Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/3592686.3592703acmotherconferencesArticle/Chapter ViewAbstractPublication PagesbicConference Proceedingsconference-collections
research-article

Research based on improved AD-Census stereo matching algorithm

Published: 31 May 2023 Publication History

Abstract

Aiming at the problem that the synthesis of AD and Census matching costs of AD-Census transformation cannot reflect the weight and central pixel dependence of pixels in the image area, an improved AD-Census stereo matching algorithm is proposed. The algorithm selects the reference pixel values by assigning the distance similarity of the pixels in the support window, and sets the threshold range based on the center pixel difference, and then calculates the cost of AD transformation cost and Census transformation cost composite weight. By using a semi-global aggregation algorithm based on dynamic programming, SGM (Semi-global matching) algorithm, the matching cost information is fused. The results of the Middlebury stereo matching evaluation platform show that compared with the binocular stereo matching performance of the improved AD-Census transform compared with the binocular stereo matching performance based on AD-Census transform, the parallax error of pixels is significantly optimized, and by adding noise interference, it can be seen that the anti-interference performance of the improved AD-Census algorithm is improved.

References

[1]
ZHANG Yifei, LI Xinfu, TIAN Xuedong. SAD stereo matching algorithm based on edge features [J]. Computer Engineering, 2020, 46(04): 236-240+246.
[2]
ZHOU Zhe, SHEN Jianxin, HAN Peng, JIANG Junjia. Stereo matching algorithm based on Census transform and guided filtering [J]. Journal of Applied Optics, 2020, 41(01): 79-85.
[3]
HUANG Bin, HU Likun, ZHANG Yu. Improved Census stereo matching algorithm based on adaptive weights [J]. Computer Engineering, 2021, 47(05): 189-196.
[4]
WANG Yunfeng, WU Wei, YU Xiaoliang, WANG Anran. Binocular stereo matching based on adaptive weight AD-Census transform [J]. Engineering Science and Technology, 2018, 50(04): 153-160.
[5]
Scharstein D, Szeliski R.A taxonomy and evaluation of dense two-frame stereo correspondence algorithms[J]. International Journal on Computer Vision, 2002, 47(1/2/3): 7-42.
[6]
LU, Z. A Resource-Efficient Pipelined Architecture for Real-Time Semi-Global Stereo Matching. IEEE Transactions on Circuits & Systems for Video Technology, [s.l.], v.32, n.2, p.660-673, 2022. DOI10.1109/TCSVT.2021.3061704.
[7]
WANG Dan, YANG Jiaqi, YANG Xieliu. Research on image stereo matching algorithm based on improved Census transformation [J]. Machinery and Electronics, 2021, 39(05): 62-67.
[8]
ZHU Jianhong, WANG Caosong, GAO Meifeng. An improved matching algorithm for Census transform and adaptive window[J]. Progress in Laser and Optoelectronics, 2021, 58(12): 427-434.
[9]
JIANG Hong. Research on three-dimensional display and stereo matching algorithm of binocular medical endoscopy system[D]. Zhejiang University, 2014.
[10]
Wang Guodong. Research on the application of medical visualization based on binocular stereo vision [D]. Northwest University, 2011.
[11]
LIU Zhenyou, ZHENG Xiying, CHENG Shuying. Anti-noise stereo matching algorithm based on improved Censustransform [J]. Semiconductorptoelectronics, 2021, 42(01): 100-105.
[12]
GUO Bei. Research on surgical instrument positioning technology based on binocular vision[D]. Jiangxi University of Science and Technology, 2019.
[13]
WANG Yunfeng, WU Wei, YU Xiaoliang, Binocular stereo matching based on adaptive weight AD-Census transform [J]. Engineering Science and Technology, 2018, 50(4):153-160.
[14]
FANG Chunjie, LAI Xiaobo, SHI Lei. Optimization of stereo matching algorithm based on Census transform and application of medical imaging [J]. Journal of Zhengzhou University (Medical Sciences), 2017, Vol. 52(2): 146-150.
[15]
Wang Di, Hu Liaolin. Improved stereoscopic matching method of features based on binocular vision [J]. Acta Electronica Sinica, 2022, 50(01):157-166.
[16]
Pan Weihua, Meng Yuanyuan, Su Pan. Adaptive filtering stereo matching algorithm for fusing edge features [J]. Progress in Laser and Optoelectronics, 2022, Vol. 59(8): 426-433.

Cited By

View all

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
BIC '23: Proceedings of the 2023 3rd International Conference on Bioinformatics and Intelligent Computing
February 2023
398 pages
ISBN:9798400700200
DOI:10.1145/3592686
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 May 2023

Permissions

Request permissions for this article.

Check for updates

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

BIC 2023

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 37
    Total Downloads
  • Downloads (Last 12 months)22
  • Downloads (Last 6 weeks)3
Reflects downloads up to 03 Oct 2024

Other Metrics

Citations

Cited By

View all

View Options

Get Access

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media