结合传播滤波的立体匹配算法研究

针对局部立体匹配算法在边缘处容易出现误匹配的问题,本文提出了一种结合权值传播进行代价聚合的局部立体匹配方法。首先采用基于颜色梯度的绝对差及Census方法构造了匹配代价函数;然后,引入传播滤波平滑匹配代价的同时保持视差空间图像边缘,与其他局部滤波器相比,该滤波器利用可传播的权值思想,不受传统局部算法窗口大小的影响;最后,通过左右一致性检查和无效视差值填充获得最终视差图。实验表明,该方法在Middlebury Stereo数据集上可获得精确结果,与Middlebury测试平台上的IGF、TSGO和Dog-Guided算法相比平均误差最低。     

Stereo processing by semiglobal matching and mutual information

This paper describes the Semi-Global Matching (SGM) stereo method. It uses a pixelwise, Mutual Information based matching cost for compensating radiometric differences of input images. Pixelwise matching is supported by a smoothness constraint that is usually expressed as a global cost function. SGM performs a fast approximation by pathwise optimizations from all directions. The discussion also addresses occlusion detection, subpixel refinement and multi-baseline matching. Additionally, postprocessing steps for removing outliers, recovering from specific problems of structured environments and the interpolation of gaps are presented. Finally, strategies for processing almost arbitrarily large images and fusion of disparity images using orthographic projection are proposed.A comparison on standard stereo images shows that SGM is among the currently top-ranked algorithms and is best, if subpixel accuracy is considered. The complexity is linear to the number of pixels and disparity range, which results in a runtime of just 1-2s on typical test images. An in depth evaluation of the Mutual Information based matching cost demonstrates a tolerance against a wide range of radiometric transformations. Finally, examples of reconstructions from huge aerial frame and pushbroom images demonstrate that the presented ideas are working well on practical problems.     

Robust multiscale stereo matching from fundus images with radiometric differences

A robust multiscale stereo matching algorithm is proposed to find reliable correspondences between low contrast and weakly textured retinal image pairs with radiometric differences. Existing algorithms designed to deal with piecewise planar surfaces with distinct features and Lambertian reflectance do not apply in applications such as 3D reconstruction of medical images including stereo retinal images. In this paper, robust pixel feature vectors are formulated to extract discriminative features in the presence of noise in scale space, through which the response of low-frequency mechanisms alter and interact with the response of high-frequency mechanisms. The deep structures of the scene are represented with the evolution of disparity estimates in scale space, which distributes the matching ambiguity along the scale dimension to obtain globally coherent reconstructions. The performance is verified both qualitatively by face validity and quantitatively on our collection of stereo fundus image sets with ground truth, which have been made publicly available as an extension of standard test images for performance evaluation.     

Sampling the disparity space image

A central issue in stereo algorithm design is the choice of matching cost. Many algorithms simply use squared or absolute intensity differences based on integer disparity steps. In this paper, we address potential problems with such approaches. We begin with a careful analysis of the properties of the continuous disparity space image (DSI) and propose several new matching cost variants based on symmetrically matching interpolated image signals. Using stereo images with ground truth, we empirically evaluate the performance of the different cost variants and show that proper sampling can yield improved matching performance.     

基于改进Census变换的多特性立体匹配算法

针对当前立体匹配算法存在的匹配准确率低，难以达到实用的高精度水平的问题，提出了一种基于改良的Census变换与色彩信息和梯度测度相结合的多特性立体匹配算法，实现高精度的双目立体匹配。算法首先在初始代价匹配阶段，将改进的Census变换、色彩和梯度测度赋权求和得出可靠的初始匹配代价；在聚合阶段，采取高效快捷的最小生成树聚合，获得匹配代价矩阵；最后根据胜者为王法则得到初始视差图，并引入左右一致性检测等策略优化视差图，获得高精度的视差图，实验阶段对源自Middlebury上的标准测试图进行测试验证，实验结果表明，经本文算法处理得到的15组测试数据集的视差图在非遮挡区域的平均误匹配率为6.81%，算法实时响应性优良。     

Toward improved ranking metrics

In many computer vision algorithms, a metric or similarity measure is used to determine the distance between two features. The Euclidean or SSD (sum of the squared differences) metric is prevalent and justified from a maximum likelihood perspective when the additive noise distribution is Gaussian. Based on real noise distributions measured from international test sets, we have found that the Gaussian noise distribution assumption is often invalid. This implies that other metrics, which have distributions closer to the real noise distribution, should be used. In this paper, we consider three different applications: content-based retrieval in image databases, stereo matching, and motion tracking. In each of them, we experiment with different modeling functions for the noise distribution and compute the accuracy of the methods using the corresponding distance measures. In our experiments, we compared the SSD metric, the SAD (sum of the absolute differences) metric, the Cauchy metric, and the Kullback relative information. For several algorithms from the research literature which used the SSD or SAD, we showed that greater accuracy could be obtained by using the Cauchy metric instead.     

改进的跨尺度引导滤波立体匹配算法

作为双目三维重建中的关键步骤,双目立体匹配算法完成了从平面视觉到立体视觉的转化.但如何平衡双目立体匹配算法的运行速度和精度仍然是一个棘手的问题.本文针对现有的局部立体匹配算法在弱纹理、深度不连续等特定区域匹配精度低的问题,并同时考虑到算法实时性,提出了一种改进的跨多尺度引导滤波的立体匹配算法.首先融合AD和Census变换两种代价计算方法,然后采用基于跨尺度的引导滤波进行代价聚合,在进行视差计算时通过制定一个判断准则判断图像中每一个像素点的最小聚合代价对应的视差值是否可靠,当判断对应的视差值不可靠时,对像素点构建基于梯度相似性的自适应窗口,并基于自适应窗口修正该像素点对应的视差值.最后通过视差精化得到最终的视差图.在Middlebury测试平台上对标准立体图像对的实验结果表明,与传统基于引导滤波器的立体匹配算法相比具有更高的精度.     

Terrain mapping under extreme light conditions with direct stereo matching method through aggregating matching costs by weight

One of the biggest problems in applying stereo vision techniques in field robotics is how to acquire 3D terrain maps under extreme light conditions. Through multiple exposures, the dynamic range of images can be increased. In this paper, instead of using existing lighting enhancement methods such as exposure fusion to increase the texture of 2D image, we propose that the matching costs of the images grabbed with multiple exposures are directly summed by weight. Compared with the previous methods such as exposure fusion, with the proposed method, it is not necessary to fuse the 2D images captured with multiple exposures, and for each pixel of the matching image, the local information in its local window can be better retained. Since it is possible that the camera is moved between exposures when the images are grabbed, the images captured with multiple exposures are aligned to the image acquired with auto exposure. In order to evaluate the performance of the proposed method, two different stereo matching algorithms were used: a local window-based method and semi-global method. Through experiments in laboratory and outdoors with a stereo vision camera fixed on a tripod and held in the hand, it was verified that the proposed method consistently allowed more valid points to be obtained and the 3D model of terrain can be built more accurately. Especially when the local window-based method was used, the proposed method performed much better.     

多特征融合和自适应聚合的立体匹配算法研究

针对局部立体匹配在光照失真和弱纹理区域匹配精度低的问题,提出了一种多特征融合的代价计算和自适应十字窗口聚合的立体匹配算法。引入HSV颜色空间分量,结合改进后的Census变换和梯度信息作为匹配代价计算方法,排除了视差边界异常值的影响,增强了算法对光照失真的稳健性;提出了基于梯度信息和可变颜色阈值的自适应窗口代价聚合方法,提高了在弱纹理区域的匹配精度;通过视差计算和多步骤的视差精细得到了最终的视差结果。实验结果表明,所提算法较AD-Census算法在无光照失真条件下误匹配减少了3.24%,能有效解决视差边界和弱纹理区域错误匹配的问题,对光照失真稳健性好且能有效抑制噪声干扰。     

一种新颖的自适应权重Census变换立体匹配算法

针对当前Census变换立体匹配算法深度不连续区域匹配精度低的缺陷,提出了一种新颖的自适应权重的Census变换立体匹配算法。在Census变换阶段计算变换窗口中心点上下左右四个像素的均值,得到中心点与该均值的差的绝对值,通过判断该绝对值的大小来确定中心点灰度值;为了有区别地对待窗口内各像素点,引入自适应权重,通过线性分段型函数计算自适应权值。在代价聚合阶段同样引入自适应权重并采用变化的聚合窗口,通过聚合窗口中心点和其左右两点的梯度值来确定聚合窗口的大小。实验结果表明,算法的匹配效果优于目前的Census变换立体匹配算法,在深度不连续区域匹配效果显著改善,而且没有明显降低实时性和增加硬件实现的难度。     

双目区域视差快速计算及测距算法

目的 双目测距对水面无人艇自主避障以及视觉侦察具有重要意义,但视觉传感器成像易受光照环境及运动模糊等因素的影响,基于经典Census变换的立体匹配代价计算方法耗时长,且视差获取精度差,影响测距精度。为了提高测距精度并保证算法运行速度,提出一种用于双目测距的快速立体匹配算法。方法 基于传统Census变换,提出一种新的比特串生成方法,在匹配点正方形支持窗口的各边等距各选3个像素点,共选出8个像素点,这8个像素点两两比较生成一个字节的比特串。将左右视场中的匹配点与待匹配点的比特串进行异或,得到两点的汉明距离,在各汉明距离中找到距离最小的像素点作为匹配像素点,两像素点的横坐标差为视差。本文采用区域视差计算的方法,在左右视场确定同一目标区域后进行视差提取和滤波,利用平均视差计算目标的距离。结果 本文算法与基于传统Census变换的立体匹配视差获取方法相比,在运算速度方面优势明显,时间稳定在0.4 s左右,用时仅为传统Census变换算法的1/5。在Middlebury数据集中的图像对teddy和cones上进行的算法运行时间对比实验中,本文基于Census变换改进的算法比已有的基于Census变换的匹配算法在运行时间上快了近20 s。在实际双目测距实验中,采用本文算法在1019 m范围内测距误差在5%以内,根据无人艇的运动特点和避障要求,通过分析可知该算法的测距精度可以满足低速无人艇的避障需求。结论 本文给出的基于改进Census变换的匹配算法在立体匹配速度上有大幅提高,提取目标视差用于测距,实际测距结果表明,本文算法能够满足水面无人艇的视觉避障要求。     

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.     

融合边缘保持与改进代价聚合的立体匹配算法

目的 立体匹配是计算机双目视觉的重要研究方向,主要分为全局匹配算法与局部匹配算法两类。传统的局部立体匹配算法计算复杂度低,可以满足实时性的需要,但是未能充分利用图像的边缘纹理信息,因此在非遮挡、视差不连续区域的匹配精度欠佳。为此,提出了融合边缘保持与改进代价聚合的立体匹配。方法 首先利用图像的边缘空间信息构建权重矩阵,与灰度差绝对值和梯度代价进行加权融合,形成新的代价计算方式,同时将边缘区域像素点的权重信息与引导滤波的正则化项相结合,并在多分辨率尺度的框架下进行代价聚合。所得结果经过视差计算,得到初始视差图,再通过左右一致性检测、加权中值滤波等视差优化步骤获得最终的视差图。结果 在Middlebury立体匹配平台上进行实验,结果表明,融合边缘权重信息对边缘处像素点的代价量进行了更加有效地区分,能够提升算法在各区域的匹配精度。其中,未加入视差优化步骤的21组扩展图像对的平均误匹配率较改进前减少3.48%,峰值信噪比提升3.57 dB,在标准4幅图中venus上经过视差优化后非遮挡区域的误匹配率仅为0.18%。结论 融合边缘保持的多尺度立体匹配算法有效提升了图像在边缘纹理处的匹配精度,进一步降低了非遮挡区域与视差不连续区域的误匹配率。     

SymStereo: Stereo Matching using Induced Symmetry

Stereo methods always require a matching function for assessing the likelihood of two pixels being in correspondence. Such functions, commonly referred as matching costs, measure the photo-similarity (or dissimilarity) between image regions centered in putative matches. This article proposes a new family of stereo cost functions that measure symmetry instead of photo-similarity for associating pixels across views. We start by observing that, given two stereo views and an arbitrary virtual plane passing in-between the cameras, it is possible to render image signals that are either symmetric or anti-symmetric with respect to the contour where the virtual plane meets the scene. The fact is investigated in detail and used as cornerstone to develop a new stereo framework that relies in symmetry cues for solving the data association problem. Extensive experiments in dense stereo show that our symmetry-based cost functions compare favorably against the best performing photo-similarity matching costs. In addition, we investigate the possibility of accomplishing Stereo Rangefinding that consists in using passive stereo to exclusively recover depth along a pre-defined scan plane. Thorough experiments provide evidence that stereo from induced symmetry is specially well suited for this purpose.     

BRDF invariant stereo using light transport constancy

Nearly all existing methods for stereo reconstruction assume that scene reflectance is Lambertian and make use of brightness constancy as a matching invariant. We introduce a new invariant for stereo reconstruction called light transport constancy (LTC), which allows completely arbitrary scene reflectance (bidirectional reflectance distribution functions (BRDFs)). This invariant can be used to formulate a rank constraint on multiview stereo matching when the scene is observed by several lighting configurations in which only the lighting intensity varies. In addition, we show that this multiview constraint can be used with as few as two cameras and two lighting configurations. Unlike previous methods for BRDF invariant stereo, LTC does not require precisely configured or calibrated light sources or calibration objects in the scene. Importantly, the new constraint can be used to provide BRDF invariance to any existing stereo method whenever appropriate lighting variation is available.     

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 fast stereo matching algorithm suitable for embedded real-time systems

In this paper, the challenge of fast stereo matching for embedded systems is tackled. Limited resources, e.g. memory and processing power, and most importantly real-time capability on embedded systems for robotic applications, do not permit the use of most sophisticated stereo matching approaches. The strengths and weaknesses of different matching approaches have been analyzed and a well-suited solution has been found in a Census-based stereo matching algorithm. The novelty of the algorithm used is the explicit adaption and optimization of the well-known Census transform in respect to embedded real-time systems in software. The most important change in comparison with the classic Census transform is the usage of a sparse Census mask which halves the processing time with nearly unchanged matching quality. This is due the fact that large sparse Census masks perform better than small dense masks with the same processing effort. The evidence of this assumption is given by the results of experiments with different mask sizes. Another contribution of this work is the presentation of a complete stereo matching system with its correlation-based core algorithm, the detailed analysis and evaluation of the results, and the optimized high speed realization on different embedded and PC platforms. The algorithm handles difficult areas for stereo matching, such as areas with low texture, very well in comparison to state-of-the-art real-time methods. It can successfully eliminate false positives to provide reliable 3D data. The system is robust, easy to parameterize and offers high flexibility. It also achieves high performance on several, including resource-limited, systems without losing the good quality of stereo matching. A detailed performance analysis of the algorithm is given for optimized reference implementations on various commercial of the shelf (COTS) platforms, e.g. a PC, a DSP and a GPU, reaching a frame rate of up to 75 fps for 640 × 480 images and 50 disparities. The matching quality and processing time is compared to other algorithms on the Middlebury stereo evaluation website reaching a middle quality and top performance rank. Additional evaluation is done by comparing the results with a very fast and well-known sum of absolute differences algorithm using several Middlebury datasets and real-world scenarios.     

一种室外非理想光照条件下的立体匹配算法

针对室外非理想光照条件和图像低纹理、低对比度造成立体匹配效果较差的问题,提出一种HSL(色相-饱和度-亮度)颜色空间下基于边缘图分割的立体匹配算法.区别于传统的RGB颜色空间下基于像素强度的度量方式,该算法采用一种HSL颜色空间下的像素非相似性度量公式来获得匹配代价,然后基于左右输入图像的边缘检测结果进行图像区域分割和立体匹配.在实验中采用一系列不同光照条件的图片集和具有明显低纹理区域的图片集,对本文算法与现有算法进行对比验证.实验结果证明,该算法能够得到比较理想的视差图,对非理想的光照条件和低纹理图像具有很好的鲁棒性,并且基本达到实时性要求.     

Dense Features for Semi-Dense Stereo Correspondence

We present a new feature based algorithm for stereo correspondence. Most of the previous feature based methods match sparse features like edge pixels, producing only sparse disparity maps. Our algorithm detects and matches dense features between the left and right images of a stereo pair, producing a semi-dense disparity map. Our dense feature is defined with respect to both images of a stereo pair, and it is computed during the stereo matching process, not a preprocessing step. In essence, a dense feature is a connected set of pixels in the left image and a corresponding set of pixels in the right image such that the intensity edges on the boundary of these sets are stronger than their matching error (which is the difference in intensities between corresponding boundary pixels). Our algorithm produces accurate semi-dense disparity maps, leaving featureless regions in the scene unmatched. It is robust, requires little parameter tuning, can handle brightnessdifferences between images, nonlinear errors, and is fast (linear complexity).