Point-pattern matching based on point pair local topology and probabilistic relaxation labeling

Deng, Wanxia; Zou, Huanxin; Guo, Fang; Lei, Lin; Zhou, Shilin

doi:10.1007/s00371-016-1311-3

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…Taha 等 [59] 提出了一种计算精确 Hausdorff 距离的方法, 计算复杂度接近线性, 在大数据集的计算中表现良好. Chen 等 [60] 提出了局部起始点搜索算法 (LLS) 计算 Hausdorff 距离, 利用了点集的空间位置信息, 显著降低了计算复杂度. Deng 等 [61] 提出了点对局部拓扑特征 (point-to-point local topology, PPLT), 结合不变特征和概率松弛改进了点模式匹配算法 (PPM), 提高了匹配的效率和准确度.…”

Section: 图像匹配unclassified

Review of scene matching visual navigation for unmanned aerial vehicles

et al. 2019

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Section: 图像匹配unclassified

Review of scene matching visual navigation for unmanned aerial vehicles

et al. 2019

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“…The DIR is defined as the ratio of the length of the line segment inner the shape and the length of the line segment. Deng et al [28] proposed a local feature descriptor, point pair local topology (PPLT). The PPLT is a histogram which is constructed by the weighting of distance measures and angle measures based on local pairs.…”

Section: Related Workmentioning

confidence: 99%

Shape Matching Based on Multi-Scale Invariant Features

Zhang

Xin-guo

2019

IEEE Access

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Shape matching has been extensively used in various fields. The local feature-based or global feature-based algorithms can hardly describe the shape comprehensively due to their inherent defects. Combining the local and global feature to describe the shape has become a trend. In this paper, an improved discrete curve evolution algorithm is proposed which combines the discrete curve evolution with the uniform sampling and achieves a better description of the shape contour. Three simple and intuitive multi-scale features which represent both the global and local features of shapes are designed from aspects of the spatial relationship of contour points, structural information of contour sequence, and shape geometry feature. A cyclic Smith-Waterman algorithm is introduced to solve local contour matching and starting point selection. Experimental results demonstrate that our proposed features are translation, rotation, and scaling invariant, and have good robustness to deformation. Retrieval accuracies of Kimia99, Kimai216, and MPEG-7 indicate that our method can bring out a better performance. INDEX TERMS Shape matching, improved discrete curve evolution, multi-scale invariant features, cyclic Smith-Waterman algorithm. I. INTRODUCTION

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“…Additionally, the problem existed where the correctly matched point might have been close to the corresponding point but was not extracted in the feature extraction stage. Given these problems, we used the probability relaxation method [30,31] to match those feature points. Probability relaxation matching takes advantage of the constraints from surrounding matches to enhance robustness against geometric distortion, and uses the points around the predicted point as candidate points to increase the probability of finding the correct matching point, which greatly improves the matching success rate.…”

Section: Probabilistic Relaxation Matchingmentioning

confidence: 99%

Matching Multi-Source Optical Satellite Imagery Exploiting a Multi-Stage Approach

Liu

Tao

2017

Remote Sensing

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Geometric distortions and intensity differences always exist in multi-source optical satellite imagery, seriously reducing the similarity between images, making it difficult to obtain adequate, accurate, stable, and well-distributed matches for image registration. With the goal of solving these problems, an effective image matching method is presented in this study for multi-source optical satellite imagery. The proposed method includes three steps: feature extraction, initial matching, and matching propagation. Firstly, a uniform robust scale invariant feature transform (UR-SIFT) detector was used to extract adequate and well-distributed feature points. Secondly, initial matching was conducted based on the Euclidean distance to obtain a few correct matches and the initial projective transformation between the image pair. Finally, two matching strategies were used to propagate matches and produce more reliable matching results. By using the geometric relationship between the image pair, geometric correspondence matching found more matches than the initial UR-SIFT feature points. Further probability relaxation matching propagated some new matches around the initial UR-SIFT feature points. Comprehensive experiments on Chinese ZY3 and GaoFen (GF) satellite images revealed that the proposed algorithm performs well in terms of the number of correct matches, correct matching rate, spatial distribution, and matching accuracy, compared to the standard UR-SIFT and triangulation-based propagation method.

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Point-pattern matching based on point pair local topology and probabilistic relaxation labeling

Cited by 5 publications

References 23 publications

Review of scene matching visual navigation for unmanned aerial vehicles

Review of scene matching visual navigation for unmanned aerial vehicles

Shape Matching Based on Multi-Scale Invariant Features

Matching Multi-Source Optical Satellite Imagery Exploiting a Multi-Stage Approach

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