otherwise it is segmented into another class. Multi-class thresholding is defined in a similar manner. There exist various algorithms for the determination of the thresholding value [2-4]. ABSTRACT: An iterative image segmentation algorithm that segments an image on a pixel-by-pixel basis is described. The observation information to be utilized is the joint gray level values of the pixel to be ...

In this paper, an improved CBFM/p-FFT algorithm is presented, which can be applied to solve electromagnetic scattering problems of large-scale periodic composite metallic/dielectric arrays, even when the array has electrically small periodicity or separating distance. Using characteristic basis function method (CBFM), scattering characteristics of any inhomogeneous targets can be represented by...

Following the results of cite{Med}, regarding the Aluthge transform of polynomial matrices, the symbolic computation of the Duggal transform of a polynomial matrix $A$ is developed in this paper, using the polar decomposition and the singular value decomposition of $A$. Thereat, the polynomial singular value decomposition method is utilized, which is an iterative algorithm with numerical charac...

Wavelet theory has been extensively developed in the function space L2 and discrete wavelet transform has successful applications in many areas. However, to understand better the performance of different discrete wavelet transforms, it is important to investigate their underlying discrete wavelet systems in l2. Though some preliminary results have been found recently, despite the fact that stab...

with σ1 ≥ σ2 ≥ · · · ≥ σr > 0 and r = rank(A). In the above, σ1, . . . , σr are the square roots of the eigenvalues of AA. They are called the singular values of A. Our basic goal is to “solve” the system Ax = b for all matrices A and vectors b. A second goal is to solve the system using a numerically stable algorithm. A third goal is to solve the system in a reasonably efficient manner. For in...

The singular value decomposition of a rectangular data matrix can be used to understand the structure of the data and give insight into the relationships of the row and column factors. For example, the rows linked to the rows might be experimental conditions of temperature and the experimental conditions linked to the columns might pressure. In a biological setting the rows might be linked to t...

Affine rank minimization algorithms typically rely on calculating the gradient of a data error followed by a singular value decomposition at every iteration. Because these two steps are expensive, heuristic approximations are often used to reduce computational burden. To this end, we propose a recovery scheme that merges the two steps with randomized approximations, and as a result, operates on...