A new stochastic variational PDE model for soft Mumford-Shah segmentation

被引：6

作者：

Posirca, Iulia ^{[1
]}

Chen, Yunmei ^{[1
]}

Barcelos, Celia Z. ^{[2
]}

机构：

[1] Univ Florida, Dept Math, Gainesville, FL 32611 USA

[2] Univ Fed Uberlandia, Dept Matemat, BR-38400 Uberlandia, MG, Brazil

来源：

JOURNAL OF MATHEMATICAL ANALYSIS AND APPLICATIONS | 2011年 / 384卷 / 01期

关键词：

Soft segmentation; Mixture pattern; Probability; Energy functional; Variable exponent; IMAGE SEGMENTATION; REGION COMPETITION; EDGE-DETECTION; MINIMIZATION; ENERGY;

D O I：

10.1016/j.jmaa.2011.05.043

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper, a new stochastic variational PDE model is developed, using instead of hard segmentation soft segmentation. In this way, each pixel is allowed to belong to each image pattern with some probability. Our work proposes a functional with variable exponent, which provides a more accurate model for image segmentation and denoising. The diffusion resulting from the proposed model is a combination between TV-based and isotropic smoothing. The modeling procedure, computational implementation and results are explored in detail and numerical examples of real and synthetic images are presented. (C) 2011 Elsevier Inc. All rights reserved.

引用

页码：104 / 114

页数：11

共 40 条

[1] APPROXIMATION OF FUNCTIONALS DEPENDING ON JUMPS BY ELLIPTIC FUNCTIONALS VIA GAMMA-CONVERGENCE [J].