Hierarchical adaptive stereo matching algorithm for obstacle detection with dynamic programming

An adaptive weighted stereo matching algorithm with multilevel and bidirectional dynamic programming based on ground control points （GCPs） is presented. To decrease time complexity without losing matching precision, using a multilevel search scheme, the coarse matching is processed in typical disparity space image, while the fine matching is processed in disparity-offset space image. In the upper level, GCPs are obtained by enhanced volumetric iterative algorithm enforcing the mutual constraint and the threshold constraint. Under the supervision of the highly reliable GCPs, bidirectional dynamic programming framework is employed to solve the inconsistency in the optimization path. In the lower level, to reduce running time, disparity-offset space is proposed to efficiently achieve the dense disparity image. In addition, an adaptive dual support-weight strategy is presented to aggregate matching cost, which considers photometric and geometric information. Further, post-processing algorithm can ameliorate disparity results in areas with depth discontinuities and related by occlusions using dual threshold algorithm, where missing stereo information is substituted from surrounding regions. To demonstrate the effectiveness of the algorithm, we present the two groups of experimental results for four widely used standard stereo data sets, including discussion on performance and comparison with other methods, which show that the algorithm has not only a fast speed, but also significantly improves the efficiency of holistic optimization.     

金字塔双层动态规划立体匹配算法

针对控制点修正的动态规划立体匹配算法存在控制点求取时阀长、实时性差的问题．提出一种金字塔双层动态规划立体匹配算法．采用金字塔算法求取低、商分辨率图像,然后分别在低、商分辨宰图像上求取候选控制点集和最终控制点集,并用最终控制点集修正商分辨率图像上的动态规划立体匹配．由干候选控制点集的求取在低分辨率图像上进行,算法用时大为减少．实验证明,此算法匹配率商、速度快．     

基于区域间协同优化的立体匹配算法

提出了一种基于分割区域间协同优化的立体匹配算法. 该算法以图像区域为匹配基元, 利用区域的彩色特征以及相邻区域间应满足的平滑和遮挡关系定义了区域的匹配能量函数, 并引入区域之间的合作竞争机制, 通过协同优化使所定义的匹配能量极小化, 从而得到比较理想的视差结果. 算法首先对参考图像进行分割, 利用相关法得到各分割区域的初始匹配; 然后用平面模型对各区域的视差进行拟合, 得到各区域的视差平面参数; 最后, 基于协同优化的思想, 采用局部优化的方法对各区域的视差平面参数进行迭代优化, 直至得到比较合理的视差图为止. 采用Middlebury test set进行的实验结果表明, 该方法在性能上可以和目前最好的立体匹配算法相媲美, 得到的视差结果接近于真实视差.     

Fast spatio-temporal stereo for intelligent transportation systems

This paper presents a fast approach for matching stereoscopic images acquired by stereo cameras mounted aboard a moving car. The proposed approach exploits the spatio-temporal consistency between consecutive frames in stereo sequences to improve matching results. This means that the matching process at current frame uses the matching results obtained at its preceding one. The preceding frame allows to compute an Initial Disparity Map for the current frame. The initial disparity map is used to derive disparity ranges for each scanline as well as what we call Matching Control Edge Points. Dynamic programming is performed for matching edge points in stereo pairs. The matching control edge points are used to drive the search for an optimal solution in the search plane. This is accomplished by dividing the dynamic programming search space into a number of subspaces depending on the number of the matching control edge points. The proposed approach has been tested both on virtual and real stereo images sequences demonstrating satisfactory performance.     

基于多尺度和多层级特征融合的立体匹配方法

基于卷积神经网络的立体匹配方法未充分利用图像中各个层级的特征图信息,造成对图像在不适定区域的特征提取能力较差。提出一种融合多尺度与多层级特征的立体匹配方法。通过在双塔结构卷积神经网络模型的前端设计一个池化金字塔层,提取图像的多尺度低层结构特征。在该网络模型的后端融合最后三层网络的高级语义特征来提取图像特征,并对图像特征进行相似性度量后输出视差图。在KITTI 2015数据集上的实验结果表明,与LUO和Anita方法相比,该方法的像素误差精度分别由14.65%、8.30%降至8.02%,且可得到细节信息更好的视差图。     

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.     

Secrets of adaptive support weight techniques for local stereo matching

In recent years, local stereo matching algorithms have again become very popular in the stereo community. This is mainly due to the introduction of adaptive support weight algorithms that can for the first time produce results that are on par with global stereo methods. The crux in these adaptive support weight methods is to assign an individual weight to each pixel within the support window. Adaptive support weight algorithms differ mainly in the manner in which this weight computation is carried out.In this paper we present an extensive evaluation study. We evaluate the performance of various methods for computing adaptive support weights including the original bilateral filter-based weights, as well as more recent approaches based on geodesic distances or on the guided filter. To obtain reliable findings, we test these different weight functions on a large set of 35 ground truth disparity pairs. We have implemented all approaches on the GPU, which allows for a fair comparison of run time on modern hardware platforms. Apart from the standard local matching using fronto-parallel windows, we also embed the competing weight functions into the recent PatchMatch Stereo approach, which uses slanted sub-pixel windows and represents a state-of-the-art local algorithm. In the final part of the paper, we aim at shedding light on general points of adaptive support weight matching, which, for example, includes a discussion about symmetric versus asymmetric support weight approaches.     

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 disparity estimation with a divide-and-conquer disparityspace image technique

A new divide-and-conquer technique for disparity estimation is proposed in this paper. This technique performs feature matching following the high confidence first principle, starting with the strongest feature point in the stereo pair of scanlines. Once the first matching pair is established, the ordering constraint in disparity estimation allows the original intra-scanline matching problem to be divided into two smaller subproblems. Each subproblem can then be solved recursively until there is no reliable feature point within the subintervals. This technique is very efficient for dense disparity map estimation for stereo images with rich features. For general scenes, this technique can be paired up with the disparity-space image (DSI) technique to compute dense disparity maps with integrated occlusion detection. In this approach, the divide-and-conquer part of the algorithm handles the matching of stronger features and the DSI-based technique handles the matching of pixels in between feature points and the detection of occlusions. An extension to the standard disparity-space technique is also presented to compliment the divide-and-conquer algorithm. Experiments demonstrate the effectiveness of the proposed divide-and-conquer DSI algorithm     

提取轮廓图背景控制点的立体匹配算法

针对动态规划匹配算法的误匹配与狭窄遮挡物问题,提出一种在轮廓图中提取背景控制点的立体匹配算法,在立体图像对的轮廓图中选择背景控制点,利用动态规划在视差空间图像中搜索最优路径,根据视差约束以及狭窄遮挡物的判定公式完善视差图。仿真实验结果表明,该算法能够降低在视差不连续区域匹配上的误匹配率。     

Stereo matching using weighted dynamic programming on a single-direction four-connected tree

In recent years, stereo matching based on dynamic programming (DP) has been widely studied and various tree structures are proposed to improve the matching accuracy. However, previous DP-based algorithms do not incorporate all the smoothness functions determined by the edges between the adjacent pixels in the image, which will usually lead to lower matching accuracies. In this paper, we propose a novel stereo matching algorithm based on weighted dynamic programming on a single-direction four-connected (SDFC) tree. The SDFC tree structure is a new tree structure which includes all the edges in the image and the disparity of a pixel can be affected by all the edges in the image. However, in the SDFC tree, conventional DP-based algorithms will make the pixels that are far away from the root node provide higher energy than the nearby pixels, which will decrease the matching accuracy. So, the weighted dynamic programming approach is proposed to optimize the energy function on the new tree structure, and all the pixels in the SDFC tree are treated equivalently. Dynamic programming in the SDFC tree of every pixel in the image separately is very time-consuming, so a fast DP optimization method is designed for the SDFC tree, which reduces the computational complexity of the proposed weighted DP algorithm to 12 times of conventional DP based algorithm. Experiments show that our algorithm not only produces quite smooth and reasonable disparity maps which are close to the state-of-the-art results, but also can be implemented quite efficiently. Performance evaluations on the Middlebury data set show that our method ranks top in all the DP-based stereo matching algorithms, even better than the algorithms that apply segmentation techniques. Experimental results in an unmanned ground vehicle (UGV) test bed show that our algorithm gets very good matching results in different outdoor conditions, even on the asphaltic road which is considered to be textureless. This illustrates the robustness of our algorithm.     

基于地面视差分布的栅格地图建立方法

针对使用立体视觉建立环境地图方法存在信息不完整的问题,提出一种基于地面视差分布的栅格地图建立方法。利用地面视差分布在视差图中进行障碍物和地面点的检测,通过统一但参数值不同的投影模型将障碍物像素和地面点像素投影到栅格地图中,同时考虑立体视觉的量化和匹配误差、地面视差和栅格占据概率的空间分布。通过在非结构化环境中的实验表明,该方法可以实时地建立信息完整且准确的栅格地图。     

一种基于图割的改进立体匹配算法

针对基于图割法的立体匹配算法耗时太长的问题,提出了一种基于简化网格图的立体匹配算法．算法 通过区域匹配算法得到每个像素的初始视差值,然后只保留完整网格图的部分可能的视差值,去除其余大部分的节 点和边缘,建立简化的网格图．该方法大大缩减了网格图的容量,缩短匹配所用时间,并且能够选用更大的视差范 围．实验证明,该算法能够得到比较理想的视差图,而且大大缩短立体匹配所用时间．     

An FPGA stereo matching unit based on fuzzy logic

Stereo matching is one of the most used algorithms in real-time image processing applications such as positioning systems for mobile robots, three-dimensional building mapping and recognition, detection and three-dimensional reconstruction of objects. In order to improve the performance, stereo matching algorithms often have been implemented in dedicated hardware such as FPGA or GPU devices. In this paper an FPGA stereo matching unit based on fuzzy logic is described. The proposed algorithm consists of three stages. First, three similarity parameters inherent to each pixel contained in the input stereo pair are computed. Then, the similarity parameters are sent to a fuzzy inference system which determines a fuzzy-similarity value. Finally, the disparity value is defined as the index which maximizes the fuzzy-similarity values (zero up to d_max). Dense disparity maps are computed at a rate of 76 frames per second for input stereo pairs of 1280 × 1024 pixel resolution and a maximum expected disparity equal to 15. The developed FPGA architecture provides reduction of the hardware resource demand compared to other FPGA-based stereo matching algorithms: near to 72.35% for logic units and near to 32.24% for bits of memory. In addition, the developed FPGA architecture increases the processing speed: near to 34.90% pixels per second and outperforms the accuracy of most of real-time stereo matching algorithms in the state of the art.     

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.     

A stereo matching approach based on particle filters and scattered control landmarks

In robot localization, particle filtering can estimate the position of a robot in a known environment with the help of sensor data. In this paper, we present an approach based on particle filtering, for accurate stereo matching. The proposed method consists of three parts. First, we utilize multiple disparity maps in order to acquire a very distinctive set of features called landmarks, and then we use segmentation as a grouping technique. Secondly, we apply scan line particle filtering using the corresponding landmarks as a virtual sensor data to estimate the best disparity value. Lastly, we reduce the computational redundancy of particle filtering in our stereo correspondence with a Markov chain model, given the previous scan line values. More precisely, we assist particle filtering convergence by adding a proportional weight in the predicted disparity value estimated by Markov chains. In addition to this, we optimize our results by applying a plane fitting algorithm along with a histogram technique to refine any outliers. This work provides new insights into stereo matching methodologies by taking advantage of global geometrical and spatial information from distinctive landmarks. Experimental results show that our approach is capable of providing high-quality disparity maps comparable to other well-known contemporary techniques.     

Automatic selection of MRF control parameters by reactive tabu search

This paper presents an optimization technique to automatically select a set of control parameters for a Markov random field. The method is based on the reactive tabu search strategy, and requires to define a suitable fitness function that measures the performance of the MRF algorithm with a given parameters set. The technique is applied to stereo matching thanks to the availability of ground truth disparity maps. Experiments with synthetic and real images illustrate the approach.     

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。     

Adaptive disparity computation using local and non-local cost aggregations

A new method is proposed to adaptively compute the disparity of stereo matching by choosing one of the alternative disparities from local and non-local disparity maps. The initial two disparity maps can be obtained from state-of-the-art local and non-local stereo algorithms. Then, the more reasonable disparity is selected. We propose two strategies to select the disparity. One is based on the magnitude of the gradient in the left image, which is simple and fast. The other utilizes the fusion move to combine the two proposal labelings (disparity maps) in a theoretically sound manner, which is more accurate. Finally, we propose a texture-based sub-pixel refinement to refine the disparity map. Experimental results using Middlebury datasets demonstrate that the two proposed selection strategies both perform better than individual local or non-local algorithms. Moreover, the proposed method is compatible with many local and non-local algorithms that are widely used in stereo matching.     

Photogrammetric processing of high-resolution airborne and satellite linear array stereo images for mapping applications

This article introduces a mathematical model for photogrammetric processing of linear array stereo images acquired by high-resolution satellite imaging systems such as IKONOS. The experimental result of the generation of simulated IKONOS stereo images based on photogrammetric principles, IKONOS imaging geometry and a set of georeferenced aerial images is presented. An accuracy analysis of ground points derived from the simulated IKONOS stereo images is performed. The impact of the number of GCPs (ground control points), distribution of GCPs, and image measurement errors on the ground point accuracy is investigated. It is concluded that an accuracy of ground coordinates from 2 m to 3 m is attainable with GCPs, and 5 m to 12 m without GCPs. Two data sets of HRSC (high resolution stereo camera) and MOMS (modular opto-electronic multispectral stereo-scanner)-2P are also utilized to test the model and system. The presented data processing method is a key to the generation of mapping products such as digital terrain models (DEM) and digitial shorelines from high-resolution satellite images.