检索结果-内蒙古大学图书馆

A Multigrid Algorithm for Maxflow and Min-Cut Problems with Applications to multiphase image segmentation

JOURNAL OF SCIENTIFIC COMPUTING 2021年第3期87卷 101-101页

作者： Tai, Xue-Cheng Deng, Liang-Jian Yin, Ke Hong Kong Baptist Univ Dept Math Kowloon Tsai Hong Kong Peoples R China Univ Elect Sci & Technol China Sch Math Sci Chengdu Peoples R China Huazhong Univ Sci & Technol Ctr Math Sci Wuhan Peoples R China

In this paper, we propose a multiphase image segmentation method via solving the min-cut minimization problem under the multigrid method framework. At each level of the multigrid method for the min-cut problem, we first transfer it to the equivalent form, e.g., max-flow problem, then actually solve the dual of the max-flow problem. Particularly, a classical multigrid method is used to solve the sub-minimization problems. Several outer iterations are used for the multigrid method. The proposed idea can be used for general min-cut/max-flow minimization problems. We use multiphase image segmentation as an example in this work. Extensive experiments on simulated and real images demonstrate the efficiency and effectiveness of the proposed method.

关键词： Continuous min-cut and max-flow multiphase image segmentation Multigrid method

来源：评论

学校读者我要写书评

暂无评论

An Innovative Variational Level Set Model for multiphase image segmentation

An Innovative Variational Level Set Model for Multiphase Ima...

引用

3rd International Conference on Multimedia and Ubiquitous Engineering

作者： Shi, Jie Pan, Zhenkuan Wei, Weibo Qingdao Univ Coll Informat Engn Qingdao 266071 Shandong Peoples R China

ISBN: (纸本)9780769536583

In this paper, a new model based on variational level set method for segmenting multiphase images is proposed. Its energy functional is based on the level set functions and the regional characteristics functions. It can deal with changes in curves' topology and be extended to multiphase image segmentation models which are based on other schemes for region competition as well. The gradient flow of the energy functional is computed using variational method to get the optimized solution of the model. In order to guarantee more accurate calculation, discrete definition of level set functions is used. Experimental results show that the new model has promising effect on image segmentation.

关键词： multiphase image segmentation Level Set Regional Characteristics Functions

来源：评论

学校读者我要写书评

暂无评论

The Vese-Chan model without redundant parameter estimation for multiphase image segmentation

引用

EURASIP JOURNAL ON image AND VIDEO PROCESSING 2020年第1期2020卷 3-3页

作者： Wang, Jie Xu, Zisen Pan, Zhenkuan Wei, Weibo Wang, Guodong Qingdao Univ Coll Comp Sci & Technol Qingdao 266071 Peoples R China Qingdao Univ Affiliated Hosp Qingdao 266003 Peoples R China

The Vese-Chan model for multiphase image segmentation uses m binary label functions to construct 2(m) characteristic functions for different phases/regions systematically;the terms in this model have moderate degrees comparing with other schemes of multiphase segmentation. However, if the number of desired regions is less than 2(m), there exist some empty phases which need costly parameter estimation for segmentation purpose. In this paper, we propose an automatic construction method for characteristic functions via transformation between a natural number and its binary expression, and thus, the characteristic functions of empty phases can be written and recognized naturally. In order to avoid the redundant parameter estimations of these regions, we add area constraints in the original model to replace the corresponding region terms to preserve its systematic form and achieve high efficiency. Additionally, we design the alternating direction method of multipliers (ADMM) for the proposed modified model to decompose it into some simple sub-problems of optimization, which can be solved using Gauss-Seidel iterative method or generalized soft thresholding formulas. Some numerical examples for gray images and color images are presented finally to demonstrate that the proposed model has the same or better segmentation effects as the original one, and it reduces the estimation of redundant parameters and improves the segmentation efficiency.

关键词： multiphase image segmentation Vese-Chan model Parameter estimation Binary label function Alternating direction method of multipliers

来源：评论

学校读者我要写书评

暂无评论

Generalizing Mumford-Shah Model for multiphase Piecewise Smooth image segmentation

引用

IEEE TRANSACTIONS ON image PROCESSING 2017年第2期26卷 942-952页

作者： Gu, Ying Xiong, Wei Wang, Li-Lian Cheng, Jierong Agcy Sci Technol & Res Inst Infocomm Res Singapore 138632 Singapore Nanyang Technol Univ Sch Phys & Math Sci Div Math Sci Singapore 637371 Singapore

This paper concerns multiphase piecewise smooth image segmentation with intensity inhomogeneities. Traditional methods based on the Mumford-Shah (MS) model require solving complicated diffusion equations evolving in irregular sub-domains, leading to significant difficulties in efficient and accurate segmentation, especially in multiphase scenarios. In this paper, we propose a general framework to modify the MS model by using smoothing operators that can avoid the complicated implementation and inaccurate segmentation of traditional approaches. A detailed analysis connecting the smoothing operators and the diffusion equations is given to justify the modification. In addition, we present an efficient algorithm based on the direct augmented Lagrangian method, which requires fewer parameters than the commonly used augmented Lagrangian method. Typically, the smoothing operator in the general model is chosen to be Gaussian kernel, the bilateral kernel, and the directional diffusion kernel, respectively. Ample numerical results are provided to demonstrate the efficiency and accuracy of the modified model and the proposed minimization algorithm through various comparisons with existing approaches.

关键词： Mumford-Shah (MS) model multiphase image segmentation smoothing operator Gaussian bilateral directional diffusion augmented Lagrangian method

来源：评论

学校读者我要写书评

暂无评论

Inhomogeneous image segmentation based on local constant and global smoothness priors

引用

DIGITAL SIGNAL PROCESSING 2021年 111卷 102989-102989页

作者： Min, Lihua Cui, Qiang Jin, Zhengmeng Zeng, Tieyong Nanjing Univ Posts & Telecommun Sch Sci Nanjing Jiangsu Peoples R China Chinese Univ Hong Kong Dept Math Shatin Hong Kong Peoples R China

In this article, we propose a new variational model for segmenting images with intensity inhomogeneity. The proposed model applies simultaneously the local constant and global smoothness priors to describe the bias part such that our model can obtain more precise segmentation results. This is different from the existing models in which either of such two priors is considered. Also, our method is developed to segment the multiphase inhomogeneous image with noise. Theoretically, we show the existence of minimizers of the proposed variational model. Moreover, by using the alternating minimization method, we design an effective algorithm to numerically solve the solution of the model. Numerical experimental results are provided to verify the better performance of our method than other test methods. (C) 2021 Elsevier Inc. All rights reserved.

关键词： multiphase image segmentation Intensity inhomogeneity Variational method Alternating minimization

来源：评论

学校读者我要写书评

暂无评论

Fuzzy region competition-based auto-color-theme design for textile images

引用

TEXTILE RESEARCH JOURNAL 2013年第6期83卷 638-650页

作者： Han, Yu Zheng, Dejun Baciu, George Feng, Xiangchu Li, Min Xidian Univ Dept Appl Math Xian 710071 Shaanxi Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Hong Kong Peoples R China Shenzhen Univ Coll Math & Computat Sci Shenzhen Peoples R China

We propose a model to recolor textile images by different color themes. The model contains three phases. The first phase is to partition an input textile image into several homogeneous regions. The CIELab color mean of each region and a bias-field function are obtained from the segmentation results. The combination of the color mean values of all regions is considered as the color theme of the input image. The second phase is to retrieve the relevant color themes from a given dataset. The retrieved color themes preserve the color mood of the input image in the sense of the similarity measurement defined in the color mood space. In the third phase, we reconstruct new images with different appearances from the input image by using the retrieved color themes. The proposed method provides a powerful tool for designers to generate and search for all relevant color combinations related to a given theme. Numerical results indicate that our recolorization model performs well on complex textile design patterns.

关键词： color theme variational differential multiphase image segmentation region competition color transformation color mood space

来源：评论

学校读者我要写书评

暂无评论

Fast Projection Algorithms for Level Set Evolution in Potts Model

Fast Projection Algorithms for Level Set Evolution in Potts ...

引用

2nd International Conference on Computer Science and Network Technology (ICCSNT)

作者： Wang, Jing Pan, Zhenkuan Zheng, Yongguo Duan, Jinming Shandong Univ Sci & Technol Coll Informat Sci & Engn Qingdao Peoples R China

ISBN: (纸本)9781467329644;9781467329637

Potts model is a basic variational model for multiphase image segmentation. Under the variational level set framework, the model is usually implemented by solving a gradient descent equation derived from energy functional minimization together with re-initialization process to preserve the level set function as a signed distance function. Because of the existence of complicated curvature terms in the PDE equations and the time-consuming re-initialization process, this method is with low computation efficiency. In this paper, we combine the Split Bregman algorithm, Dual method together with a very simple projection method to overcome the problems mentioned above. We name these two methods as Split Bregman Projection Method(SBPM) and Dual Split Bregman Projection Method(DSBPM). They naturally preserve the level set function as a signed distance function during the evolution without the initialization process. These methods are compared with traditional method and the ones proposed by other authors according to some numerical experiments. These examples demonstrate that our proposed methods are of higher efficiency than the other ones and can segment images quickly.

关键词： Potts model Variational level set method multiphase image segmentation Split Bregman Projection Method Dual Split Bregman Projection Method

来源：评论

学校读者我要写书评

暂无评论

Fast Projection Algorithms for Level Set Evolution in Potts Model

Fast Projection Algorithms for Level Set Evolution in Potts ...

引用

International Conference on Computer Science and Network Technology

作者： Jing WANG Zhenkuan PAN Yongguo ZHENG Jinming DUAN College of Information Science and Engineering Shandong University of Science and Technology College of Information Engineering Qingdao University

ISBN: (纸本)9781467329637

Potts model is a basic variational model for multiphase image segmentation. Under the variational level set framework, the model is usually implemented by solving a gradient descent equation derived from energy functional minimization together with re-initialization process to preserve the level set function as a signed distance function. Because of the existence of complicated curvature terms in the PDE equations and the time-consuming re-initialization process, this method is with low computation efficiency. In this paper, we combine the Split Bregman algorithm, Dual method together with a very simple projection method to overcome the problems mentioned above. We name these two methods as Split Bregman Projection Method (SBPM) and Dual Split Bregman Projection Method (DSBPM). They naturally preserve the level set function as a signed distance function during the evolution without the initialization process. These methods are compared with traditional method and the ones proposed by other authors according to some numerical experiments. These examples demonstrate that our proposed methods are of higher efficiency than the other ones and can segment images quickly.

关键词： Potts model Variational level set method multiphase image segmentation Split Bregman Projection Method Dual Split Bregman Projection Method Potts model distance function projection method Level set reinitialization evolution Projection algorithms Evolution image segmentation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：