Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 10
3179-3185

Homography estimation method based on multi-scale residual network

Tang Yun1
Shuai Pengfei1
Jiang Peifan1
Deng Fei1
Yang Qiang1,2
1. College of Computer & Network Security(Oxford Brookes College), Chengdu University of Technology, Chengdu 610059, China
2. College of Control Engineering, Chengdu University of Information Technology, Chengdu 610225, China
DOI: 10.19734/j.issn.1001-3695.2022.03.0124

Abstract

Homography estimation is a basic and important step in many computer vision tasks. Traditional homography estimation methods are based on feature point matching, which are difficult to work in weak texture images. Deep learning has been applied to homography estimation to improve its robustness, but the existing methods do not consider the multi-scale problem caused by object scale differences, resulting in limited accuracy. To solve the above problems, this paper proposed a multi-scale residual network for homography estimation. The network could extract the multi-scale feature of the image, and used the multi-scale feature fusion module to effectively fuse the features. In addition, it further reduced the difficulty of network optimization by estimating the four-corner normalized offset. Experiments on MS-COCO dataset show that the average corner error of this method is only 0.788 pixels, which achieves sub-pixel accuracy, and can maintain high accuracy in 99% of cases. Due to the comprehensive utilization of multi-scale features and easier to optimize, this method has significantly improved accuracy and stronger robustness.

Foundation Support

四川省科学技术厅应用基础项目(2021YJ0086)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.03.0124
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 10
Section: Technology of Graphic & Image
Pages: 3179-3185
Serial Number: 1001-3695(2022)10-048-3179-07

Publish History

[2022-05-24] Accepted Paper
[2022-10-05] Printed Article

Cite This Article

唐云, 帅鹏飞, 蒋沛凡, 等. 基于多尺度残差网络的单应估计方法 [J]. 计算机应用研究, 2022, 39 (10): 3179-3185. (Tang Yun, Shuai Pengfei, Jiang Peifan, et al. Homography estimation method based on multi-scale residual network [J]. Application Research of Computers, 2022, 39 (10): 3179-3185. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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