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收稿：2018-03-13，
修回：2018-4-9，
纸质出版：2018-10-16
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目的
2
非刚性物体进行匹配时，往往需要对图像中存在的非刚性形变目标进行快速精确的配准，进而实现对图像的后续处理和分析，实现快速而准确的非刚体匹配显得尤为重要。针对传统特征点匹配方法在非刚性物体匹配中准确性差的问题，本文提出了一种基于DAISY算子和有约束Patch-Match的非刚体密集匹配算法。
方法
2
首先对参考图像和待匹配图像生成DAISY特征描述子，其次对两幅图像进行超像素分割，形成相互邻接但没有重叠的超像素块结构，并以其为单元，计算初始位置上对应每一个像素的DAISY特征算子聚合代价。然后，采用Patch-Match算法对整幅图像进行传播和变异，在变异过程中，通过图像预处理和分析得到的先验知识对位置标签的变异窗口进行局部空间约束，使得每个像素的位置标签在该空间范围内随机更新，计算新的聚合代价，保留代价较小的位置标签，重复迭代此过程，直到聚合代价不发生变化或者达到最大迭代次数为止。
结果
2
实验选取了标准数据集、10幅分别由TFDS（the trucking fault dynamic image detection system）线阵列相机和框幅式相机采集的包含非刚体的图像进行匹配，均取得了较好的匹配效果，经验证，本文方法的匹配精度为86%，误匹配点的平均匹配误差为5个像素左右，是传统基于SIFT特征光流匹配方法误差的一半，并且本文采用的DAISY算子在特征提取速度上是Dense SIFT（dense scale invariant feature transform）特征提取算法的2~3倍，大大提升了图像匹配的效率。
结论
2
本文提出了一种非刚体密集匹配算法，针对非刚体变化的不确定性采用密集特征点进行最优化搜索匹配。本文算法对包含小范围非刚性变化的图像匹配上具有较好的适应性，且匹配精度高，视觉效果好，鲁棒性强。
Objective
2
Feature-based image matching is a fundamental research in the fields of computer vision and pattern recognition.Progress has been achieved in matching rigid objects.However
the fast and accurate registration of non-rigid deformation is often necessary in many practical matching problems to facilitate the subsequent processing and analysis.the existing non-rigid matching algorithms at home and abroad have difficulty in making perfect trade-offs between matching precision
speed
and robustness.Therefore
research on non-rigid matching algorithms that can achieve non-rigid deformation quickly and accurately and obtain nonlinear transformation parameters with an optimization algorithm should be conducted.
Method
2
Our method is based on the SIFT flow algorithm proposed by Ce Liu et al.for stereo matching.The SIFT flow algorithm uses fixed-scale SIFT descriptors densely for the entire image lattice
and thus
it cannot match the scenes containing non-rigid and spatially varying deformations well (e.g.
the scale and rotation changes).Meanwhile
due to the complex construction process of SIFT feature operators
the algorithm has an unsatisfactory real-time performance.In this study
we introduce the DAISY descriptor to replace the SIFT operator.The descriptor not only improves the operator construction speed but also has a good rotation invariance because of its unique circular symmetric structure.This paper presents a non-rigid dense matching algorithm that is based on the DAISY feature descriptor and the constrained patch-match.First
the DAISY feature descriptor is generated for the reference and under-matched images.Second
the reference and under-matched images are segmented to form super-pixel block structures
which are adjacent but non-overlapping.These super-pixel block structures will be used as units for calculating the cost of the DAISY feature descriptor in each pixel at the initial position.Then
each pixel in the entire image undergoes propagation followed by a random search based on the patch-match algorithm.In the process of random search
the initialization window of the local label is localized
and the position tag of each pixel is updated in the space range based on the prior knowledge obtained by the pre-process and analysis.The new aggregation cost is calculated through the above processes
and the position tag with the smaller cost is retained.This process is repeated until the aggregate cost does not change or the maximum number of iterations is reached.
Result
2
A random search should be conducted for each pixel in the entire image to provide more matching possibilities due to the spatially varying deformations of the moving object in the traditional optical flow-based stereo matching method.In this paper
three types of images are selected:the standard test sets provided by the Middlebury visual website
the coupler buffer images taken with TFDS line array cameras
and the non-rigid images collected by frame cameras.These datasets contain non-rigid and a small range of deformations.In this experiment
we perform spatial constraints artificially on the initialization window in the random search of the patch-match algorithm
which avoids the mismatches caused by noise or a lack of texture
instead of randomly searching the entire image.All tests achieve improved match results.The experiments verify that the matching accuracy of our method is 86%
and the average matching error of mismatched points is approximately 5 pixels
which is half of that produced by the traditional SIFT flow matching method.The speed of the DAISY operator adopted in this paper is 1 to 2 times faster than that of the Dense SIFT in features extraction
which greatly improves the image matching efficiency.
Conclusion
2
Traditionally
the entire image should be searched for the best matching points to consider the matching accuracy of the points with large-scale changes
which can result in mismatches.To obtain non-rigid images with small-scale deformation
this paper proposes a non-rigid dense matching algorithm.To deal with the uncertainty of changes in non-rigid images
we use the optimization search algorithm based on dense features.Experiment results indicate that our method is adaptive to the image matching with non-rigid deformation and successfully achieves higher matching accuracy and better visual effects than other methods.
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