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14 results (View BibTeX file of all listed publications)

2009

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Learning an Interactive Segmentation System

Nickisch, H., Kohli, P., Rother, C.

Max Planck Institute for Biological Cybernetics, December 2009 (techreport)

Abstract
Many successful applications of computer vision to image or video manipulation are interactive by nature. However, parameters of such systems are often trained neglecting the user. Traditionally, interactive systems have been treated in the same manner as their fully automatic counterparts. Their performance is evaluated by computing the accuracy of their solutions under some fixed set of user interactions. This paper proposes a new evaluation and learning method which brings the user in the loop. It is based on the use of an active robot user - a simulated model of a human user. We show how this approach can be used to evaluate and learn parameters of state-of-the-art interactive segmentation systems. We also show how simulated user models can be integrated into the popular max-margin method for parameter learning and propose an algorithm to solve the resulting optimisation problem.

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2009

Nickisch, H., Kohli, P., Rother, C. Learning an Interactive Segmentation System Max Planck Institute for Biological Cybernetics, December 2009 (techreport)

Web [BibTex]

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Detection of objects in noisy images and site percolation on square lattices

Langovoy, M., Wittich, O.

(2009-035), EURANDOM, Technische Universiteit Eindhoven, November 2009 (techreport)

Abstract
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 objects of unknown shapes in the presence of random noise. Our procedure substantially differs from wavelets-based algorithms. The algorithm has linear complexity and exponential accuracy and is appropriate for real-time systems. We prove results on consistency and algorithmic complexity of our procedure.

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Langovoy, M., Wittich, O. Detection of objects in noisy images and site percolation on square lattices (2009-035), EURANDOM, Technische Universiteit Eindhoven, November 2009 (techreport)

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An Incremental GEM Framework for Multiframe Blind Deconvolution, Super-Resolution, and Saturation Correction

Harmeling, S., Sra, S., Hirsch, M., Schölkopf, B.

(187), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, November 2009 (techreport)

Abstract
We develop an incremental generalized expectation maximization (GEM) framework to model the multiframe blind deconvolution problem. A simplistic version of this problem was recently studied by Harmeling etal~cite{harmeling09}. We solve a more realistic version of this problem which includes the following major features: (i) super-resolution ability emph{despite} noise and unknown blurring; (ii) saturation-correction, i.e., handling of overexposed pixels that can otherwise confound the image processing; and (iii) simultaneous handling of color channels. These features are seamlessly integrated into our incremental GEM framework to yield simple but efficient multiframe blind deconvolution algorithms. We present technical details concerning critical steps of our algorithms, especially to highlight how all operations can be written using matrix-vector multiplications. We apply our algorithm to real-world images from astronomy and super resolution tasks. Our experimental results show that our methods yield improve d resolution and deconvolution at the same time.

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Harmeling, S., Sra, S., Hirsch, M., Schölkopf, B. An Incremental GEM Framework for Multiframe Blind Deconvolution, Super-Resolution, and Saturation Correction (187), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, November 2009 (techreport)

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Efficient Filter Flow for Space-Variant Multiframe Blind Deconvolution

Hirsch, M., Sra, S., Schölkopf, B., Harmeling, S.

(188), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, November 2009 (techreport)

Abstract
Ultimately being motivated by facilitating space-variant blind deconvolution, we present a class of linear transformations, that are expressive enough for space-variant filters, but at the same time especially designed for efficient matrix-vector-multiplications. Successful results on astronomical imaging through atmospheric turbulences and on noisy magnetic resonance images of constantly moving objects demonstrate the practical significance of our approach.

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Hirsch, M., Sra, S., Schölkopf, B., Harmeling, S. Efficient Filter Flow for Space-Variant Multiframe Blind Deconvolution (188), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, November 2009 (techreport)

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Algebraic polynomials and moments of stochastic integrals

Langovoy, M.

(2009-031), EURANDOM, Technische Universiteit Eindhoven, October 2009 (techreport)

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Langovoy, M. Algebraic polynomials and moments of stochastic integrals (2009-031), EURANDOM, Technische Universiteit Eindhoven, October 2009 (techreport)

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Expectation Propagation on the Maximum of Correlated Normal Variables

Hennig, P.

Cavendish Laboratory: University of Cambridge, July 2009 (techreport)

Abstract
Many inference problems involving questions of optimality ask for the maximum or the minimum of a finite set of unknown quantities. This technical report derives the first two posterior moments of the maximum of two correlated Gaussian variables and the first two posterior moments of the two generating variables (corresponding to Gaussian approximations minimizing relative entropy). It is shown how this can be used to build a heuristic approximation to the maximum relationship over a finite set of Gaussian variables, allowing approximate inference by Expectation Propagation on such quantities.

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Hennig, P. Expectation Propagation on the Maximum of Correlated Normal Variables Cavendish Laboratory: University of Cambridge, July 2009 (techreport)

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Consistent Nonparametric Tests of Independence

Gretton, A., Györfi, L.

(172), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, July 2009 (techreport)

Abstract
Three simple and explicit procedures for testing the independence of two multi-dimensional random variables are described. Two of the associated test statistics (L1, log-likelihood) are defined when the empirical distribution of the variables is restricted to finite partitions. A third test statistic is defined as a kernel-based independence measure. Two kinds of tests are provided. Distribution-free strong consistent tests are derived on the basis of large deviation bounds on the test statistcs: these tests make almost surely no Type I or Type II error after a random sample size. Asymptotically alpha-level tests are obtained from the limiting distribution of the test statistics. For the latter tests, the Type I error converges to a fixed non-zero value alpha, and the Type II error drops to zero, for increasing sample size. All tests reject the null hypothesis of independence if the test statistics become large. The performance of the tests is evaluated experimentally on benchmark data.

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Gretton, A., Györfi, L. Consistent Nonparametric Tests of Independence (172), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, July 2009 (techreport)

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Semi-supervised subspace analysis of human functional magnetic resonance imaging data

Shelton, J., Blaschko, M., Bartels, A.

(185), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, May 2009 (techreport)

Abstract
Kernel Canonical Correlation Analysis is a very general technique for subspace learning that incorporates PCA and LDA as special cases. Functional magnetic resonance imaging (fMRI) acquired data is naturally amenable to these techniques as data are well aligned. fMRI data of the human brain is a particularly interesting candidate. In this study we implemented various supervised and semi-supervised versions of KCCA on human fMRI data, with regression to single- and multi-variate labels (corresponding to video content subjects viewed during the image acquisition). In each variate condition, the semi-supervised variants of KCCA performed better than the supervised variants, including a supervised variant with Laplacian regularization. We additionally analyze the weights learned by the regression in order to infer brain regions that are important to different types of visual processing.

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Shelton, J., Blaschko, M., Bartels, A. Semi-supervised subspace analysis of human functional magnetic resonance imaging data (185), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, May 2009 (techreport)

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Model selection, large deviations and consistency of data-driven tests

Langovoy, M.

(2009-007), EURANDOM, Technische Universiteit Eindhoven, March 2009 (techreport)

Abstract
We consider three general classes of data-driven statistical tests. Neyman's smooth tests, data-driven score tests and data-driven score tests for statistical inverse problems serve as important special examples for the classes of tests under consideration. Our tests are additionally incorporated with model selection rules. The rules are based on the penalization idea. Most of the optimal penalties, derived in statistical literature, can be used in our tests. We prove general consistency theorems for the tests from those classes. Our proofs make use of large deviations inequalities for deterministic and random quadratic forms. The paper shows that the tests can be applied for simple and composite parametric, semi- and nonparametric hypotheses. Applications to testing in statistical inverse problems and statistics for stochastic processes are also presented..

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Langovoy, M. Model selection, large deviations and consistency of data-driven tests (2009-007), EURANDOM, Technische Universiteit Eindhoven, March 2009 (techreport)

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1998

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Generalization bounds and learning rates for Regularized principal manifolds

Smola, A., Williamson, R., Schölkopf, B.

NeuroCOLT, 1998, NeuroColt2-TR 1998-027 (techreport)

[BibTex]

1998

Smola, A., Williamson, R., Schölkopf, B. Generalization bounds and learning rates for Regularized principal manifolds NeuroCOLT, 1998, NeuroColt2-TR 1998-027 (techreport)

[BibTex]

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Generalization Bounds for Convex Combinations of Kernel Functions

Smola, A., Williamson, R., Schölkopf, B.

Royal Holloway College, 1998 (techreport)

[BibTex]

Smola, A., Williamson, R., Schölkopf, B. Generalization Bounds for Convex Combinations of Kernel Functions Royal Holloway College, 1998 (techreport)

[BibTex]

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Generalization Performance of Regularization Networks and Support Vector Machines via Entropy Numbers of Compact Operators

Williamson, R., Smola, A., Schölkopf, B.

(19), NeuroCOLT, 1998, Accepted for publication in IEEE Transactions on Information Theory (techreport)

[BibTex]

Williamson, R., Smola, A., Schölkopf, B. Generalization Performance of Regularization Networks and Support Vector Machines via Entropy Numbers of Compact Operators (19), NeuroCOLT, 1998, Accepted for publication in IEEE Transactions on Information Theory (techreport)

[BibTex]

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Quantization Functionals and Regularized PrincipalManifolds

Smola, A., Mika, S., Schölkopf, B.

NeuroCOLT, 1998, NC2-TR-1998-028 (techreport)

[BibTex]

Smola, A., Mika, S., Schölkopf, B. Quantization Functionals and Regularized PrincipalManifolds NeuroCOLT, 1998, NC2-TR-1998-028 (techreport)

[BibTex]

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Support Vector Machine Reference Manual

Saunders, C., Stitson, M., Weston, J., Bottou, L., Schölkopf, B., Smola, A.

(CSD-TR-98-03), Department of Computer Science, Royal Holloway, University of London, 1998 (techreport)

PostScript [BibTex]

Saunders, C., Stitson, M., Weston, J., Bottou, L., Schölkopf, B., Smola, A. Support Vector Machine Reference Manual (CSD-TR-98-03), Department of Computer Science, Royal Holloway, University of London, 1998 (techreport)

PostScript [BibTex]