基于图像全局矢量的特征点矢量提取方法

邹彬; 陆阳; 钱钧; 贺峻峰; 李良福; 杨一洲

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基于图像全局矢量的特征点矢量提取方法

更新时间：2026-09-04

- 基于图像全局矢量的特征点矢量提取方法
- Binary Image Feature Extraction Method Based on the Image Global Binary Vector
- 微电子学与计算机 2017年34卷第1期页码：58-61
- 作者机构：
  
  西安应用光学研究所,陕西,西安,710065
- 作者简介：
- 基金信息：
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  中图分类号：
- 纸质出版：2017
- 稿件说明：
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邹彬, 陆阳, 钱钧, 贺峻峰, 李良福, 杨一洲. 基于图像全局矢量的特征点矢量提取方法[J]. 微电子学与计算机, 2017, 34(1): 58-61.

ZOU Bin, LU Yang, QIAN Jun, et al. Binary Image Feature Extraction Method Based on the Image Global Binary Vector[J]. 2017, 34(1): 58-61.
邹彬, 陆阳, 钱钧, 贺峻峰, 李良福, 杨一洲. 基于图像全局矢量的特征点矢量提取方法[J]. 微电子学与计算机, 2017, 34(1): 58-61. DOI：

ZOU Bin, LU Yang, QIAN Jun, et al. Binary Image Feature Extraction Method Based on the Image Global Binary Vector[J]. 2017, 34(1): 58-61. DOI：

摘要

针对传统二进制特征描述子提取算法复杂

鲁棒性较差等缺点

提出了一种具有一定全局参考性及鲁棒性的二进制特征矢量; 首先

对输入的匹配图像求取其Harris特征点及其显著图; 然后

将图像空间网格化

求取各网格中心

并按其对应网格的显著度进行排序

生成全局基准矢量; 最后

将各个特征点与全局基准矢量进行比较

生成基于显著度加权的二进制特征矢量.实验结果表明

该算法提取的二进制特征在极大减少计算的同时

还具有良好的特征表征能力.

Abstract

To overcome the weakness of algorithm complexity and less robust by traditional binary feature extraction algorithm

this paper presents a novel binary feature extraction method with certain global reference and robustness. Firstly

Harris feature points and Saliency map are obtained from the input matching image. Secondly

the image space is partitioned into grid in order to get the grid center

which is sorted according to the saliency degree of the corresponding grid to generate the global reference vector. Finally

the feature points are compared with the global reference vector to generate the binary feature based on the saliency weighted. Experimental results show that the proposed algorithm can greatly reduce the computational burden

and has a good ability of characterization.

关键词

Keywords

references

Bay H, Tuytelaars T, Van G. SURF: speeded up robust features[C]//Proceedings of the 9th European Conference on Computer Vision Graz. Berlin: Springert, 2006, 3951: 404-417.

Mikolajczyk K, Schmid C. A performance evaluation of local descriptors[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2005(27): 1615-1630. DOI: 1615

Lowe D G. Distinctive image features from scale-invariant key-points[J]. International Journal of Computer Vision, 2004, 60: 91-110. DOI: 91

Chan K W, Chan K L. Optimisation of multi-level block truncation coding[J]. Signal Processing: Image Communication, 2001, 16: 445-459. DOI: 445

Harris C, Stephens M. A combined corner and edge detector[C]//Proceedings of Alvey Vision Conference. Manchester, Britain: BMVA, 1988: 147-151.

Alahi A, Ortiz R, Vandergheynst P. FREAK: fast retina key-point[C]//Proceedings of the 25th IEEE Conference on Computor Vision and Pattern Recognition. Rhode Island, Providence, USA: IEEE, 2012: 16-21.

Calonder M, Lepetit V, Ozuysal M, et al. BRIEF: computing a local binary descriptor very fast[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34: 1281-1298. DOI: 1281

Itti L, Koch C, Niebur E. A model of saliency-based visual attention for rapid scene analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20 (11): 1254-1259. DOI: 1254

尚明姝. 一种基于改进SURF的图像匹配方法[J]. 微电子学与计算机, 2014, 31(2): 125-128. DOI: 125

庄东晔, 张冬明, 张勇东, 等. 随机亮度差量化的二进制特征描述[J]. 中国图象图形学报, 2014, 19(4): 630-636. DOI: 630

刘辉, 申海龙. 一种基于基于改进SIFT算法的图像配准方法[J]. 微电子学与计算机, 2014, 31(1): 38-42. DOI: 38

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