消除图像失配：改进的GMS-PROSAC算法研究

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"这篇研究论文提出了一种改进的GMS-PROSAC算法，目标是消除图像匹配过程中的失配问题。GMS-PROSAC是Guided Matching with Sequential Probability Ratio Test (指导的序列概率比测试匹配)和Progressive Sample Consensus (逐步样本一致性)两种算法的结合，旨在提高图像配准的精度和效率。该算法在计算机视觉和图像处理领域有着广泛的应用，特别是在3D重建、物体识别和场景理解等任务中。原始的PROSAC算法是一种基于随机采样的方法，用于从大量的候选匹配对中找到最符合几何约束的一组对应关系。它通过优先选择最具共识的样本，逐渐剔除不一致的匹配，从而提高了计算效率。然而，PROSAC算法对于图像噪声和局部特征匹配的不准确性比较敏感，容易导致误匹配。 GMS算法则引入了指导匹配的概念，利用图像的上下文信息来帮助筛选匹配对，减少错误匹配的可能性。它通过比较像素邻域的相似性来评估匹配的质量，增强了匹配的鲁棒性。改进的GMS-PROSAC算法在此基础上，进一步优化了匹配过程。可能的改进包括更精确的指导匹配策略、更高效的样本选择机制或者更有效的剔除错误匹配的策略。这些改进有助于在保持算法速度的同时，提高图像配准的准确性和稳定性，尤其是在存在大量失配的情况下。论文作者Panpan Zhao, Derui Ding, Yongxiong Wang和Hongjian Liu详细探讨了这个算法的实现细节，包括算法的数学模型、具体步骤以及实验验证。他们通过对比实验展示了改进算法相对于原算法和其他已知方法的优越性，可能包括更高的匹配精度、更快的收敛速度或者更强的抗干扰能力。总体来说，这项工作对于理解和优化图像匹配过程中的失配问题提供了新的视角，对于图像处理和计算机视觉领域的研究者和实践者具有重要的参考价值。" 这篇研究论文的全文可在指定的链接中找到，包括详细的理论分析、算法实现以及实验结果，读者可以通过访问该链接获取更多信息。

SYSTEMS SCIENCE & CONTROL ENGINEERING: AN OPEN ACCESS JOURNAL 221

developed in Morel and Yu (2009) to improve match-

ing accuracy. But, it is still a time consumption one.

Very recently, an oriented matching algorithm (called as

ORB Rublee, Rabaud, Konolige, & Bradski, 2011) compos-

ing features from accelerated segment test (FAST) and

rotated BRIEF feature (Rublee et al., 2011) discloses the

rapidity in detecting feature points and matching while

achieving the rough real-time performance for sparse fea-

ture point detection. For obtained match feature points in

VSLAM of mobile robots, we found that there are a large

number of false matches, which leads to the inaccuracy of

the pose estimation and the poor robustness.

It is very crucial to eliminate false matches while retain-

ing the matching quality in VSLAM. Typical approaches

include both the Fast Library for Approximate Near-

est Neighbours (FLANN) algorithm (Muja & Lowe, 2009)

and Random Sample Consensus (RANSAC) (Fischler

& Bolles, 1987) algorithm. FLANN is dependent on an arti-

ficial threshold value and RANSAC cannot accommodate

the sheer number of false matches in the set of all nearest-

neighbour matches. Recently, Bian et.al. has proposed

a grid based motion statistics (GMS) algorithm (Bian

et al., 2017) to distinguish some false and true matches.

It should be pointed out that such an algorithm only get

a rough match set by eliminating some false matches of

two images about the same 3D scene. In other words,

there still exist some false matches. As false matches do

not satisfy epipolar geometry constraints (EGCs) (Hart-

ley & Zisserman, 2003; Kushnir & Shimshoni, 2014), it

is possible to develop a new PROSAC algorithm (Chum

& Matas, 2005) by fusing an EGC model to further elim-

inate false matches. EGC, which reveals the relationship

between two views, is a basic concept in image geome-

try. Specially, suppose a point P in the three-dimensional

space can be projected into two image planes, and its

projection is denoted as p and p



in the left and right

images, respectively. This constraint shows that p



must

be located in the p corresponding polar line. In the end,

it can be utilized to get more desired quality matches for

VSLAM.

Responding the above discussion, our paper is con-

cerned with the design of novel mismatch elimination

algorithm via uniting both GMS algorithm and PROSAC

algorithm. We endeavour to deal with the following chal-

lenges: how can we introduce the concept of epipolar

geometry constraint and then utilize it to further elim-

inate mismatches, and how can we design and carry

out a series of experiments to verify the effectiveness of

proposed algorithm. To this end, first, we use the ORB

algorithm (Rublee et al., 2011) to describe feature points

and get a rough matching set with the help of hamming

distance method. Then, a new algorithm via the epipolar

geometric constraint is designed to get a large number

of high-quality matching pairs. In comparison with the

traditional ORB algorithm (Rublee et al., 2011) combined

with FLANN (Muja & Lowe, 2009) and RANSAC (Fischler

& Bolles, 1987), the proposed algorithm should possess

higher real-time performance, higher matching precision

and more number of high-quality matches. Our main

contributions can be highlighted as follows: (1) A new

epipolar geometric constraint model, combining with a

projection error function, is employed to further eliminate

mismatches; (2) an improved GMS-PROSAC algorithm,

named as GMS-EGCPROSAC algorithm, is put forward to

enhance the real-time performance and the matching

precision, and to increase the number of high-quality

matching; and (3) in comparison with the GMS, ORB, SIFT,

SURF algorithms with a standard ratio-test, the effective-

ness of proposed algorithm is adequately demonstrated

by a series of experiments.

The rest sections are organized as follows. In Section 2,

we introduce the ORB image matching algorithm includ-

ing both the ORB feature detection and the ORB feature

descriptor. In Section 3, we briefly summarize the GMS

algorithm and then put forward to an improved PROSAC

algorithm by adding an epipolar geometric constraint. Its

flow chart and the algorithm steps are provided in details.

In Section 4, we verified the validity of our algorithm

through experiments. Finally, a conclusion is drawn in

Section 5.

2. ORB image matching algorithm

In this section, we introduce the ORB image match

algorithm, which consists of oriented FAST and rotated

BRIEF feature match algorithms.

2.1. Oriented FAST

Oriented FAST is commonly obtained by adding a direc-

tion information to the well-known original FAST pro-

posed by Rosten and Drummond (2006). It should be

pointed out that FAST has neither the direction invariance

nor the scale invariance. Firstly, an image pyramid (Klein

& Murray, 2008) should be built for oriented FAST in order

to achieve the scale invariance, and then the FAST corner

is detected at each level of such a pyramid. Furthermore,

the direction of feature points can be obtained by cal-

culating the centre of mass of image blocks via the grey

matter method. In what follows, specific steps of the ori-

ented FAST are shown with the help of an example, see

Figure 1. In this figure, the right sub-figure shows a dis-

sociated Bresenham ring around the centre corner candi-

date p, where the red squares are the pixels used in the

FAST corner detection and the arc plotted by dashed line

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