Improved Census Transform Method for Semi-Global Matching Algorithm

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

doi:10.23919/ICAC50006.2021.9594147

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 proceeding › Conference contribution › peer-review

5 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 language	English
Title of host publication	2021 26th International Conference on Automation and Computing
Subtitle of host publication	System Intelligence through Automation and Computing, ICAC 2021
Editors	Chenguang Yang
Publisher	IEEE
Number of pages	6
ISBN (Electronic)	9781860435577
ISBN (Print)	9781665443524
DOIs	https://doi.org/10.23919/ICAC50006.2021.9594147
Publication status	Published - 15 Nov 2021
Event	26th International Conference on Automation and Computing - University of Portsmouth, Portsmouth, United Kingdom Duration: 2 Sep 2021 → 4 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

Conference	26th International Conference on Automation and Computing
Abbreviated title	ICAC 2021
Country/Territory	United Kingdom
City	Portsmouth
Period	2/09/21 → 4/09/21
Internet address	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

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@inproceedings{c810399a47444e75a0b719cfe21e7f53,

title = "Improved Census Transform Method for Semi-Global Matching Algorithm",

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. ",

keywords = "Census transform, Census window, sequence templates, SGM, weak texture region",

author = "Xie Pan and Guoben Jun and Yuanping Xu and Zhijie Xu and Tukun Li and Jian Huang and Wenbo Qiao",

note = "Funding Information: ACKNOWLEDGMENT This work is supported by the NSFC (61203172), the Sichuan Science and Technology Programs (2019YFH0187 and 2020018). Publisher Copyright: {\textcopyright} 2021 Chinese Automation and Computing Society in the UK-CACSUK.; 26th International Conference on Automation and Computing, ICAC 2021 ; Conference date: 02-09-2021 Through 04-09-2021",

year = "2021",

month = nov,

day = "15",

doi = "10.23919/ICAC50006.2021.9594147",

language = "English",

isbn = "9781665443524",

editor = "Chenguang Yang",

booktitle = "2021 26th International Conference on Automation and Computing",

publisher = "IEEE",

address = "United States",

url = "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",

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Pan, X, Jun, G, Xu, Y, Xu, Z, Li, T, Huang, J & Qiao, W 2021, Improved Census Transform Method for Semi-Global Matching Algorithm. in C Yang (ed.), 2021 26th International Conference on Automation and Computing: System Intelligence through Automation and Computing, ICAC 2021., 9594147, IEEE, 26th International Conference on Automation and Computing, Portsmouth, United Kingdom, 2/09/21. https://doi.org/10.23919/ICAC50006.2021.9594147

Improved Census Transform Method for Semi-Global Matching Algorithm. / Pan, Xie; Jun, Guoben; Xu, Yuanping et al.

2021 26th International Conference on Automation and Computing: System Intelligence through Automation and Computing, ICAC 2021. ed. / Chenguang Yang. IEEE, 2021. 9594147.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Improved Census Transform Method for Semi-Global Matching Algorithm

AU - Pan, Xie

AU - Jun, Guoben

AU - Xu, Yuanping

AU - Xu, Zhijie

AU - Li, Tukun

AU - Huang, Jian

AU - Qiao, Wenbo

N1 - Conference code: 26

PY - 2021/11/15

Y1 - 2021/11/15

N2 - 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.

AB - 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.

KW - Census transform

KW - Census window

KW - sequence templates

KW - SGM

KW - weak texture region

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DO - 10.23919/ICAC50006.2021.9594147

M3 - Conference contribution

AN - SCOPUS:85123183099

SN - 9781665443524

BT - 2021 26th International Conference on Automation and Computing

A2 - Yang, Chenguang

PB - IEEE

T2 - 26th International Conference on Automation and Computing

Y2 - 2 September 2021 through 4 September 2021

ER -

Improved Census Transform Method for Semi-Global Matching Algorithm

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