We consider Bayesian approach to wavelet decomposition. We show how prior knowledge about a function's regularity can be incorporated into a prior model for its wavelet coeecients by establishing a relationship between the hyperparameters of the proposed model and the parameters of those Besov spaces within which realizations from the prior will fall. Such a relation may be seen as giving insig...

This paper considers how to separate text and/or graphics from smooth background in screen content and mixed content images and proposes an algorithm to perform this segmentation task. The proposed methods make use of the fact that the background in each block is usually smoothly varying and can be modeled well by a linear combination of a few smoothly varying basis functions, while the foregro...

The rst purpose of this paper is to describe a new mathematical approach for the computation of an irredundant primary decomposition of a given polynomial ideal I. This presentation will be formed of three parts : a decomposition of the associated radical ideal p I to an intersection of prime ideals P i , then the determination of ideals I i whose the radical is prime (equal to P i), and lastly...

Many geometric fitting problems encountered in computer vision are of non-linear nature and have a large number of parameters. Typically, iterative techniques such as the Levenberg-Marquardt algorithm are used to compute the optimum of highly complex optimization functions. Here we are faced with problems of good initial estimates and high risk of getting in local optimums. In this paper we pro...

The task of digital image compression is the subject of many researches for many years. Up to now this task remains a topic of constant interest. The reason of such interest is that digital media and digital communication are becoming of growing significance worldwide. The subject of this paper is a new original approach to digital image compression. It is based on using singular value decompos...

Prior optimal CUR decomposition and near optimal column reconstruction methods have been established by combining BSS sampling and adaptive sampling. In this paper, we propose a new approach to the optimal CUR decomposition and near optimal column reconstruction by just using leverage score sampling. In our approach, both the BSS sampling and adaptive sampling are not needed. Moreover, our appr...