Image segmentation based on gray-level spatial correlation maximum between-cluster variance

DOI

10.2991/isci-15.2015.28How to use a DOI?

Keywords

Otsu; gray-level spatial correlation; Integral figure; image segmentation

Abstract

When processing the background and target blurred image, 1D-Otsu and 2D-Otsu segmentation effect is not good. The proposed algorithm used the gray value of the pixels and their similarity with neighboring pixels in gray value to build a histogram which was called gray-level spatial correlation histogram. Then threshold value is obtained by calculating GLSC histogram maximum between-class variance. Integral figure was introduced in order to make the time complexity from original to . The experimental results show that the proposed method image segmenting is better than 1D-Otsu and 2D-Otsu, when processing the background and target blurred image.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Zeng Fu
AU  - Jianfeng He
AU  - Yan Xiang
AU  - Rui Cui
AU  - Sanli Yi
PY  - 2015/01
DA  - 2015/01
TI  - Image segmentation based on gray-level spatial correlation maximum between-cluster variance
BT  - Proceedings of the 2015 International Symposium on Computers & Informatics
PB  - Atlantis Press
SP  - 190
EP  - 197
SN  - 2352-538X
UR  - https://doi.org/10.2991/isci-15.2015.28
DO  - 10.2991/isci-15.2015.28
ID  - Fu2015/01
ER  -