Segmentation Using Histogram and Fuzzy Entropy Principle

Jie Zhang, Tao Han, Hongli He, Zanchao Wang

2018

Abstract

Segmentation of a composite image which contains two simple subimages is described. The a-priori knowledge about the two simple subimages is that they possess the maximum amount of entropy. The probability density functions(pdf s) of these image pixels are shown to be of theQuasi-gaussian form. Parameters for the pdf are estimatedand then the maximum likelihood ratio test is applied to segmentation. An iterative algorithm is employed to improve the segmentation accuracy. Extension of this method to the segmentation of images with arbitrary pdf is discussed. This paper presents a thresholding approach by performing fuzzy partition on a two-dimensional (2-D) histogram based on fuzzy relation and maximum fuzzy entropy principle. The experiments with various gray level and color images have demonstrated that the proposed approach outperforms the 2-D non-fuzzy approach and the one-dimensional(1-D) fuzzy partition approach.

Paper Citation

in Harvard Style

Zhang J., Han T., He H. and Wang Z. (2018). Segmentation Using Histogram and Fuzzy Entropy Principle.In Proceedings of the 2nd International Conference on Intelligent Manufacturing and Materials - Volume 1: ICIMM, ISBN 978-989-758-345-2, pages 573-578. DOI: 10.5220/0007536005730578

in Bibtex Style

@conference{icimm18,
author={Jie Zhang and Tao Han and Hongli He and Zanchao Wang},
title={Segmentation Using Histogram and Fuzzy Entropy Principle},
booktitle={Proceedings of the 2nd International Conference on Intelligent Manufacturing and Materials - Volume 1: ICIMM,},
year={2018},
pages={573-578},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007536005730578},
isbn={978-989-758-345-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Intelligent Manufacturing and Materials - Volume 1: ICIMM,
TI - Segmentation Using Histogram and Fuzzy Entropy Principle
SN - 978-989-758-345-2
AU - Zhang J.
AU - Han T.
AU - He H.
AU - Wang Z.
PY - 2018
SP - 573
EP - 578
DO - 10.5220/0007536005730578