Unsupervised hierarchical image segmentation with level set and additive operator splitting

2005 ◽  
Vol 26 (10) ◽  
pp. 1461-1469 ◽  
Author(s):  
M. Jeon ◽  
M. Alexander ◽  
W. Pedrycz ◽  
N. Pizzi
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Operator Splitting ◽  
Additive Operator ◽  
Additive Operator Splitting

A New Image Segmentation Method Based on Improved Fast Implicit Level Set Scheme in X/γ-Ray Inspection System

2011 ◽  
Vol 103 ◽  
pp. 705-710 ◽  
Author(s):  
Yu Jie Li ◽  
Hui Min Lu ◽  
Li Feng Zhang ◽  
Shi Yuan Yang ◽  
Serikawa Seiichi
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Active Contours ◽  
Operator Splitting ◽  
Inspection System ◽  
Segmentation Method ◽  
Piecewise Constant ◽  
Γ Ray ◽  
Luggage Inspection ◽  
Segmentation Of Images

Digital X/γ-ray imaging technology has been widely used to help people deliver effective and reliable security in airports, train stations, and public buildings. Nowadays, luggage inspection system with digital radiographic/computed tomography (DR/CT) represents a most advanced nondestructive inspection technology in aviation system, which is capable of automatically discerning interesting regions in the luggage objects with CT subsystem. In this paper, we propose a new model for active contours to detect luggage objects in the system, in order to facilitate people to identify the things in luggage. The proposed method is based on techniques of piecewise constant and piecewise smooths Chan-Vese Model, semi-implicit additive operator splitting (AOS) scheme for image segmentation. Different from traditional models, the fast implicit level set scheme (FILS) is ordinary differential equation (ODE). Characterized by no need of any pre-information of topology of images and efficient segmentation of images with complex topology, the FILS scheme is fast more than traditional level set scheme 30 times. At the same time, it performs well in image segmentation of DR images in our experiments.

Improved Additive Operator Splitting Algorithms for Basket Option Pricing Model

2013 ◽  
Vol 756-759 ◽  
pp. 2739-2743
Author(s):  
Xiao Zhong Yang ◽  
Gao Xin Zhou
Keyword(s):  
Option Pricing ◽  
Operator Splitting ◽  
Stability And Convergence ◽  
Pricing Model ◽  
Explicit Schemes ◽  
Basket Option ◽  
Additive Operator ◽  
Black Scholes ◽  
Option Pricing Model ◽  
Additive Operator Splitting

In order to solve Black-Scholes equation of basket option pricing model by numerical method. This paper used Additive Operator Splitting (AOS) algorithm to split the multi-dimensional Black-Scholes equation into equivalent one-dimensional equation set, and constructed 'Explicit-Implicit' and 'Implicit-Explicit' schemes to solve it. Then compatibility, stability and convergence of those schemes were analyzed. Finally, this paper compared computation time and precision of the schemes through numerical experiments. 'Explicit-Implicit' and 'Implicit-Explicit' schemes of AOS algorithms have both higher accuracy and faster computing speed and them have practical significance in solving basket option pricing model.

Proposal of Fast Implicit Level Set Scheme for Medical Image Segmentation Using the Chan and Vese Model

2011 ◽  
Vol 103 ◽  
pp. 695-699 ◽  
Author(s):  
Hui Min Lu ◽  
Serikawa Seiichi ◽  
Yu Jie Li ◽  
Li Feng Zhang ◽  
Shi Yuan Yang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Social Life ◽  
Medical Image ◽  
High Speed ◽  
Active Contours ◽  
Operator Splitting ◽  
Medical Image Segmentation ◽  
Proposed Model ◽  
The Social

People living in the information age, are more and more attention to their lives. It is also said, social life is more important in present and future. The social life contains three fields. In this paper, we propose a new model for active contours to detect objects in a given medical image, in order to facilitate people to have medical treatment. The proposed method is based on techniques of piecewise constant and piecewise smooths Chan-Vese Model, semi-implicit additive operator splitting (AOS) scheme for image segmentation. Different from traditional models, our model uses the level set which are corresponding to ordinary differential equation (ODE). Our model has more improved characteristics than traditional models, such as: less sensibility of noise; unnecessary of re-initialization and high speed by the simplified ordinary differential function. Finally, we validate the proposed model by numerical synthetic and real images. The experimental results demonstrate that our model is at least two times more efficient than the widely used methods.

scholarly journals An Efficient Multi-Scale Local Binary Fitting-Based Level Set Method for Inhomogeneous Image Segmentation

2018 ◽  
Vol 2018 ◽  
pp. 1-17
Author(s):  
Dengwei Wang
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Operator Splitting ◽  
Reaction Diffusion ◽  
Initial Contour ◽  
Time Step ◽  
Detection Mechanism ◽  
Diffusion Term ◽  
Implementation Phase ◽  
Level Set Function

An efficient level set model based on multiscale local binary fitting (MLBF) is proposed for image segmentation. By introducing multiscale idea into the LBF model, the proposed MLBF model can effectively and efficiently segment images with intensity inhomogeneity. In addition, by adding a reaction diffusion term into the level set evolution (LSE) equation, the regularization of the level set function (LSF) can be achieved, thus completely eliminating the time-consuming reinitialization process. In the implementation phase, in order to greatly improve the efficiency of the numerical solution of the level set segmentation model, we introduce three strategies: The first is the additive operator splitting (AOS) solver which is used for breaking the restrictions on time step; the second is the salient target detection mechanism which is used to achieve full automatic initialization of the LSE process; the third is the sparse filed method (SFM) which is used to restrict the groups of pixels that need to be updated in a small strip region. Under the combined effect of these three strategies, the proposed model achieves very high execution efficiency in the following aspects: contour location accuracy, speed of evolution convergence, robustness against initial contour position, and robustness against noise interference.

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