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- Martin Kiechle, Simon Hawe, Martin Kleinsteuber
- 2013 IEEE International Conference on Computer…
- 2013

High-resolution depth maps can be inferred from low-resolution depth measurements and an additional high-resolution intensity image of the same scene. To that end, we introduce a bimodal co-sparse analysis model, which is able to capture the interdependency of registered intensity and depth information. This model is based on the assumption that the… (More)

- Simon Hawe, Matthias Seibert, Martin Kleinsteuber
- 2013 IEEE Conference on Computer Vision and…
- 2013

Many techniques in computer vision, machine learning, and statistics rely on the fact that a signal of interest admits a sparse representation over some dictionary. Dictionaries are either available analytically, or can be learned from a suitable training set. While analytic dictionaries permit to capture the global structure of a signal and allow a fast… (More)

- Simon Hawe, Martin Kleinsteuber, Klaus Diepold
- 2011 International Conference on Computer Vision
- 2011

In this work we propose a method for estimating disparity maps from very few measurements. Based on the theory of Compressive Sensing, our algorithm accurately reconstructs disparity maps only using about 5% of the entire map. We propose a conjugate subgradient method for the arising optimization problem that is applicable to large scale systems and… (More)

- Simon Hawe, Martin Kleinsteuber, Klaus Diepold
- IEEE Transactions on Image Processing
- 2013

Exploiting a priori known structural information lies at the core of many image reconstruction methods that can be stated as inverse problems. The synthesis model, which assumes that images can be decomposed into a linear combination of very few atoms of some dictionary, is now a well established tool for the design of image reconstruction algorithms. An… (More)

- Martin Kleinsteuber, Knut Hüper
- 2007 IEEE International Conference on Acoustics…
- 2007

In this paper, a conjugate gradient method on the complex Grabmann manifold is proposed that computes the k-principal components of a Hermitian (n × n)-matrix. The algorithm is at most of order O(n<sup>2</sup>k) and yields locally good convergence results.

- Rémi Gribonval, Rodolphe Jenatton, Francis R. Bach, Martin Kleinsteuber, Matthias Seibert
- IEEE Transactions on Information Theory
- 2015

Many modern tools in machine learning and signal processing, such as sparse dictionary learning, principal component analysis, non-negative matrix factorization, K-means clustering, and so on, rely on the factorization of a matrix obtained by concatenating high-dimensional vectors from a training collection. While the idealized task would be to optimize the… (More)

- Florian Seidel, Clemens Hage, Martin Kleinsteuber
- ArXiv
- 2013

An increasing number of methods for background subtraction use Robust PCA to identify sparse foreground objects. While many algorithms use the `1-norm as a convex relaxation of the ideal sparsifying function, we approach the problem with a smoothed `p-norm and present pROST, a method for robust online subspace tracking. The algorithm is based on alternating… (More)

- Martin Kleinsteuber, Hao Shen
- IEEE Signal Processing Letters
- 2012

This work studies the problem of simultaneously separating and reconstructing signals from compressively sensed linear mixtures. We assume that all source signals share a common sparse representation basis. The approach combines classical Compressive Sensing (CS) theory with a linear mixing model. It allows the mixtures to be sampled independently of each… (More)

We propose an algorithm for segmenting natural images based on texture and color information, which leverages the co-sparse analysis model for image segmentation. As a key ingredient of this method, we introduce a novel textural similarity measure, which builds upon the co-sparse representation of image patches. We propose a statistical MAP inference… (More)

- Martin Kleinsteuber, Uwe Helmke, Knut Hüper
- SIAM J. Matrix Analysis Applications
- 2004

A generalization of the cyclic Jacobi algorithm is proposed that works in an arbitrary compact Lie algebra. This allows, in particular, a unified treatment of Jacobi algorithms on different classes of matrices, such as, e.g., skew-symmetric or skew-Hermitian Hamiltonian matrices. Wildberger has established global, linear convergence of the algorithm for the… (More)