基于立体匹配的低纹理图像重构算法

针对立体匹配中低纹理区域容易产生误匹配及传统动态规划固有的条纹问题,提出一种改进的基于双目立体视觉的低纹理图像三维重构算法。该算法首先基于像素间相似度和像素自身特异性计算匹配代价并引入一种自适应多边形支撑区域聚集匹配度。然后采用一种全局意义的简单树形动态规划进行逐点匹配。最后基于左右一致性准则运用一种简单有效的视差校正方法消除误匹配得到最终视差图。实验证明将算法运用于实拍低纹理灰度图像的匹配,得到轮廓光滑清晰的三维点云,说明该方法的适用性。     

基于图像区域分割和置信传播的立体匹配算法

传统基于像素的立体匹配算法误匹配率较高.为解决该问题,提出一种基于图像区域分割和置信传播的匹配算法.采用均值偏移对参考图像进行区域分割,通过自适应权值匹配计算初始视差图,对各分割区域的初始视差用平面模型拟合得到视差平面参数,使用基于区域的改进置信传播算法求得各区域的最优视差平面,从而得到最终视差图.与全局优化的经典置信传播算法和图割算法的对比实验结果表明,该算法能降低低纹理区域和遮挡区域的误匹配率.     

融合多特征表示和超像素优化的双目立体匹配

针对目前许多局部双目立体匹配方法在缺乏纹理区域、遮挡区域、深度不连续区域匹配精度低的问题,提出了基于多特征表示和超像素优化的立体匹配算法。通过在代价计算步骤中加入边缘信息特征,与图像局部信息代价相融合,增加了在视差计算时边缘区域的辨识度;在代价聚合步骤,基于超像素分割形成的超像素区域,利用米字骨架自适应搜索,得到聚合区域,对初始代价进行聚合;在视差精化步骤利用超像素分割信息,对匹配错误视差进行修正,提高匹配精度。基于Middlebury立体视觉数据集测试平台,与自适应权重AD-Census、FA等方法得出的视差图进行比较,该算法在深度不连续区域和缺乏纹理区域的匹配效果显著改善,提高了立体匹配精度。     

一种应用于双目移动机器人目标跟踪的新型立体匹配算法

针对移动机器人目标跟踪对立体匹配准确性和实时性的要求,提出了一种基于平行配置系统的改进WTA算法;首先提取图像的边缘点和两幅视图间存在较大差异的点作为特征点;然后对特征点采用WTA算法进行立体匹配,而对非特征点仅进行简单的验证,其视差值为邻近像素的视差值;最后得到致密的视差图;该算法提取的特征点集中于视差不连续区域,实验结果表明该算法匹配精度与现有其它算法相当,但计算速度很好地满足了实时性的要求,并且边缘特性较好,是一种匹配准确、实时性好的立体匹配算法。     

双目立体视觉的三维人脸重建方法

创建逼真的三维人脸模型始终是一个极具挑战性的课题.随着三维人脸模型在虚拟现实、视频监控、三维动画、人脸识别等领域的广泛应用,三维人脸重建成为计算机图像学和计算机视觉领域的一个研究热点.针对这一问题,提出一种基于双目立体视觉的三维人脸重建方法,重建过程中无需三维激光扫描仪和通用人脸模型.首先利用标定的2台摄像机获取人脸正面图像对,通过图像校正使图像对的极线对齐并且补偿摄像机镜头的畸变;在立体匹配方面,选择具有准确可靠视差的人脸边缘特征点作为种子像素,以种子像素的视差作为区域生长的视差,在外极线约束、单调性约束以及对应匹配的边缘特征点的约束下,进行水平扫描线上的区域生长,从而得到整个人脸区域的视差图,提高了对应点匹配的速度和准确度;最后,根据摄像机标定结果和立体匹配生成的视差图计算人脸空间散乱点的三维坐标,对人脸的三维点云进行三角剖分、网格细分和光顺处理.实验结果表明,该方法能够生成光滑、逼真的三维人脸模型,证明了该算法的有效性.     

基于分割导向滤波的亚像素精度立体匹配视差优化算法*

针对局部立体匹配中存在的弱纹理区域匹配精度较低、斜面等区域容易产生视差阶梯效应等问题,文中提出基于分割导向滤波的视差优化算法,以获得亚像素级高精度匹配视差.首先依据左右一致性准则对立体匹配的初始视差进行误匹配检验及均值滤波修正.然后在修正视差图上确定区域分割导向图,对修正视差进行区域导向滤波优化,获得亚像素级高精度的视差结果.实验表明,文中算法能有效改善斜面等区域的视差不平滑现象,降低初始视差的误匹配率,获得较高精度的稠密视差结果.     

熵率超像素分割一致性检验视差细化算法

针对传统分割一致性检验视差细化算法处理低纹理图片时优化效果较差的问题,提出一种基于熵率超像素分割的改进方法,使用基于熵率的超像素分割算法代替均值漂移(Mean-shift)分割算法.针对参考图像进行超像素分割处理;将每一个分割块进行统计分析,根据集中趋势值筛选可信值与不可信值;进行视差填充处理获得最终优化后的视差图.选...     

基于改进的种子点扩展自适应立体匹配算法

由于在图像中不同区域的纹理密集程度不同,因此使用固定窗口大小的算法无法兼顾纹理不同的区域,并且在视差不连续区域的匹配精度较低。针对该问题,提出一种自适应窗口和自适应权重相结合的算法,并且采用种子点扩展的方法。首先,通过交叉自适应窗口法,区分出连续点和孤立点,对于不同的分类点采用不同的处理方法。其次,针对每一个像素点,利用改进的自适应权重方法进行匹配。最后,提出一种新的种子点扩展的视差优化方法,对初始视差图进行精细化。实验结果表明,视差图中纹理密集区域和视差不连续区域的误匹配现象得到改善。该算法可以有效地处理图像中纹理分布不均的问题,提高了在视差不连续区域内匹配精度。     

基于自适应权重的改进Census立体匹配算法

针对传统Census算法对噪声敏感且在弱纹理区域匹配精度低的不足,提出一种基于自适应权重的改进算法。在代价计算阶段,通过空间相似度加权计算得到参考像素值,设定阈值限定参考值与中心点像素的差异,使算法能够判断中心点是否发生突变并自适应选择中心参考像素值。在代价聚合阶段,引入多尺度聚合策略,将引导滤波作为代价聚合核函数,加入正则化约束保持代价聚合时尺度间的一致性。在视差计算阶段,通过胜者通吃法得到初始视差图。在视差优化阶段,对初始视差图做误匹配点检测及左右一致性检测,并对遮挡区域进行像素填充得到最终的视差图。基于Middlebury标准图的实验结果表明,该算法平均误匹配率为5.81%,对比于传统Census算法抗干扰性提升显著,并能在平均误匹配率表现上达到主流经典算法的性能水准。     

一种基于SSD和图割的快速立体匹配算法

针对图割法的立体匹配算法耗时多的问题,提出了一种基于S S D和图割的快速立体匹配算法。为了缩小视差搜索范围,缩短匹配时间,先采用区域匹配S S D算法得到初始视差,然后再采用左右一致性校验法去除误匹配点,可以提高初始视差图的质量;在构造能量函数时,把初始视差图中的像素视差作为图割的能量函数的限制项,根据这些限制可以减少不必要的节点,从而减少了计算量,缩短匹配时间。通过实验证明了本文算法在保证匹配图像质量的情况下,能提高匹配效率,减少匹配时间。     

一种利用动态规划和左右一致性的立体匹配算法

立体匹配是计算机视觉领域研究的一个重要课题,为了得到准确、稠密的视差图,提出了一种利用动态规划和左右一致性的立体匹配算法。该算法首先分别以左、右图像为基元图像,计算各自的视差空间图像,在视差空间图像上利用动态规划,计算得到左视差图和右视差图;然后通过使用左右视差图之间的一致性关系,消除误匹配点,得到较为准确的部分视差图;最后利用视差图的顺序约束关系,给出未匹配视差点的搜索空间计算方法,并利用一种简单有效的方法来计算这些点的视差值。在一些标准立体图像对上所做的实验结果表明,该算法效果良好。     

Near-real-time stereo matching with slanted surface modeling and sub-pixel accuracy

This paper presents a new stereo matching algorithm which takes into consideration surface orientation at the per-pixel level. Two disparity calculation passes are used. The first pass assumes that surfaces in the scene are fronto-parallel and generates an initial disparity map, from which the disparity plane orientations of all pixels are estimated and refined. In the second pass, the matching costs for different pixels are aggregated along the estimated disparity plane orientations using adaptive support weights, where the support weights of neighboring pixels are calculated using a combination of four terms: a spatial proximity term, a color similarity term, a disparity similarity term, and an occlusion handling term. The disparity search space is quantized at sub-pixel level to improve the accuracy of the disparity results. The algorithm is designed for parallel execution on Graphics Processing Units (GPUs) for near-real-time processing speed. The evaluation using Middlebury benchmark shows that the presented approach outperforms existing real-time and near-real-time algorithms in terms of subpixel level accuracy.     

Trinocular Stereo Using Shortest Paths and the Ordering Constraint

This paper describes a new algorithm for disparity estimation using trinocular stereo. The three cameras are placed in a right angled configuration. A graph is then constructed whose nodes represent the individual pixels and whose edges are along the epipolar lines. Using the well known uniqueness and ordering constraint for pair by pair matches simultaneously, a path with the least matching cost is found using dynamic programming and the disparity filled along the path. This process is repeated iteratively until the disparity at all the pixels are filled up. To demonstrate the effectiveness of our approach, we present results from real world images and compare it with the traditional line by line stereo using dynamic programming.     

利用块几何约束及视差概率的立体匹配算法

为了解决倾斜表面或曲面的匹配问题,提出了一种基于图像分割块之间的几何约束和视差值的概率分布信息的视差估算方法。在一个全局能量函数中增加了图像分割块之间的几何约束项,通过计算匹配能量得到分割块的最优视差平面。为了确定可信像素和可信分割块,利用了视差的概率分布信息。同时,利用了分割块之间的几何约束和分割块内像素之间的约束来估计不可信像素点的视差值。用包含大视差范围、更多倾斜表面、曲面和弱纹理表面等典型图像对所建议的算法进行测试,实验结果表明,该方法对于存在倾斜表面和曲面的立体视差计算是有效的。     

An FPGA-based RGBD imager

This paper describes a trinocular stereo vision system using a single chip of FPGA to generate the composite color (RGB) and disparity data stream at video rate, called the RGBD imager. The system uses the triangular configuration of three cameras for synchronous image capture and the trinocular adaptive cooperative algorithm based on local aggregation for smooth and accurate dense disparity mapping. We design a fine-grain parallel and pipelining architecture in FPGA for implementation to achieve a high computational and real-time throughput. A binary floating-point format is customized for data representation to satisfy the wide data range and high computation precision demands in the disparity calculation. Memory management and data bit-width control are applied in the system to reduce the hardware resource consumption and accelerate the processing speed. The system is able to produce dense disparity maps with 320 × 240 pixels in a disparity search range of 64 pixels at the rate of 30 frames per second.     

Real-time parallel computation of disparity and optical flow using phase difference

A phase-difference-based algorithm for disparity and optical flow estimation is implemented on a TI-C40-based parallel DSP system. The module performs real-time computation of disparity maps on images of size 128 × 128 pixels and computation of optical flows on images of size 64 × 64 pixels. This paper describes the algorithm and its parallel implementation. Processing times required for the computation of disparity maps and velocity fields and measures of the algorithm's performance are reported in detail.     

基于纹理分析的视差估计算法

针对传统方法难以可靠估计图像中纹理单一像素点视差的问题,提出一种新的基于纹理分析的视差估计算法。与已有方法不同,在以极线约束计算像素点视差时,将极线上纹理单一且近似的像素点合并成直线段,根据连续性和唯一性约束对直线段进行整体匹配,采用直线段的视差得到纹理单一区域的稠密视差图。利用直线段进行整体匹配,提高比较基元包含的信息量,减少扫描范围,从而降低误匹配产生的概率和算法时间复杂度。实验结果表明,该方法能提高纹理单一区域稠密视差图的精度,匹配速度快,具有实用价值。     

FPGA based disparity map computation with vergence control

Depth estimation in a scene using image pairs acquired by a stereo camera setup, is one of the important tasks of stereo vision systems. The disparity between the stereo images allows for 3D information acquisition which is indispensable in many machine vision applications. Practical stereo vision systems involve wide ranges of disparity levels. Considering that disparity map extraction of an image is a computationally demanding task, practical real-time FPGA based algorithms require increased device utilization resource usage, depending on the disparity levels operational range, which leads to significant power consumption. In this paper a new hardware-efficient real-time disparity map computation module is developed. The module constantly estimates the precisely required range of disparity levels upon a given stereo image set, maintaining this range as low as possible by verging the stereo setup cameras axes. This enables a parallel-pipelined design, for the overall module, realized on a single FPGA device of the Altera Stratix IV family. Accurate disparity maps are computed at a rate of more than 320 frames per second, for a stereo image pair of 640 × 480 pixels spatial resolution with a disparity range of 80 pixels. The presented technique provides very good processing speed at the expense of accuracy, with very good scalability in terms of disparity levels. The proposed method enables a suitable module delivering high performance in real-time stereo vision applications, where space and power are significant concerns.     

Scalable hardware architecture for disparity map computation and object location in real-time

We present the disparity map computation core of a hardware system for isolating foreground objects in stereoscopic video streams. The operation is based on the computation of dense disparity maps using block-matching algorithms and two well-known metrics: sum of absolute differences and Census transform. Two sets of disparity maps are computed by taking each of the images as reference so that a consistency check can be performed to identify occluded pixels and eliminate spurious foreground pixels. Taking advantage of parallelism, the proposed architecture is highly scalable and provides numerous degrees of adjustment to different application needs, performance levels and resource usage. A version of the system for 640 × 480 images and a maximum disparity of 135 pixels was implemented in a system based on a Xilinx Virtex II-Pro FPGA and two cameras with a frame rate of 25 fps (less than the maximum supported frame rate of 40 fps on this platform). Implementation of the same system on a Virtex-5 FPGA is estimated to achieve 80 fps, while a version with increased parallelism is estimated to run at 140 fps (which corresponds to the calculation of more than 5.9 × 10⁹ disparity-pixels per second).