New stochastic sampling method for region extraction: theory and experiments

Taizo Anan; Makoto Ohtsu; Hiroyuki Kudo; Tsuneo Saito

doi:10.1117/12.253443

8 October 1996 New stochastic sampling method for region extraction: theory and experiments

Taizo Anan, Makoto Ohtsu, Hiroyuki Kudo, Tsuneo Saito

Proceedings Volume 2823, Statistical and Stochastic Methods for Image Processing; (1996) https://doi.org/10.1117/12.253443
Event: SPIE's 1996 International Symposium on Optical Science, Engineering, and Instrumentation, 1996, Denver, CO, United States

Abstract

We propose a region extraction method based on a new energy function and a new stochastic sampling method. The new energy function is based on the mixed density description derived by clustering an input image using the ISO DATA algorithm. Our energy function is suitable for natural images. We developed a new stochastic sampling method by modifying the conventional Gibbs sampler. The conventional Gibbs sampler converges to global optimum of the energy function, but is cannot be applied to region extraction because of its inability to preserve topological property of the initial region during its state transition process. To overcome this drawback, our sampling process is driven by 'dynamic site selection' which enables to preserve the topology of the initial region in the state transition process. We prove the global convergence property of our proposed sampling method by extending the existing stochastic sampling theories. We demonstrate the performances of our method by simulation studies for both synthetic and natural images.

Citation Download Citation

Taizo Anan, Makoto Ohtsu, Hiroyuki Kudo, and Tsuneo Saito "New stochastic sampling method for region extraction: theory and experiments", Proc. SPIE 2823, Statistical and Stochastic Methods for Image Processing, (8 October 1996); https://doi.org/10.1117/12.253443

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