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Filter Learning for Linear Structure Segmentation

Publications associées (59)

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UV-patternable polymers with selective spectral response

Jürgen Brugger, Victor Javier Cadarso Busto

We present the characterization of dyed doped/modified Epocore resist (Microresist Technology, Germany) with three commercially-available dyes (Proquimac Color, Spain). After being doped with lilac, red and yellow dyes in a 1:2500 ratio (dye:Epocore, w:w), ...

Elsevier2012

Bi-Exponential Edge-Preserving Smoother

Michaël Unser, Daniel Sage, Philippe Thévenaz

Edge-preserving smoothers need not be taxed by a severe computational cost. We present, in this paper, a lean algorithm that is inspired by the bi-exponential filter and preserves its structure-a pair of one-tap recursions. By a careful but simple local ad ...

Institute of Electrical and Electronics Engineers2012

Model-Based Similarity Measure in TimeCloud

Karl Aberer, Ho Young Jeung

This paper presents a new approach to measuring similarity over massive time-series data. Our approach is built on two principles: one is to parallelize the large amount computation using a scalable cloud serving system, called TimeCloud. The another is to ...

Springer2012

A Wideband Doubly-Sparse Approach For MITO Sparse Filter Estimation

Rémi Gribonval

We propose an approach for the estimation of sparse filters from a convolutive mixture of sources, exploiting the time-domain sparsity of the mixing filters and the sparsity of the sources in the time-frequency (TF) domain. The proposed approach is based o ...

IEEE2011

Estimating multiple filters from stereo mixtures: a double sparsity approach

Rémi Gribonval

We consider the problem of estimating multiple filters from convolutive mixtures of several unknown sources. We propose to exploit both the time-frequency (TF) sparsity of the sources and the sparsity of the mixing filters. Our framework consists of: a) a ...

2011

Input Filter Design for Feasibility in Constraint-Adaptation Schemes

Dominique Bonvin, Grégory François, Gene Bunin

The subject of real-time, steady-state optimization under significant uncertainty is addressed in this paper. Specifically, the use of constraint-adaptation schemes is reviewed, and it is shown that, in general, such schemes cannot guarantee process feasib ...

2011

Subspaces Indexing Model on Grassmann Manifold for Image Search

Xinchao Wang

Conventional linear subspace learning methods like principal component analysis (PCA), linear discriminant analysis (LDA) derive subspaces from the whole data set. These approaches have limitations in the sense that they are linear while the data distribut ...

Institute of Electrical and Electronics Engineers2011

Fast O(1) Bilateral Filtering Using Trigonometric Range Kernels

Michaël Unser, Daniel Sage, Kunal Narayan Chaudhury

It is well known that spatial averaging can be realized (in space or frequency domain) using algorithms whose complexity does not scale with the size or shape of the filter. These fast algorithms are generally referred to as constant-time or O(1) algorithm ...

IEEE2011

NxN Coupling Matrix Synthesis of Dissipative Microwave Filters

Michael Mattes, Francisco Edén Sorolla Rosario, Diego Navarro Tapia

In this communication, the coupling matrix synthesis of dissipative microwave filters is analyzed and compared to the synthesis of lossless filters. A modified definition of the vector matrix product allows using the same synthesis procedure in both cases. ...

2011

Double Sparsity: Towards Blind Estimation of Multiple Channels

Rémi Gribonval

We propose a framework for blind multiple filter estimation from convolutive mixtures, exploiting the time-domain sparsity of the mixing filters and the disjointness of the sources in the time-frequency domain. The proposed framework includes two steps: (a ...

Springer-Verlag's2010