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收稿：2018-06-12，
修回：2018-8-21，
纸质出版：2019-02-16
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目的
2
针对直线描述子匹配算法缺乏有效的几何约束，且易受弱纹理、尺度变化的影响，提出一种结合多重约束条件的LBD描述子的直线段匹配算法（LBDs）。
方法
2
该算法以LSD算法提取的直线段作为匹配基元，利用SIFT匹配得到的同名点构建同名三角网约束确定候选直线；参考影像上以目标直线段为中心轴建立该直线段的矩形支撑域；根据目标直线段端点及其支撑域四角点在搜索影像上的核线约束建立候选直线段的对应支撑域；利用仿射变换统一目标直线段及候选直线段支撑域的大小；将直线段支撑域分解为大小相等的条形带，通过计算每个条形带的描述符得到该直线段的描述子，依次完成目标直线段与候选直线段LBD描述子的构建；分别计算目标直线段与每个候选直线段描述子向量间的欧氏距离，将满足最近邻距离比准则的候选直线段作为匹配结果；最后选取角度约束对匹配结果检核，确定同名直线。
结果
2
实验选取网上公开的3组分别存在角度、旋转、尺度变换的近景影像对作为实验数据，采用LBDs分别对其进行直线段匹配实验，并与其他直线段匹配算法进行对比分析，实验结果表明，LBDs获取同名直线数目约为其他算法的1.061.41倍，匹配正确率也提高了2.411.6个百分点，从匹配效率上来看，LBDs更为耗时，但兼顾该算法匹配获得同名直线数目、匹配正确率及运行时间，LBDs的鲁棒性更强，匹配结果的准确性与可靠性较高。
结论
2
结合多重约束条件构建的LBD描述子对于存在角度、旋转和尺度变化的影像进行直线匹配过程中具有稳定性。
Objective
2
A new straight-line matching algorithm for line band descriptors (LBDs) combined with multiple constraints is proposed to solve the typical problems in many straight-line matching algorithms that use descriptors. Such problems include insufficient information utilization between matching straight lines
which are effective geometric constraints
and vulnerability of matching straight lines to the influence of low texture and scale change of images during the matching process.
Method
2
Straight line segments are extracted by using a line segment detector method as matching elements
and a corresponding triangulation network established by using SIFT matching points is then used as the constraint region to determine the candidate lines in the searched image. After the candidate lines are selected
a region for band descriptor construction is constructed. The construction method is described as follows. A rectangular support region
in which the target straight line segment is the central axis in the region
is established in the reference image. Then
the corresponding support region of the candidate straight line segment in the searched image is determined based on epipolar constraints
which is calculated by the endpoints of the target straight line segment and four corner points of its support region in the reference image. The support regions of the target and candidate straight line segments are constructed with the same size by utilizing affine transformation. After completing the support regions of straight line segments
the regions are divided into a set of bands
where each band has the same size and the length of the band equals the length of the straight line segment
and the LBDs of straight line segment are obtained by calculating the information of each band in the support region. The descriptors are calculated based on the gradient values of four directions of pixels
and each band weight coefficient that is along the vertical direction in the support region is controlled by using a Gaussian function. On the basis of the above methods
the matching descriptor construction of LBDs for the target and candidate straight line segments is completed in sequence. Furthermore
new LBDs combined with multiple constraints are normalized in obtaining a unit LBD to reduce the influence of nonlinear illumination changes
and the descriptor is a 40D vector. Euclidean distances are used as the similarity measure in our algorithm and are determined based on the calculated vectors between the target straight line segment and each candidate straight line segment descriptor. The candidate straight line segment
which satisfies the nearest neighbor distance ratio criterion of Euclidean distances
is the matching straight line. In this process
the minimum Euclidean distance and nearest neighbor distance ratio thresholds should be determined
which directly affect the matching performance of the algorithm. Thus
many experiments should be conducted to ensure the accuracy of multi-threshold. The angle constraint
which is between the corresponding straight line and its corresponding epipolar line
is used to evaluate the matching result and determine the final corresponding straight lines.
Result
2
Three typical groups of close-range image pairs with angle
rotation
and scale transformation are used as the experimental dataset
which is used to complete the straight line segment matching experiments by the proposed algorithm. In comparison with other straight line segment matching algorithms
the matching results show that the proposed algorithm is more suitable in different typical close-range image pairs. The conclusions based on the result analysis are summarized as follows. The successful matches of the proposed algorithm have 1.06-1.41 times more lines compared with other straight-line matching algorithms
and the proposed algorithm can improve the accuracy of straight line matching by 2.4% to 11.6%. In terms of matching efficiency
although the proposed algorithm is time-consuming
it is robust and achieves accurate and reliable straight line matching results by synthesizing the relevant experiment results on the number of corresponding matching straight lines
matching accuracy
and running time. Moreover
a highly accurate and reliable matching result is obtained.
Conclusion
2
Constructed LBDs combined with multiple constraints are stable for line matching of close-range images with angles
rotation
and scale changes. The instability of other descriptors caused by numerous factors in line matching is improved.
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