Image Segmentation with Multilevel Threshold of Gray-Level & Gradient-Magnitude Entropy Based on Genetic Algorithm

DOI

10.2991/aiie-15.2015.144How to use a DOI?

Keywords

genetic algorithm; gray-level & gradient-magnitude entropy; multi-threshold segmentation; image segmentation

Abstract

Due to consider the gray level spatial distribution information, some image segmentation technologies based on entropy threshold can enhance the thresholding segmentation performance. However, they still cannot distinguish image edges and noise well. Even though GLGM(gray-level & gradient-magnitude) entropy can effectively solve the problem, but it cannot segment effectively multi-objective and complex image. In this paper, a GLGM entropy fast segmentation method based on GA is presented by combining Real-code-GA and GLGM entropy, and the single threshold segmentation of GLGM entropy is further extended to multilevel threshold segmentation. Our method compared with GLGM entropy multi-threshold exhaustive method, the segmentation result obtained by our method is basically the same as the result obtained by exhaustive method.

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  - Z. Fu
AU  - J.F. He
AU  - R. Cui
AU  - Y. Xiang
AU  - S.L. Yi
AU  - S.J. Cao
AU  - Y.Y. Bao
AU  - K.K. Du
AU  - H. Zhang
AU  - J.X. Ren
PY  - 2015/07
DA  - 2015/07
TI  - Image Segmentation with Multilevel Threshold of Gray-Level & Gradient-Magnitude Entropy Based on Genetic Algorithm
BT  - Proceedings of the 2015 International Conference on Artificial Intelligence and Industrial Engineering
PB  - Atlantis Press
SP  - 539
EP  - 542
SN  - 1951-6851
UR  - https://doi.org/10.2991/aiie-15.2015.144
DO  - 10.2991/aiie-15.2015.144
ID  - Fu2015/07
ER  -