双目立体匹配算法的研究与进展

立体匹配一直是计算机视觉领域的一个中心研究问题.首先综合介绍了立体匹配算法的研究概况,论述了双目立体匹配算法中各种约束的核心概念和适用范围;然后重点归纳分析了立体匹配算法的分类及其发展过程中的各种演化算法,对其关键技术进行了剖析和比较,并总结了目前存在的主要难题和可能的解决途径;最后对该领域存在的问题和技术发展趋势进行了分析和讨论.     

彩色图像对自适应匹配算法

提出了两种方法来提高图像匹配的精度。一种方法是改变自适应匹配窗口的选择方法,与原来的矩形窗口相比较,提出的窗口选取方法能在低纹理区域得到包含有效信息量更多、更接近实际边界的窗口。此外,通过降低边界点相似度在计算过程中的权重来降低边界点影响力,使得视差不连续区域的匹配精度得到提高。算法采用了Middlebury网站上提供的四幅立体图像对Tsukuba、Venus、Teddy和Cones分别进行实验验证。实验表明文中提出的两种方法对四幅图像的匹配精度均有所提高。     

An automatized method to parameterize embedded stereo matching algorithms

Many applications rely on 3D information as a depth map. Stereo Matching algorithms reconstruct a depth map from a pair of stereoscopic images. Stereo Matching algorithms are computationally intensive, that is why implementing efficient stereo matching algorithms on embedded systems is very challenging for real-time applications.Indeed, like many vision algorithms, stereo matching algorithms have to set a lot of parameters and thresholds to work efficiently. When optimizing a stereo-matching algorithm, or changing algorithms parts, all those parameters have to be set manually. Finding the most efficient solution for a stereo-matching algorithm on a specific platform then becomes troublesome.This paper proposes an automatized method to find the optimal parameters of a dense stereo matching algorithm by learning from ground truth on a database in order to compare it with respect to any other alternative.Finally, for the C6678 platform, a map of the best compromise between quality and execution time is obtained, with execution times that are between 42 ms and 382 ms and output errors that are between 6% and 9.8%.     

Local algorithms on dense two-frame stereo matching

Stereo matching is a fundamental and crucial problem in computer vision. In the last decades, many researchers have been working on it and made great progress. Generally stereo algorithms can be classified into local methods and global methods. In this paper, the challenges of stereo matching are first introduced, and then we focus on local approaches which have simpler structures and higher efficiency than global ones. Local algorithms generally perform four steps: cost computation, cost aggregation, disparity computation and disparity refinement. Every step is deeply investigated, and most work focuses on cost aggregation. We studied most of the classical local methods and divide them into several classes. The classification well illustrates the development history of local stereo correspondence and shows the essence of local matching along with its important and difficult points. At the end we give the future development trend of local methods.     

双目视觉的匹配算法综述

双目视觉是获取对现实世界立体感知的重要方法,在自动驾驶等领域得到了普遍 的应用。立体匹配是实现双目感知的前提,该算法对左右摄像机拍摄的照片进行像素级的匹配, 生成稠密视差图,从而获取了三维坐标信息。概述了立体匹配算法近 20 年来的发展过程,围绕 基于人工特征和深度学习两个方向进行了综述,对算法实现过程中的代价计算、代价聚合、视 差计算和视差求精进行分析讨论,评估了算法的准确性和时间复杂度。最后总结了立体匹配算 法面对的挑战和对未来发展的展望。     

A 1D approach to correlation-based stereo matching

In stereovision, indices allowing pixels of the left and right images to be matched are basically one-dimensional features of the epipolar lines. In some situations, these features are not significant or cannot be extracted from the single epipolar line. Therefore, many techniques use 2D neighbourhoods to increase the available information. In this paper, we discuss the systematic use of 2D neighbourhoods for stereo matching. We propose an alternative approach to stereo matching using multiple 1D correlation windows, which yields a semi-dense disparity map and an associated confidence map. A particular technique derived from this approach — using fuzzy filtering and a basic decision rule — is compared to about 80 other methods on the Middlebury image datasets [1]. Results are first presented in the framework of the Middlebury website, then on the Receiver Operating Characteristics (ROC) evaluation [2] and, finally, on stereo image pairs of slanted surfaces. We show that a 1D correlation window is sufficient to provide correct matchings in most cases.     

Learning and feature selection in stereo matching

We present a novel stereo matching algorithm which integrates learning, feature selection, and surface reconstruction. First, a new instance based learning (IBL) algorithm is used to generate an approximation to the optimal feature set for matching. In addition, the importance of two separate kinds of knowledge, image dependent knowledge and image independent knowledge, is discussed. Second, we develop an adaptive method for refining the feature set. This adaptive method analyzes the feature error to locate areas of the image that would lead to false matches. Then these areas are used to guide the search through feature space towards maximizing the class separation distance between the correct match and the false matches. Third, we introduce a self-diagnostic method for determining when apriori knowledge is necessary for finding the correct match. If the a priori knowledge is necessary then we use a surface reconstruction model to discriminate between match possibilities. Our algorithm is comprehensively tested against fixed feature set algorithms and against a traditional pyramid algorithm. Finally, we present and discuss extensive empirical results of our algorithm based on a large set of real images     

Reliability analysis of the rank transform for stereo matching

The rank transform is a nonparametric technique which has been recently proposed for the stereo matching problem. The motivation behind its application to this problem is its invariance to certain types of image distortion and noise, as well as its amenability to real-time implementation. This paper derives one constraint which must be satisfied for a correct match. This has been termed the rank constraint. Experimental work has shown that this constraint is capable of resolving ambiguous matches, thereby improving matching reliability. A novel matching algorithm incorporating the rank constraint has also been proposed. This modified algorithm consistently resulted in an increased percentage of correct matches, for all test imagery used. Furthermore, the rank constraint has been used to devise a method of identifying regions of an image where the rank transform, and hence matching outcome, is more susceptible to noise. Experimental results have shown that the errors predicted using this technique are consistent with the actual errors which result when images are corrupted with noise. Such a method could be used to identify matches which are likely to be incorrect and/or provide a measure of confidence in a match.     

The non-parametric Parzen''s window in stereo vision matching

This paper presents an approach to the local stereovision matching problem using edge segments as features with four attributes. From these attributes we compute a matching probability between pairs of features of the stereo images. A correspondence is said true when such a probability is maximum. We introduce a nonparametric strategy based on Parzen's window (1962) to estimate a probability density function (PDF) which is used to obtain the matching probability. This is the main finding of the paper. A comparative analysis of other recent matching methods is included to show that this finding can be justified theoretically. A generalization of the proposed method is made in order to give guidelines about its use with the similarity constraint and also in different environments where other features and attributes are more suitable.     

基于SAD算法的立体匹配的实现

利用FPGA并行性计算和合理的流水线设计完成了立体视觉中最核心的部分——立体匹配以及硬件结构,选取SAD区域立体匹配算法,利用补码来实现SAD算法,在算法流程中采用窗口并行和像素串行来完成.在获得视差图时,采用128×128图像对,窗口大小为3×3,视差为24,在系统时钟为50 MHz情况下,实现了每秒425帧的处理速度,最后给出了视差图.实验证明,选用FPGA来实现立体匹配系统的设计是可行的,具有一定的鲁棒性.     

An improved graph cut algorithm in stereo matching

In stereo matching tasks, the matching effect is often very poor when the texture of the region is weak or repeated. To solve this problem, an improved Graph Cut stereo matching algorithm based on Census transform is proposed. The Hamming distance of the corresponding pixels in the left and right images after Census transform is introduced as the similarity measure in the data term of the energy function. In this way, the dependence on the pixel value is reduced. The stereo matching experiments are implemented on the standard images of Middlebury stereo benchmark and the real scene images, and it demonstrates that our algorithm is robust and can obtain better performance in weak texture or repeated texture region.     

A stereo matching paradigm based on the Walsh transformation

Describes a new feature-based stereo matching technique which exploits the Walsh transformation. The established matching strategy adopts and integrates the fundamental steps of the stereo vision problem: (a) detecting and locating feature points, (b) searching for potential matches, (c) validating a match through a global consistency check, and (d) determining the disparity of the matched feature points. The unique representation of stereo images into Walsh-based attributes unites the aforementioned steps into an integrated process which yields accurate disparity extraction. It is shown that the first and second Walsh attributes are used as operators approximating the first and second derivatives for the extraction and localization of feature points. The complete set of these attributes are then used as matching primitives contributing equally to the decision-making process and providing relevant information on both the characterization of a potential match and its validation through a consistency check. Computer results using images of varying complexities prove the soundness and the relatively fast processing time of this stereo matching technique     

双目立体匹配图像对的预处理研究

在进行双目立体匹配过程中，对获取的双目立体图像对，分别采取邻域平均和中值滤波的方法进行预处理研究，从实验仿真结果得出，在双目立体图像对求取视差场前采用高斯模板对其进行滤波预处理后，能较好地改善视差场的质量，提高视差场的精度，并对该预处理在工程实现中的一些问题进行了分析研究。     

彩色图像非参数变换立体匹配研究

将非参数Rank变换引用到彩色图像的立体配算法中,采用绝对值指数方法来计算像素的颜色相似性,对彩色图像进行一种分等级的改进Rank变换,将彩色图像转变了一个整数矩阵,然后建立一种新的匹配测度,同时加入色差梯度约束进行匹配。通过对比实验,表明这种新的匹配基元进行匹配,与直接采用灰度特征作为匹配基元的算法相比,有更好的抗噪性和匹配准确性。     

双目立体视觉中的图像匹配方法研究

对目前匹配能力很强的基于SIFT特征的图像匹配方法进行研究,并在该方法中加入极线约束,有效去除了大部分虚假匹配。提出以特征匹配与区域匹配相结合、边缘特征与角点特征相结合的立体匹配方法。实验证明该方法不仅能够有效地缩短匹配时间,还能达到较高的匹配精度。     

Automatic feature matching in uncalibrated stereo vision through the use of color

One difficult problem in stereo vision is how to establish correspondence between features extracted from a pair of images. The difficulty is due to ambiguities or inconsistencies of available information on images. In this paper, we invetigate stereo correspondence problem in the framework of color stereo vision. We propose the use of a matching consistence (MC) constraint in RGB color space and the generalized epipolar geometry to develop an automatic feature matching algorithm.     

双目立体图像的快速匹配值计算方法

立体视觉计算过程中的匹配值计算极其耗时,整幅图的匹配值由各个参考像点的匹配值计算构成,因此,如果能够将前后两次参考像点的匹配值计算中重复的部分提取出来,那么就可以节约部分计算时间。将前后两次匹配值计算所涉及的领域像素分布情况分析清楚,结合所选取的计算步骤,将各个子步骤的中间输出结果缓存起来,以便下一次计算时直接使用而不需重新计算。如果匹配所用的邻域子图与整幅图相比非常小,则这种类似于流水作业的时间重叠计算方式可以获得很好的加速性能,同时该加速性能也会随着邻域尺寸的加大而提高,这得益于所省略的中间计算步骤较多的原因。     

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。     

Similarity measures for image matching despite occlusions in stereo vision

In the context of computer vision, matching can be done with similarity measures. This paper presents the classification of these measures into five families. In addition, 18 measures based on robust statistics, previously proposed in order to deal with the problem of occlusions, are studied and compared to the state of the art. A new evaluation protocol and new analyses are proposed and the results highlight the most efficient measures, first, near occlusions, the smooth median powered deviation, and second, near discontinuities, a non-parametric transform-based measure, CENSUS.     

一种改进的模版匹配识别算法

通过对现有常用的几种模板匹配算法的分析与研究,提出了一种全区域特征加权模板匹配识别算法,它是对特征加权的模板匹配算法的一种改进。经过理论分析与实际测试,这种改进的识别算法进一步降低了字符的误识率。