An image threshold estimation model

Rongteng Wu; Xiaohong Xie; Zeyun Song

doi:10.1117/12.871813

9 November 2010 An image threshold estimation model

Rongteng Wu, Xiaohong Xie, Zeyun Song

Proceedings Volume 7850, Optoelectronic Imaging and Multimedia Technology; 78500A (2010) https://doi.org/10.1117/12.871813
Event: Photonics Asia 2010, 2010, Beijing, China

Abstract

The Guassian distribution model is often used to characterize the statistical behavior of image or other multimedia signal, and applied in fitting probability density functions of a signal. But, in practically, the probability density function of data source may be inherently non-Gaussian. As the distribution family covers most of the common distribution types and the frequency curves provided by the family are as wide as in general use, this paper considers Johnson distribution family to estimate the unknown parameters and approximate the empirical distribution. The method uses the moments to initialize the parameters of the distribution family, and then calculates parameters by using EM algorithm. The experiment results show that the fitted model could depicts quite successfully the both Gaussian and non-Gaussian probability density function of image intensity, and comparatively the method has low computing complexity.

Citation Download Citation

Rongteng Wu, Xiaohong Xie, and Zeyun Song "An image threshold estimation model", Proc. SPIE 7850, Optoelectronic Imaging and Multimedia Technology, 78500A (9 November 2010); https://doi.org/10.1117/12.871813

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available