A novel variational model for image segmentation

In this paper, we propose a new variational model for image segmentation. Our model is inspired by the complex Ginzburg-Landau model and the semi-norm defined by us. This new model can detect both the convex and concave parts of images. Moreover, it can also detect non-closed edges as well as quadruple junctions. Compared with other methods, the initialization is completely automatic and the segmented images obtained by using our new model could keep fine structures and edges of the original images very effectively. Finally, numerical results show the effectiveness of our model.     

A network model for incompressible two-fluid flow and its numerical solution

In this paper we consider an incompressible version of the two-fluid network model proposed by Porsching (Nu. Methods Part. Diff. Eq., 1 , 295–313 [1985]). The system of equations governing the model is a mixed system of differential and algebraic equations (DAEs). These DAEs are then recast, through proper transformation, into a system of ordinary differential equations on a submanifold of ?ⁿ, for which uniqueness, existence, and stability theorems are proved. Numerical simulations are presented.     

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

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.     

A variational formulation for physical noised image segmentation

Image segmentation is a hot topic in image science. In this paper we present a new variational segmentation model based on the theory of Mumford-Shah model. The aim of our model is to divide noised image, according to a certain criterion, into homogeneous and smooth regions that should correspond to structural units in the scene or objects of interest. The proposed region-based model uses total variation as a regularization term, and different fidelity term can be used for image segmentation in the cases of physical noise, such as Gaussian, Poisson and multiplicative speckle noise. Our model consists of five weighted terms, two of them are responsible for image denoising based on fidelity term and total variation term, the others assure that the three conditions of adherence to the data, smoothing, and discontinuity detection are met at once. We also develop a primal-dual hybrid gradient algorithm for our model. Numerical results on various synthetic and real images are provided to compare our method with others,these results show that our proposed model and algorithms are effective.     

A boundary function equation and its numerical solution

We consider the asymptotic solution of the plasma-sheath integro-differential equation, which is singularly perturbed due to the presence of a small coefficient multiplying the highest order (second) derivative. The asymptotic solution is obtained by the boundary function method. A second-order differential equation is derived describing the behavior of the zeroth-order boundary functions. A numerical algorithm for this equation is discussed. __________ Translated from Prikladnaya Matematika i Informatika, No. 24, pp. 24–34, 2006.     

Finite difference solution of Euler equations arising in variational image segmentation

This paper deals with finite-difference approximations of Euler equations arising in the variational formulation of image segmentation problems. We illustrate how they can be defined by the following steps: (a) definition of the minimization problem for the Mumford–Shah functional (MSf), (b) definition of a sequence of functionals Γ-convergent to the MSf, and (c) definition and numerical solution of the Euler equations associated to the kth functional of the sequence. We define finite difference approximations of the Euler equations, the related solution algorithms, and we present applications to segmentation problems by using synthetic images. We discuss application results, and we mainly analyze computed discontinuity contours and convergence histories of method executions. This revised version was published online in June 2006 with corrections to the Cover Date.     

A local variational relation and applications

In [BGH] the authors show that for a given topological dynamical system (X,T) and an open coveru there is an invariant measure μ such that infh _μ(T,ℙ)≥h _top(T,U) where infimum is taken over all partitions finer thanu. We prove in this paper that if μ is an invariant measure andh _μ(T,ℙ) > 0 for each ℙ finer thanu, then infh _μ(T,ℙ > 0 andh _top(T,U) > 0. The results are applied to study the topological analogue of the Kolmogorov system in ergodic theory, namely uniform positive entropy (u.p.e.) of ordern (n≥2) or u.p.e. of all orders. We show that for eachn≥2 the set of all topological entropyn-tuples is the union of the set of entropyn-tuples for an invariant measure over all invariant measures. Characterizations of positive entropy, u.p.e. of ordern and u.p.e. of all orders are obtained. We could answer several open questions concerning the nature of u.p.e. and c.p.e.. Particularly, we show that u.p.e. of ordern does not imply u.p.e. of ordern+1 for eachn≥2. Applying the methods and results obtained in the paper, we show that u.p.e. (of order 2) system is weakly disjoint from all transitive systems, and the product of u.p.e. of ordern (resp. of all orders) systems is again u.p.e. of ordern (resp. of all orders). Project supported by one hundred talents plan and 973 plan.     

The numerical solution of parabolic differential equations using variational methods

Summary The question of constructing stable numerical representations for the solutions of initial-boundary value problems for parabolic differential equations is examined.An earlier formulation and discussion of this work can be found in the Author's Ph.D. Thesis (University of Adelaide, South Australia, 1967).     

A local variational principle of pressure and its applications to equilibrium states

We prove a local variational principle of pressure for any given open cover. More precisely, for a given dynamical system (X, T), an open cover of X, and a continuous, real-valued function f on X, we show that the corresponding local pressure P(T, f; ) satisfies

A relative local variational principle for topological pressure

We define the relative local topological pressure for any given factor map and open cover,and prove the relative local variational principle of this pressure.More precisely,for a given factor map π:(X,T)→(Y,S) between two topological dynamical systems,an open cover U of X,a continuous,real-valued function f on X and an S-invariant measure ν on Y,we show that the corresponding relative local pressure P(T,f,U,y) satisfies sup μ∈M(X,T){ hμ(T,U|Y)+∫X f(x)dμ(x) :πμ=ν}=∫Y P(T,f,U,y)dν(y),where M(X,T) denotes the family of all T-invariant measures on X.Moreover,the supremum can be attained by a T-invariant measure.     

A solution method for linear variational relation problems

In this paper, we consider a particular class of variational relation problem namely linear variational relation problem wherein the sets are defined by linear inequalities. The purpose is to study the existence of the solution set and its nature for this class of problem. Using these results, we provide algorithms to obtain the solutions of the problem based on which we present some numerical illustrations.     

A coupled multivalued model for ice streams and its numerical simulation

^** Email: nati{at}dma.uvigo.es^*** Email: durany{at}dma.uvigo.es^**** Email: anaisabel.munoz{at}urjc.es^***** Email: emanuele.schiavi{at}urjc.es^****** Email: carlosv{at}udc.es This paper deals with the numerical solution of a non-linearmodel describing a free-boundary problem arising in modern glaciology.Considering a shallow, viscous ice sheet flow along a soft,deformable bed, a coupled non-linear system of differentialequations can be obtained. Particularly, an obstacle problemis then deduced and solved in the framework of its complementarityformulation. We present the numerical solution of the resultingmultivalued system modelling the ice sheet non-Newtonian dynamicsdriven by the underlying drainage system. Our numerical resultsshow the existence of fast ice streams when positive wave-likeinitial conditions are considered. The solutions are numericallycomputed with a decoupling iterative method and finite-elementtechnique. A duality algorithm and a projected Gauss–Seidelmethod are the alternatives used to cope with the resultingvariational inequality while the explicit treatment, Newtonmethod or a duality method are proposed to deal with the non-linearsource term. Finally, the numerical solutions are physicallyinterpreted and some comparisons among the numerical methodsare then discussed.     

Duality for implicit variational problems and numerical applications

The present paper deals with a dual characterization of the solutions of implicit variational problems. Some general results relating the solutions of the dual problem with those of the primal one are applied to variational and quasi-variational inequalities, Nash equilibria, saddle points and fixed points.

Finally a dual method for the numerical solutions of some quasi-variational inequalities is developed.     

The planar radiosity equation and its numerical solution

This article gives properties of the planar radiosity equationand methods for its numerical solution. Regularity propertiesof the radiosity solution are examined, including both the effectsof corners and the effects of the visibility function. Theseare taken into account in the design of collocation methodswith piecewise polynomial approximating functions. Numericalexamples conclude the paper.     

A method for numerical integration on an automatic computer

A new method for the numerical integration of a well-behaved function over a finite range of argument is described. It consists essentially of expanding the integrand in a series of Chebyshev polynomials, and integrating this series term by term. Illustrative examples are given, and the method is compared with the most commonly-used alternatives, namelySimpson's rule and the method ofGauss.