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Recently, HodgeRank on random graphs has been proposed as an effective framework for multimedia quality assessment problem based on paired comparison method. With the ran-dom design on large graphs, i...
Recently, HodgeRank on random graphs has been proposed as an effective framework for multimedia quality assessment problem based on paired comparison method. With the ran-dom design on large graphs, i...
We formulate the problem of hyperspectral image unmixing as a nonconvex optimization problem, similar to nonnegative matrix factorization. We present a heuristic for approximately solving this problem...
Estimation of mechanical structure damage can greatly benefit from the knowledge that the damage accumulates irreversibly over time. This paper formulates a problem of estimation of a pixel-wise monot...
Image priors based on products have been recognized to offer many advantages because they allow simultaneous enforcement of multiple constraints. However, they are inconvenient for Bayesian inferenc...
Topic modeling based on latent Dirichlet allocation (LDA) has been a framework of choice to perform scene recognition and annotation. Recently, a new type of topic model called the Document Neural Aut...
This paper investigates a fully unsupervised statistical method for edge preserving image restoration and compression using a spatial decomposition scheme. Smoothed maximum likelihood is used for loca...
Shape-based regularization has proven to be a useful method for delineating objects from the noisy im-ages encountered in many applications when one has prior knowledge of the shape of the targeted o...
We present a two-stage approach for learning dic-tionaries for object classification tasks based on the principle of information maximization. The proposed method seeks a dictionary that is compact, d...
We describe a new optimization scheme for nding high-quality clusterings in planar graphs that uses weighted perfect matching as a subroutine. Our method provides lower-bounds on the energy of the op...
In this paper we address the problem of understanding the success of algorithms that organize patches according to graph-based metrics. Algorithms that analyze patches extracted from images or time se...
We propose a novel probabilistic method for detection of objects in noisy images. The method uses results from percolation and random graph theories. We present an algorithm that allows to detect obje...
In the series of our earlier papers on the subject, we proposed a novel statistical hypothesis testing method for detection of objects in noisy images. The method uses results from percolation theory ...
A new class of statistical deformable models is introduced to study high-dimensional curves or images. These models are useful to analyze the geometric modes of variation of a data set around a commo...
In this article we give our contribution to the problem of segmentation with plug-in procedures. We give general sufficient conditions under which plug in procedure are efficient. We also give an al...

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