This paper is devoted to the modeling of real textured images by functional minimization and partial differential equations. Following the ideas of Yves Meyer in a total variation minimization framework of L. Rudin, S. Osher, and E. Fatemi, we decompose a given (possible textured) image f into a sum of two functions u+v, where u ¥ BV is a function of bounded variation (a cartoon or sketchy appr...

BrainK is a set of automated procedures for characterizing the tissues of the human head from MRI, CT, and photogrammetry images. The tissue segmentation and cortical surface extraction support the primary goal of modeling the propagation of electrical currents through head tissues with a finite difference model (FDM) or finite element model (FEM) created from the BrainK geometries. The electri...

In this study, the radial basis functions based SG algorithm (SGRBF) is applied for evolution of level sets in image segmentation. The implementation of level set method in image processing often involves solving partial differential equations (PDEs). Finite differences implicit scheme is a prevalent method to solve PDE for extending the evolution of level sets. Instead of using finite differen...

We propose in this paper an alternative approach for computing p-harmonic maps and flows: instead of solving a constrained minimization problem on SN−1, we solve an unconstrained minimization problem on the entire space of functions. This is possible, using the projection on the sphere of any arbitrary function. Then we show how this formulation can be used in practice, for problems with both i...

Partial Differential Equations (PDEs) have become an important tool in image processing and analysis. A PDE mode for image zooming is introduced in this paper. This model exploits a higher order nonlinear partial differential equation. The resulted nonlinear equation is solved by an explicit finite difference schemes. Numerical results on real digital images are given to show effectiveness and ...

They can be written in vector forms respectively as E1(s, I) = (s − PxCxI) Ax(s − PxCxI) + (s − PyCyI) Ay(s − PyCyI), (4) E2(I) = (I − I0) B(I − I0), (5) where s, I and I0 are vector representations of s, I and I0. Cx and Cy are discrete backward difference matrices that are used to compute image gradients in the xand ydirections. Px, Py , Ax, Ay and B are diagonal matrices, whose i-th diagonal...

Many image processing applications require estimating the orientation of the image edges. This estimation is often done with a finite difference approximation of the orthogonal gradient. As an alternative, we apply contour stencils, a method for detecting contours from total variation along curves, and show it more robustly estimates the edge orientations than several finite difference approxim...

In this paper we present a low power temporaldifference image sensor with wireless communication capability designed specifically for imaging sensor networks. The eventbased image sensor features a 64×64 pixel array and can also report standard analog intensity images. An ultra-wideband (UWB) radio channel allows to transmit digital temporal difference images wirelessly to a receiver with high ...

In this paper, we propose an algorithm for the measure of local and global contrast in digital images. It applies locally, at various sub-sampled levels, a simplified computation of local contrast based on DOG and finally it recombines all the values to obtain a global measure. The proposed method comes from the modification of a previous algorithm with a different local measure of contrast and...