Improving the convergence of local binary fitting energy for image segmentation

Yangyang Song; Guohua Peng

doi:10.1117/12.2524236

6 May 2019 Improving the convergence of local binary fitting energy for image segmentation

Yangyang Song, Guohua Peng

Proceedings Volume 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018); 110692E (2019) https://doi.org/10.1117/12.2524236
Event: Tenth International Conference on Graphic and Image Processing (ICGIP 2018), 2018, Chengdu, China

Abstract

This paper explores a Riemannian steepest method for fast converging local binary fitting model. The proposed method takes advantage of intensity information in local regions, which solves the intensity inhomogeneous images with satisfactory results. Furthermore, the Riemannian steepest descent method can be employed to local binary fitting model from exponential family and achieves convergence fast. The main contribution of this paper is that presents a general closed-form expression for the manifold’s Riemannian metric tensor of local binary fitting model, which makes the computation of Riemannian gradient flow possible. In addition, to ensure the accuracy of the segmentation results, we regularize the level set function by Gaussian smooth operator. Experimental results for synthetic and real-life images show satisfactory performances of proposed method.

Citation Download Citation

Yangyang Song and Guohua Peng "Improving the convergence of local binary fitting energy for image segmentation", Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110692E (6 May 2019); https://doi.org/10.1117/12.2524236

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