Segmentation of Noisy and Textured Images by an Expectation Maximization and Gradient Descent Iteration Method

DOI

10.2991/nceece-15.2016.66How to use a DOI?

Keywords

Image Segmentation; Expectation Maximization; Gibbs Distribtion

Abstract

Segmentation of noisy and textured images remains challenging in both accuracy and computation efficiency.In this paper, we propose a new method for segmentation of noisy and textured images. The proposed method is based on the famous Expectation Maximization (EM) methodwhich calculates the global parameters of the image and Gibbs distribution which calculates the local parameters of the image.With the global parameters of the objects and the background computed from EM, a pre-segmentation is achieved. Then we propose a gradient descent iteration (GDI) method to achieve the final segmentation by minimizing the sum of local energy.Experimental results show that the proposed method is more effective than the state of art Normalized Cut method in segmenting noisy and textured images.

Copyright

© 2016, 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  - Yang Zhenzhou
PY  - 2015/12
DA  - 2015/12
TI  - Segmentation of Noisy and Textured Images by an Expectation Maximization and Gradient Descent Iteration Method
BT  - Proceedings of the 2015 4th National Conference on Electrical, Electronics and Computer Engineering
PB  - Atlantis Press
SP  - 331
EP  - 336
SN  - 2352-5401
UR  - https://doi.org/10.2991/nceece-15.2016.66
DO  - 10.2991/nceece-15.2016.66
ID  - Zhenzhou2015/12
ER  -