A wavelet-based multiresolution approach to solve the stereo correspondence problem using mutual information.

In this correspondence, we propose a wavelet-based hierarchical approach using mutual information (MI) to solve the correspondence problem in stereo vision. The correspondence problem involves identifying corresponding pixels between images of a given stereo pair. This results in a disparity map, which is required to extract depth information of the relevant scene. Until recently, mostly correlation-based methods have been used to solve the correspondence problem. However, the performance of correlation-based methods degrades significantly when there is a change in illumination between the two images of the stereo pair. Recent studies indicate MI to be a more robust stereo matching metric for images affected by such radiometric distortions. In this short correspondence paper, we compare the performances of MI and correlation-based metrics for different types of illumination changes between stereo images. MI, as a statistical metric, is computationally more expensive. We propose a wavelet-based hierarchical technique to counter the increase in computational cost and show its effectiveness in stereo matching.     

A new neural real-time implementation for obstacle detection using linear stereo vision

This paper presents a neural vision system for real-time obstacle detection in front of a moving vehicle using linear stereo vision. The key problem is the correspondence task which consists of matching features in two stereo images that are projections of the same physical entity in the three-dimensional world. The linear stereo correspondence problem is formulated as an optimization task. An energy function, which represents the constraints on the solution, is mapped onto a Hopfield neural network for minimization. The system has been evaluated with experimental results on real stereo images.     

基于直线间结构信息的立体视觉图像动态匹配方法

针对立体视觉匹配问题，介绍一种改进的动态规划图像匹配方法，它将边缘直线相似测度分为局部相似测度和全局相似测度，在后者中加入图像边缘直线之问的结构关系信息，并在动态搜索最优匹配路径的过程中利用结构关系约束删除不合理的匹配路径。仿真实验结果证明，采用该方法解决立体视觉中边缘线段的匹配问题，不仅提高了匹配的准确率，而且大大减少了匹配时间。     

一种改进的区域双目立体匹配方法

双目立体匹配是机器视觉中的热点、难点问题。分析了区域立体匹配方法的优缺点,提出了改进的区域立体匹配方法。首先,采集双目视觉图像对对图像对进行校正、去噪等处理,利用颜色特征进行图像分割,再用一种快速有效的块立体匹配算法对图像进行立体匹配。然后,在匹配过程中使用绝对误差累积(SAD)的小窗口来寻找左右两幅图像之间的匹配点。最后,通过滤波得到最终的视差图。实验表明:该方法能够有效地解决重复区域、低纹理区域、纹理相似区域、遮挡区域等带来的误匹配问题,能得到准确清晰的稠密视差图。     

Improving sub-pixel correspondence through upsampling

Many fundamental computer vision problems, including optical flow estimation and stereo matching, involve the key step of computing dense color matching among pixels. In this paper, we show that by merely upsampling, we can improve sub-pixel correspondence estimation. In addition, we identify the regularization bias problem and explore its relationship to image resolution. We propose a general upsampling framework to compute sub-pixel color matching for different computer vision problems. Various experiments were performed on motion estimation and stereo matching data. We are able to reduce errors by up to 30%, which would otherwise be very difficult to achieve through other conventional optimization methods.     

Stereo matching algorithm with guided filter and modified dynamic programming

Dense stereo correspondence is a challenging research problem in computer vision field. To address the poor accuracy behavior of stereo matching, we propose a novel stereo matching algorithm based on guided image filter and modified dynamic programming. Firstly, we suggest a combined matching cost by incorporating the absolute difference and improved color census transform (ICCT). Secondly, we use the guided image filter to filter the cost volume, which can aggregate the costs fast and efficiently. Then, in the disparity computing step, we design a modified dynamic programming algorithm, which can weaken the scanning line effect. At last, final disparity maps are gained after post-processing. The experimental results are evaluated on Middlebury Stereo Datasets, showing that our approach can achieve good results both in low texture and depth discontinuity areas with an average error rate of 5.14 % and strong robustness.     

Generalised residual images’ effect on illumination artifact removal for correspondence algorithms

The intensity (grey value) consistency of image pixels in a sequence or stereo camera setup is of central importance to numerous computer vision applications. Most stereo matching and optical flow algorithms minimise an energy function composed of a data term and a regularity or smoothing term. To date, well performing methods rely on the intensity consistency of the image pixel values to model the data term. Such a simple model fails if the illumination is (even slightly) different between the input images. Amongst other situations, this may happen due to background illumination change over the sequence, different reflectivity of a surface, vignetting, or shading effects.In this paper, we investigate the removal of illumination artifacts and show that generalised residual images substantially improve the accuracy of correspondence algorithms. In particular, we motivate the concept of residual images and show two evaluation approaches using either ground truth correspondence fields (for stereo matching and optical flow algorithms) or errors based on a predicted view (for stereo matching algorithms).     

LiDAR数据与CCD影像融合算法研究

依据新型遥感载荷LiDAR数据高程精度高、CCD面阵影像平面精度高的特点,本文提出一种基于点特征的激光点云与CCD影像高精度匹配的方法,研究了立体像对与激光点云联合同名点搜索算法和影像特征点与离散激光点迭代匹配求解算法,实现了一个像对区域的影像与点云高精度配准,获得了三维彩色点云影像。得到的影像既具光学影像的颜色纹理信息又具有LiDAR数据的高程信息,提高了地物目标识别解译的可靠性和量测的精度,也为更进一步的地物分类提供更为精确的原始数据。     

基于颜色和几何特征的图像特征点匹配算法

提出一种基于颜色和几何特征的图像特征点匹配算法。首先提取两幅图像特征点集邻域色调的局部累加直方图,然后结合图像特征点的几何特征构造亲近矩阵,再对亲近矩阵进行奇异值分解(SVD),利用分解的结果构造出一个反应特征点之间匹配程度的关系矩阵,最后根据关系矩阵实现两幅图像的特征点匹配。实验结果显示,这种图像特征点匹配算法对真实图像的平面旋转和立体旋转都具有较高的匹配精确度。     

3-D shape reconstruction in an active stereo vision system using genetic algorithms

The recovery of 3-D shape information (depth) using stereo vision analysis is one of the major areas in computer vision and has given rise to a great deal of literature in the recent past. The widely known stereo vision methods are the passive stereo vision approaches that use two cameras. Obtaining 3-D information involves the identification of the corresponding 2-D points between left and right images. Most existing methods tackle this matching task from singular points, i.e. finding points in both image planes with more or less the same neighborhood characteristics. One key problem we have to solve is that we are on the first instance unable to know a priori whether a point in the first image has a correspondence or not due to surface occlusion or simply because it has been projected out of the scope of the second camera. This makes the matching process very difficult and imposes a need of an a posteriori stage to remove false matching.In this paper we are concerned with the active stereo vision systems which offer an alternative to the passive stereo vision systems. In our system, a light projector that illuminates objects to be analyzed by a pyramid-shaped laser beam replaces one of the two cameras. The projections of laser rays on the objects are detected as spots in the image. In this particular case, only one image needs to be treated, and the stereo matching problem boils down to associating the laser rays and their corresponding real spots in the 2-D image. We have expressed this problem as a minimization of a global function that we propose to perform using Genetic Algorithms (GAs). We have implemented two different algorithms: in the first, GAs are performed after a deterministic search. In the second, data is partitioned into clusters and GAs are independently applied in each cluster. In our second contribution in this paper, we have described an efficient system calibration method. Experimental results are presented to illustrate the feasibility of our approach. The proposed method yields high accuracy 3-D reconstruction even for complex objects. We conclude that GAs can effectively be applied to this matching problem.     

改进型SIFT立体匹配算法研究

针对机器人视觉系统立体匹配中存在的匹配重复或错误等问题,提出了一种基于尺度不变特征变换（Scale Invariant Feature Transform,SIFT算法）和余弦相似度匹配规则的立体匹配方法。该方法以左、右两幅图像中特征向量较多的图像作为基准匹配图像,另一幅图像作为待匹配图像;再由二者的特征向量之间的余弦相似度所建立的匹配规则进行立体匹配。实验结果表明,改进型立体匹配方法有效地降低了匹配错误或重复比,具有较强的鲁棒性,匹配效果较佳,更加有利于机器人视觉系统的三维重建与定位。     

An Evolutionary-Based Stereo Matching Method with a Multilevel Searching Strategy

Stereo matching is one of the fundamental problems in computer vision. It consists in identifying features in two or more stereo images that are generated by the same physical feature in the three-dimensional space. This paper presents an evolutionary approach with a multilevel searching strategy for matching edges extracted from two stereo images. The matching problem is turned into an optimization task, which is performed by means of a genetic algorithm with a new encoding scheme. For an effective exploitation of the genetic stereo matching algorithm for real-time obstacle detection, a multilevel searching strategy is proposed to match the edges at different levels by considering their gradient magnitudes. Experimental results and comparative analysis are presented to demonstrate the effectiveness of the proposed method for real-time obstacle detection in front of a moving vehicle using linear stereo vision.     

视皮层中的视差计算

对应问题是从图像恢复物体三维几何形状的核心问题,也是计算机视觉领域长久以来没有很好解决的问题.尽管文献中 有大量对应点匹配算法报道,但这些主体基于数学和工程的方法,在鲁棒性方面离人类视觉还有很大的差距.所以,建立基于生理机理的 立体感知模型,可望有效提高匹配的鲁棒性.然而,要建立这种基于生理机制的计算模型, 需要对生物立体感知的机理有所了解.基于此,本文对近年来 关于生物立体感知的进展和主要研究成果进行了总结,包括初级区域V1, V2, V3区,以及 背部通道的MT, MST, IPS和腹部通道的V4, IT区.除了对这些区域 关于神经生理的成果进行介绍外,我们还简单介绍了著名的视差能量模型及其推广模型.本文对研究计算机视觉的人员有一定的参考价值.     

RASCor: An associative hardware algorithm for real time stereo

Solving the stereo correspondence problem is an important step towards extraction of three dimensional structure of a scene from two or more images taken from distinct viewpoints. This paper presents a hardware solution to real time stereo matching problem using system of associative relations (SOAR) computational model. SOAR makes use of direction of derivatives for capturing pair-wise pixel interactions and uses pair-wise interactions to determine the underlying structure of associations. Pair-wise pixel interactions are defined over structures called Tokens. The hardware stereo matching solution developed in this study utilizes direction of derivatives to encode inter-pixel associations and implements a real time stereo matching solution. RASCor is realized on an FPGA platform and offers real time stereo matching using hardware parallelism and process pipelining. Real time stereo matching is achieved through hardware unit multiplicity and frequency scaling. The cost of hardware implementation is presented along with performance improvements from frequency scaling, unit multiplicity and search depth for stereo matching.     

双目视觉小波域SIFT匹配与极线约束算法研究

研究双目立体视觉技术,特征的提取和匹配是双目视觉的最基本的问题。目前,SIFT已经被证明鲁棒性最好的局部不变特征描述符。但是SIFT算法产生的误匹配较多,精度偏低,为了解决这一问题,同时降低算法特征提取与匹配的复杂度,达到双目立体视觉实时性的要求,文中提出了一种结合小波变换和SIFT特征点的双目立体视觉匹配方法。首先,对双目视觉系统采集的左、右图像进行小波分解,把分解得到的低频图像作为输入,用SIFT算法进行特征点的初始匹配,再利用极线约束的理论求得精确匹配。实验结果表明,该方法具有较强的适应性,能够在减少误匹配的同时,大大加快运算速度。     

Line segment matching for 3D computer vision using a new iteration scheme

A new iteration scheme is proposed to solve the line segment matching problem in stereo vision analysis. A match function which directly reflects the requirements of the epipolar and disparity constraints is proposed for line segment matching. The information contained in the match function is used to determine line segment correspondences indirectly. After a match network is established according to the match function values, a new iteration algorithm is employed to tune the strengths of the match links in the match network so that the match network can converge to a stable state. No explicit compatibility coefficient need be defined for computing the support function values in the iterations, resulting in a faster computation speed than those of conventional relaxation matching techniques. The inherent anti-symmetric characteristic of relaxation matching for the image correspondence problem is also avoided naturally. The experimental results show that the proposed iteration scheme is effective and suitable for matching line segments even when images are complicated.     

基于FPGA的双目立体视觉系统

立体视觉的目的之一就是为了获得周围场景的3维信息,其关键在于匹配算法。然而即便是使用目前先进的通用处理器,其计算致密视差图所需的时间仍无法满足高速自主导航的需求。为了解决这个问题,提出了一种基于现场可编程门阵列(FPGA)的双目立体视觉系统的设计方案,同时介绍了系统的硬件结构,并在讨论区域匹配的快速算法的基础上,提出了基于FPGA的像素序列和并行窗口算法框架,用以实现零均值像素灰度差平方和(ZSSD)的匹配算法。该算法是先将视频信号经解码芯片生成场景立体图像对,并由FPGA来完成立体图像对的几何校正和ZSSD匹配算法,然后将获得的致密视差图通过PC I总线发送至上位机。实践表明,该算法效果好、速度快,不仅具有较强的鲁棒性,并且硬件系统性能稳定、可靠。此外,该方案还适用于像素灰度差的绝对值和(SAD)和像素灰度差的平方和(SSD)等多种传统区域匹配算法的快速实现和实时处理。     

Structured light using pseudorandom codes

We solve the correspondence problem in active stereo vision using a novel pseudorandom coded structured light (SL). This coding scheme performs well in the presence of occlusion. In settings where color coding is feasible, 3D information can be obtained using a single image     

基于径向基神经网络的立体匹配算法*

针对双目视觉中的图像立体匹配问题,提出了一种基于径向基神经网络的立体匹配算法。该算法提取图像的尺度不变特征变换（SIFT）特征建立特征匹配矩阵,对特征匹配向量进行约简,最后将约简的特征匹配向量输入径向基神经网络进行识别输出。仿真和实际图像实验表明,该算法的匹配正确率比标准的SIFT有所到提高。     

基于多邻域支持技术的迭代式角点匹配算法

角点匹配是立体视觉研究中的一个重要问题,文中针对该问题提出一种基于多邻域支持技术的迭代式角点匹配算法.该算法首先使用Marr和Frisby提出的立体匹配的五大约束限定搜索区域,然后使用了多邻域支持技术对点特征相似性的评价方法进行了改进,最后引入了左/右以及上/下两种对称性测试过程和迭代技术以提高匹配的精度.这些技术解决了传统匹配算法实时性差、精确度低的问题.仿真实验表明,该算法是一种快速、稳定并且实用的角点匹配算法.