A terracotta image partition matching method based on learned invariant feature transform

Jun FENG; Yu-yu YAN; Yan ZHAO; Fang XIAO; Xiao-ning LIU

doi:10.3788/OPE.20182607.1774

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A terracotta image partition matching method based on learned invariant feature transform

Information Sciences | 更新时间：2020-08-13

- A terracotta image partition matching method based on learned invariant feature transform
- Optics and Precision Engineering Vol. 26, Issue 7, Pages: 1774-1783(2018)
- 作者机构：
  
  西北大学信息科学与技术学院, 陕西西安 710127
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20182607.1774
  CLC： TP391
- Received：27 November 2017，
  
  Accepted：24 January 2018，
  
  Published：25 July 2018
- 稿件说明：
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Jun FENG, Yu-yu YAN, Yan ZHAO, et al. A terracotta image partition matching method based on learned invariant feature transform[J]. Optics and precision engineering, 2018, 26(7): 1774-1783.
DOI：

Jun FENG, Yu-yu YAN, Yan ZHAO, et al. A terracotta image partition matching method based on learned invariant feature transform[J]. Optics and precision engineering, 2018, 26(7): 1774-1783. DOI： 10.3788/OPE.20182607.1774.

摘要

针对兵马俑图像三维重建时特征误匹配率较高、效率较低的问题，提出了一种基于两视图兵马俑图像的特征分区匹配方案。该方案在兵马俑图像上，首先利用学习不变特征变换（Learned Invariant Feature Transform，LIFT）方法提取整幅兵马俑图像的特征；接着利用提出的基于先验知识的特征点分布曲线分割方法确定兵马俑头部分割线位置，根据头部分割线将提取的特征分为头部特征和躯干特征；最终采用欧式距离进行分区特征匹配，对于匹配结果集合使用随机抽样一致性算法（Random Sample Consensus，RANSAC）滤除误匹配点集。实验结果表明：在兵马俑图像特征提取及匹配中该方案的正确匹配率可以达到98%，相比于SIFT和SURF方法正确匹配率提高了20%左右，特征点的可重复率提高了10%左右，同时将RANSAC的迭代时间降低了50%，而且在尺度、光照、角度发生变换时具有较好的鲁棒性。因此本文提出的方案能够很好地实现特征点的正确匹配，在兵马俑的三维重建中具有很高的使用价值。

Abstract

A novel feature partition matching scheme for two-view Terracotta warrior images was presented to address the problem of high false matching rate and low feature matching efficiency during 3D reconstruction in this paper. The new scheme was as follows:First

the features of the complete Terracotta warriors image were extracted using the learned invariant feature transform (LIFT) method. Second

the position of the dividing line on the head of the image of the warrior was determined by applying the proposed prior knowledge-based feature point distribution curve

and the extracted features were then divided into head and torso features based on the dividing line. Third

the Euclidean distance was used to perform the regional feature matching

and the random sample consensus (RANSAC) algorithm was subsequently used to filter out the mismatched point set from the matched result set. Experimental results show that in the terracotta image feature extraction and matching

the correct matching rate of the new scheme can reach 98%; the correct matching rate is increased by approximately 20% compared with those of the SIFT and SURF methods

and the repeat rate of the feature points is increased by 10% while the iteration time of RANSAC is decreased by 50%. The new scheme also has better robustness when scale

illumination

and angle are changed in the images. Therefore

the proposed scheme can achieve correct matching of the feature points with sufficient accuracy and has applications in the robust 3D reconstruction of the Terracotta warrior images.

关键词

Keywords

references

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