Improved Census Transform Method for Semi-Global Matching Algorithm

Xie Pan, Guoben Jun, Yuanping Xu, Zhijie Xu, Tukun Li, Jian Huang, Wenbo Qiao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

In order to solve the problem that the original Census transform algorithm is easily affected by the gray value of the central pixel, and further improvement of the matching accuracy of SGM algorithm, this study proposes an improved Census transform algorithm based on the sequence template. It divides the pixel values in the Census window into sequential sequences from the outside to inside, and then constructs Census encoding templates within and between sequences according to predefined rules in this study, such that the Census value of the center pixel can contain more information within the window, and can further improve the matching accuracy of SGM. Experimental results show that the improved sequence template-based Census transform method can enhance average 16.26% matching accuracy of SGM algorithm, while preserving its efficiency.

Original languageEnglish
Title of host publication2021 26th International Conference on Automation and Computing
Subtitle of host publicationSystem Intelligence through Automation and Computing, ICAC 2021
EditorsChenguang Yang
PublisherIEEE
Number of pages6
ISBN (Electronic)9781860435577
ISBN (Print)9781665443524
DOIs
Publication statusPublished - 15 Nov 2021
Event26th International Conference on Automation and Computing - University of Portsmouth, Portsmouth, United Kingdom
Duration: 2 Sep 20214 Sep 2021
Conference number: 26
http://www.cacsuk.co.uk/index.php/icac2021
https://www.ieee-ras.org/conferences-workshops/technically-co-sponsored/icac
https://ieeexplore.ieee.org/xpl/conhome/9594055/proceeding

Conference

Conference26th International Conference on Automation and Computing
Abbreviated titleICAC 2021
Country/TerritoryUnited Kingdom
CityPortsmouth
Period2/09/214/09/21
Internet address

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