Evaluation of Robust Fitting Based Detection

Index Terms—Adaptive signal processing, covariance matrix estima- tion, robust estimation, Majorization-Minimization, Kronecker product, low rank..

1) Local approaches, which use the window/neighborhood of the considered pixel to estimate the filtered value. Different spatial estimation supports have been developed: 1)

L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des

After formalizing the matrix inversion, some questions about the inverse matrix can be expressed using its explicit form. As a consequence, the inverse of the nonsingular

This was done under the following assumptions: the locations of the non-corrupted columns are chosen uniformly at random; L 0 satisfies some sparse/low-rank incoherence condition;

In this paper, we propose a novel algorithm for moving object detection based on a robust matrix factorization. For a data matrix A, we assume that it is approximatively low-rank, and

In this paper, we propose to use a low-rank matrix factorization with IRLS scheme (Iteratively reweighted least squares) and to address in the minimization process the spatial

This noise model was shown to include four state-of-the-art diffuse noise models as special cases, namely the spatially uncorrelated noise model, the fixed noise coherence model,