映射关系,进而以之为基础实现LiDAR点云影像概略相关;第三、以LiDAR点云影像概略相关获得的近似同名像点为观测值,以视差互信息为权重,实施摄影测量空间后方交会计算获得优化的影像外方位元素,生成新的LiDAR深度影像并重复上述过程,直至满足给定的迭代计算条件.结果 选取重叠度约60%、幅面大小7 216×5 428像素、空间分辨率约0.5 m的立体航空像对与平均点间距约1.5 m、水平精度约25 cm的LiDAR"点云"进行空间配准实验,配准精度接近1个像素.结论 实验结果表明,本文方法自动化程度高且配准精度适中,理论上适用于不同场景类型、相机内参数已知立体航空影像,具有良好的应用价值.     

A LSS-based registration of stereo thermal–visible videos of multiple people using belief propagation

In this paper, we propose a novel stereo method for registering foreground objects in a pair of thermal and visible videos of close-range scenes. In our stereo matching, we use Local Self-Similarity (LSS) as similarity metric between thermal and visible images. In order to accurately assign disparities to depth discontinuities and occluded Region Of Interest (ROI), we have integrated color and motion cues as soft constraints in an energy minimization framework. The optimal disparity map is approximated for image ROIs using a Belief Propagation (BP) algorithm. We tested our registration method on several challenging close-range indoor video frames of multiple people at different depths, with different clothing, and different poses. We show that our global optimization algorithm significantly outperforms the existing state-of-the art method, especially for disparity assignment of occluded people at different depth in close-range surveillance scenes and for relatively large camera baseline.     

基于PatchMatch的半全局高效双目立体匹配算法

近年来双目立体匹配技术发展迅速,高精度、高分辨率、大视差的应用需求无疑对该技术的计算效率提出了更高的要求.由于传统立体匹配算法固有的计算复杂度正比于视差范围,已经难以满足高分辨率、大视差的应用场景.因此,从计算复杂度、匹配精度、匹配原理等多方面综合考虑,提出了一种基于PatchMatch的半全局双目立体匹配算法,在路径...     

Real-time stereo on GPGPU using progressive multi-resolution adaptive windows

We introduce a new GPGPU-based real-time dense stereo matching algorithm. The algorithm is based on a progressive multi-resolution pipeline which includes background modeling and dense matching with adaptive windows. For applications in which only moving objects are of interest, this approach effectively reduces the overall computation cost quite significantly, and preserves the high definition details. Running on an off-the-shelf commodity graphics card, our implementation achieves a 36 fps stereo matching on 1024 × 768 stereo video with a fine 256 pixel disparity range. This is effectively same as 7200 M disparity evaluations per second. For scenes where the static background assumption holds, our approach outperforms all published alternative algorithms in terms of the speed performance, by a large margin. We envision a number of potential applications such as real-time motion capture, as well as tracking, recognition and identification of moving objects in multi-camera networks.     

用于立体视频会议系统的立体对象分割与跟踪算法

基于对象的立体视频编码压缩技术能在立体视频会议系统中得到很好的应用，从立体视频信号中正确分割出立体视频对象是基于对象的立体视频编码压缩的一个前提条件，基于立体视频会议图像序列的时空特性和左右通道间的视差特性，提出了一种立体视频对象分割与跟踪算法，首先利用空域分割和运动检测相结合的方法，提取左通道中的运动物体；然后，提出一种左右通道间基于边缘轮廓的二级视差匹配算法，并根据已分割的左通道运动对象提取右通道的视频运动对象；最后利用对象边界轮廓的跟踪方法对后续图像中的运动对象进行快速跟踪，实验结果说明文中算法能够应用于立体视频会议图像序列的立体对象分割与跟踪。     

Genetic-Based Stereo Algorithm and Disparity Map Evaluation

In this paper, a new genetic-based stereo algorithm is presented. Our motivation is to improve the accuracy of the disparity map by removing the mismatches caused by both occlusions and false targets. In our approach, the stereo matching problem is considered as an optimization problem. The algorithm first takes advantage of multi-view stereo images to detect occlusions, and therefore, removes mismatches caused by visibility problems. By optimizing the compatibility between corresponding points and the continuity of the disparity map using a genetic algorithm, mismatches caused by false targets are removed. The quadtree structure is used to implement the multi-resolution framework. Since nodes at different level of the quadtree cover different number of pixels, selecting nodes at different levels gives a similar effect as adjusting the window size at different locations of the image. The experimental results show that our approach can generate more accurate disparity maps than two existing approaches. In addition, we introduce a new disparity map evaluation technique, which is developed based on a similar technique employed in the image segmentation area. Comparing with two existing evaluation approaches, the new technique can evaluate the disparity maps generated without additional knowledge of the scene, such as the correct depth information or novel views.     

Real-time joint disparity and disparity flow estimation on programmable graphics hardware

Disparity flow depicts the 3D motion of a scene in the disparity space of a given view and can be considered as view-dependent scene flow. A novel algorithm is presented to compute disparity maps and disparity flow maps in an integrated process. Consequently, the disparity flow maps obtained helps to enforce the temporal consistency between disparity maps of adjacent frames. The disparity maps found also provides the spatial correspondence information that can be used to cross-validate disparity flow maps of different views. Two different optimization approaches are integrated in the presented algorithm for searching optimal disparity values and disparity flows. The local winner-take-all approach runs faster, whereas the global dynamic programming based approach produces better results. All major computations are performed in the image space of the given view, leading to an efficient implementation on programmable graphics hardware. Experimental results on captured stereo sequences demonstrate the algorithm’s capability of estimating both 3D depth and 3D motion in real-time. Quantitative performance evaluation using synthetic data with ground truth is also provided.     

一种基于立体视觉的运动目标检测算法

在目标检测中采用立体视觉方法。首先对立体图像对进行匹配求取场景的视差图,再运用基于视差的背景差分法获得含有运动目标的前景区域,最后根据前景区域的视差和位置分布准确定位各运动目标。立体视觉方法有效解决了单目视觉检测方法中的一些难点问题,可以克服光线的变化和阴影干扰对目标检测带来的影响,在多个目标发生部分遮挡时仍能正确区分各运动目标。     

基于视差和SIFT的双目视觉移动目标识别和追踪的研究

开展基于视差和尺度不变特征变换(SIFT)的双目视觉移动目标识别和追踪的研究。首先采用基于梯度的立体匹配算法得到较准确的左右视图视差映射,其次通过视差映射提高基于SIFT特征的左右视图运动目标的匹配精度,最后利用视差映射和区域增长的方法相结合分别在左右视图完成运动目标的追踪。实验结果表明,基于视差信息和SIFT的双目视觉移动目标识别与追踪算法具有很好的准确性,能够在连续视频中完成左右视场中对同一物体的追踪。     

Performance evaluation of scene registration and stereo matchingfor cartographic feature extraction

Three major areas in the development of competent 3-D scene interpretation system are discussed. First, the importance of accurate automatic scene registration and the difficulty in automated extraction and matching of scene reference points are described. Second, the authors describe two stereo matching algorithms, S1, which is an area-based matcher previously used in the SPAM system, and S2, which is a feature-based matching algorithm based on hierarchical waveform matching. Third, the authors introduce several performance evaluation metrics that made it possible to measure the quality of the overall scene recovery, the building disparity estimate, and the quality and sharpness of the building delineations. Such manually generated scene reference models are critical for understanding strengths and weaknesses of various matching algorithms and in the incremental development of improvements to existing algorithms. Experiments were performed on difficult examples of aerial imagery     

基于秩空间的二次立体匹配

提出了一种基于秩空间的区域立体匹配算法。首先对立体图像进行秩（rank）变换,将图像从灰度空间变换到秩空间,消除因噪声和两个摄像机参数不一致产生的干扰,再根据自然视频序列每幅图像多数景物景深变化不大的事实,把视差分解为全局视差与局部视差之和,在秩空间进行二次立体匹配：先在最大窗口内估计全局视差,然后在这个最大窗口内采用块匹配方式进行二次匹配求各点的实际视差。该二次立体匹配算法有效地消除了误匹配,提高正确匹配率。实验结果证明,提出的算法优于传统的基于区域的立体匹配方法。     

基于模糊判别的立体匹配算法

立体视觉一直是计算机视觉领域所研究的一个中心问题,而立体匹配则是立体视觉技术中最关键也是最困难的部分,就得到适用于基于图象绘制技术中视图合成的准确、高密度视差图（Disparity Map）而言,现有的一些方法存在一定的局限性。考虑到立体匹配过程中存在的不确定性和模糊性,本文将已获得广泛应用的模糊理论引入立体匹配领域,提出了基于模糊判别的立体匹配算法,并用实际图象与合成图象进行了实验验证,结果表明该算法效果良好,具有实用价值。     

Optimizing stereo‐to‐multiview conversion for autostereoscopic displays

We present a novel stereo‐to‐multiview video conversion method for glasses‐free multiview displays. Different from previous stereo‐to‐multiview approaches, our mapping algorithm utilizes the limited depth range of autostereoscopic displays optimally and strives to preserve the scene's artistic composition and perceived depth even under strong depth compression. We first present an investigation of how perceived image quality relates to spatial frequency and disparity. The outcome of this study is utilized in a two‐step mapping algorithm, where we (i) compress the scene depth using a non‐linear global function to the depth range of an autostereoscopic display and (ii) enhance the depth gradients of salient objects to restore the perceived depth and salient scene structure. Finally, an adapted image domain warping algorithm is proposed to generate the multiview output, which enables overall disparity range extension.     

基于视差优化的立体匹配网络

现有的立体匹配算法通常采用深层卷积神经网络提取特征,对前景物体的检测更加精细,但对背景中的小物体及边缘区域匹配效果较差。为提高视差估计质量,构建一个基于视差优化的立体匹配网络CTFNet。分别提取浅层与深层特征,并基于深层特征构建全局稀疏代价卷,从而预测初始视差图。在预测的初始视差图和浅层特征的基础上构建局部稠密代价卷并进行视差优化,以细化预测视差值邻域的概率分布,提高特征不明显区域的匹配精度。此外,引入新的概率分布损失函数,监督softmax函数计算的视差值概率分布在真实视差值附近成单峰分布,提高算法的鲁棒性。实验结果表明,该网络在SceneFlow和KITTI数据集上的误匹配率分别为0.768%和1.485%,在KITTI测评网站上的误差率仅为2.20%,与PSMNet网络相比,精度和速度均得到一定提升。     

Real-time stereo using approximated joint bilateral filtering and dynamic programming

We present a stereo algorithm that is capable of estimating scene depth information with high accuracy and in real time. The key idea is to employ an adaptive cost-volume filtering stage in a dynamic programming optimization framework. The per-pixel matching costs are aggregated via a separable implementation of the bilateral filtering technique. Our separable approximation offers comparable edge-preserving filtering capability and leads to a significant reduction in computational complexity compared to the traditional 2D filter. This cost aggregation step resolves the disparity inconsistency between scanlines, which are the typical problem for conventional dynamic programming based stereo approaches. Our algorithm is driven by two design goals: real-time performance and high accuracy depth estimation. For computational efficiency, we utilize the vector processing capability and parallelism in commodity graphics hardware to speed up this aggregation process over two orders of magnitude. Over 90 million disparity evaluations per second [the number of disparity evaluations per seconds (MDE/s) corresponds to the product of the number of pixels and the disparity range and the obtained frame rate and, therefore, captures the performance of a stereo algorithm in a single number] are achieved in our current implementation. In terms of quality, quantitative evaluation using data sets with ground truth disparities shows that our approach is one of the state-of-the-art real-time stereo algorithms.     

Accurate real-time stereo correspondence using intra- and inter-scanline optimization

This paper deals with a novel stereo algorithm that can generate accurate dense disparity maps in real time. The algorithm employs an effective cross-based variable support aggregation strategy within a scanline optimization framework. Rather than matching intensities directly, the use of adaptive support aggregation allows for precisely handling the weak textured regions as well as depth discontinuities. To improve the disparity results with global reasoning, we reformulate the energy function on a tree structure over the whole 2D image area, as opposed to dynamic programming of individual scanlines. By applying both intra- and inter-scanline optimizations, the algorithm reduces the typical ’streaking’ artifact while maintaining high computational efficiency. The experimental results are evaluated on the Middlebury stereo dataset, showing that our approach is among the best for all real-time approaches. We implement the algorithm on a commodity graphics card with CUDA architecture, running at about 35 fames/s for a typical stereo pair with a resolution of 384×288 and 16 disparity levels.     

基于双序列比对算法的立体图像匹配方法*

在分析现有立体匹配方法的基础上,提出一种基于双序列比对算法的立体图像匹配方法。将立体图像对中同名极线上的像素灰度值看做是一对字符序列,使用基于动态规划思想的双序列比对算法对这些对字符序列进行匹配,以获取立体图像视差。为验证该方法的可行性和适用性,采用人脸立体图像对进行实验。实验结果表明,使用该方法进行立体图像匹配能获得光滑的、稠密的视差图。基于动态规划思想的双序列比对算法能够有效地解决立体图像匹配问题,从而为图像的立体匹配提供了一个实用有效的方法。     

垂直交叉双向搜索策略的自适应窗口匹配算法

目的 基于区域的局部匹配算法是一种简单高效的立体匹配方法.针对局部算法中窗口的抉择问题,提出了基于垂直交叉双向搜索的自适应窗口匹配算法.方法 该算法考虑到局部区域内灰度值与视差值的相关性,通过垂直交叉双向搜索策略自适应地调节窗口的形状和大小,并获得相应掩码窗口;再利用积分图像计算掩码窗口的匹配代价,获取视差图;最后采用米字投票和双边滤波器两个步骤对视差图进行修复.结果 针对不同图像采用提出的自适应窗口算法,得到了适用于各种图像结构的匹配窗口,相较于原始垂直交叉算法的匹配精度提高了约30％ (Teddy),同时两步骤视差后处理较好地保持了图像边缘.结论 实验结果表明,该算法改善了规则窗口产生的视差边缘扩充问题,在提高视差精度的同时提高了算法鲁棒性.