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European Signal processing Conference

作者： Alvaro Guevara Rudolf Mester Visual Sensorics and Information Processing Lab (VSI) Computer Science Dept. Goethe University

This paper addresses the problem of interpolating a (non-bandlimited) signal from a discrete set of noisy measurements obtained from non-δ sampling kernels. We present a linear estimation approach, assuming the signal is given by a continuous model for which first and second order moments are known. The formula provides a generalization of the well-known discrete-discrete Wiener style estimator, but does not necessarily involve Fourier domain considerations. Finally, some experiments illustrate the flexibility of the method under strong noise and aliasing effects, and shows how the input autocorrelation, the sampling kernel and the noise process shape the form of the optimal interpolating kernels.

关键词： signals Flexibility methods discrete set Seeds NUCLEOPORIN, 214-KD Noise calcium ammonium nitrate Interpolation formula Mini Mental State Examination Moment of force aliasing effect Flexibility Kernel Generalization

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Optical Rails: View-Based Track Following with Hemispherical Environment Model and Orientation View Descriptors

Optical Rails: View-Based Track Following with Hemispherical...

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International Conference on Pattern Recognition

作者： David Dederscheck Martin Zahn Holger Friedrich Rudolf Mester Visual Sensorics and Information Processing Laboratory (VSI) Computer Sciences Department J. W. Goethe Universitat Frankfurt Frankfurt Germany

ISBN: (纸本)9781424475421

We present a purely view-based method for robot navigation along a prerecorded track using compact omni directional view-descriptors. This paper focuses on a new model for the navigation environment to determine the steering direction by efficient holistic comparison of views. The concept of view descriptors based on low-order expansion of local orientation vectors into spherical harmonic basis functions is augmented by a linear illumination model, providing discriminative view matching also under illumination changes.

关键词： Lighting Navigation Robot kinematics Optical imaging Rails Brightness

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'Featuring' optical rails: View-based robot guidance using orientation features on the sphere

'Featuring' optical rails: View-based robot guidance using o...

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2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009

作者： Dederscheck, David Friedrich, Holger Lenhart, Christine Zahn, Martin Mester, Rudolf Goethe University Frankfurt Visual Sensorics and Information Processing Lab. Robert-Mayer-Str. 10 60325 Frankfurt am Main Germany

ISBN: (纸本)9781424444427

In this paper, we propose the extension of a view-based method for autonomous track following formerly introduced as Optical Rails by [3, 6], to vector-valued feature images. Instead of gray or color values, the whole analysis works on local orientations, extracted from omnidirectional input images and represented as low-frequency vector-valued view descriptors using spherical harmonics. New signal processing schemes on the sphere are presented for such view descriptors, allowing for efficient approximation, comparison, and differential motion estimation also for incomplete spherical signals. Track following is performed using only visual information, and no auxiliary guidance systems or odometry information is used. We present first results of track following with a mobile robot in an indoor environment, which demonstrates the feasibility of this novel approach. ©2009 IEEE.

关键词： Spheres

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Statistical Certainty Models in Image processing

Statistical Certainty Models in Image Processing

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IEEE/SP Workshop on Statistical Signal processing (SSP)

作者： Rudolf Mester Visual Sensorics and Information Processing Lab Computer Science Department Johann Wolfgang Goethe-University of Frankfurt Frankfurt Germany

This paper revisits the fundamental question of how uncertain or unknown signal values should be appropriately dealt with in signal and image processing. It puts the concept of certainty and applicability values proposed by Westin and Knutsson in contrast to a statistical model where the state of knowledge on individual signal values is expressed by expectation values and variances, while the structure of signals is espressed by covariance functions, or covariance matrices, respectively. Using this simple approach, and exploiting the power of the Gauss-Markov-Theorem, a linear signal processing framework is obtained which is both powerful, generally applicable, and encompasses the classical certainty/applicability approach, providing answers to the open issues in that classical theory.

关键词： Image processing Interpolation Filters Signal processing Convolution Smoothing methods information processing Computer science Covariance matrix Gaussian processes

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Highly Accurate Orientation Estimation using Steerable Filters

Highly Accurate Orientation Estimation using Steerable Filte...

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IEEE International Conference on Image processing

作者： Jakub K. Kominiarczuk Kai Krajsek Rudolf Mester Vortical Flow Research Laboratory Massachusetts Institute of Technology Cambridge MA USA Visual Sensorics and Information Processing Laboratory Johann Wolfgang Goethe-University of Frankfurt Frankfurt am Main Germany

This paper presents a generalized theoretical framework for designing accurate steerable filters for orientation estimation. We derive the necessary properties of orientation estimation filters in their most general form. Based on our framework, we implemented a highly angular-specific filter. Numerical experiments show the enhanced accuracy of our proposed filter as compared with existing filters.

关键词： Nonlinear filters Equations Taylor series information filtering information filters information processing Shape Fourier transforms Vectors Image processing

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A Maximum Likelihood Estimator for Choosing the Regularization Parameters in Global Optical Flow Methods

A Maximum Likelihood Estimator for Choosing the Regularizati...

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IEEE International Conference on Image processing

作者： Kai Krajsek Rudolf Mester Visual Sensorics and Information Processing Laboratory Johann Wolfgang Goethe-University of Frankfurt Frankfurt Germany

Global optical flow estimation methods based on variational calculus contain a regularization parameter which controls the tradeoff between the different constraints on the optical flow field. The counterpart to the regularization parameter are the hyper-parameters in the Bayesian framework. These hyper-parameters have distinct physical meanings and thus can be inferred from the observable data. We derive a combined marginal maximum likelihood/maximum a posteriori (MML/MAP) estimator for simultaneously estimating hyper-parameters and optical flow for all differential variational approaches directly from the observed signal without any prior knowledge of the optical flow. Experiments demonstrate the performance of this optimization technique and show that the choice of the regularization parameter is an essential key-point in order to obtain precise motion estimation.

关键词： Maximum likelihood estimation Image motion analysis Motion estimation Optical sensors Bayesian methods Statistics Optical noise Brightness Equations information processing

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On the equivalence of variational and statistical differential motion estimation

On the equivalence of variational and statistical differenti...

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IEEE Southwest Symposium on Image Analysis and Interpretation

作者： Kai Krajsek R. Mester Institute for Computer Science Visual Sensorics and Information Processing Laboratory Johann Wolfgang Goethe-University of Frankfurt Frankfurt Germany

ISBN: (纸本)1424400694

In this contribution, we examine variational based motion estimation techniques, e.g. (B. Horn and B. Schunck, 1981), (A. Bruhn, et al., 2005), from a statistical point of view. The fact that all deterministic motivated methods can be described in a Bayesian framework allows the understanding of the physical meaning of the parameters which occur as free parameters in the deterministic framework. Furthermore, these parameters can directly be estimated from observable data when choosing the statistical point of view. The estimation of the optimal regularization parameter is demonstrated to work successfully on image sequences with known ground truth

关键词： Motion estimation Image motion analysis Bayesian methods Brightness Equations Least squares approximation Computer science information processing Image sequences Optical sensors

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Marginalized Maximum a Posteriori Hyper‐parameter Estimation for Global Optical Flow Techniques

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AIP Conference Proceedings 2006年第1期872卷 311-318页

作者： Kai Krajsek Rudolf Mester Visual Sensorics and Information Processing Lab Institute for Computer Science J. W. Goethe University Robert Mayer Str. 2‐4 60054 Frankfurt am Main Germany

Global optical flow estimation methods contain a regularization parameter (or prior and likelihood hyper‐parameters if we consider the statistical point of view) which control the tradeoff between the different constraints on the optical flow field. Although experiments (see e.g. Ng et al. [Ng and Solo(1997)]) indicate the importance of the optimal choice of the hyper‐parameters, only little attention has been focused on the optimal choice of these parameters in global motion estimation techniques in literature so far (the authors are only aware of one contribution [Ng and Solo(1997)] which attempts to estimate only the prior hyper‐parameter whereas the likelihood hyper‐parameter needs to be known). We adapt the marginalized maximum a posteriori (MMAP) estimator proposed in [Mohammad‐Djafari(1995)] to simultaneously estimating hyper‐parameters and optical flow for global motion estimation techniques. Experiments demonstrate the performance of this optimization technique and show that the choice of the regularization parameter/hyper‐parameters is an essential key‐point in order to obtain precise motion estimates.

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Statistical Methods in Video processing 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Dorin Comaniciu Rudolf Mester Kenichi Kanatani David Suter

ISBN: (数字)9783540302124

ISBN: (纸本)9783540239895

The 2nd International Workshop on Statistical Methods in Video processing, SMVP 2004, was held in Prague, Czech Republic, as an associated workshop of ECCV 2004, the 8th European Conference on Computer Vision. A total of 30 papers were submitted to the workshop. Of these, 17 papers were accepted for presentation and included in these proceedings, following a double-blind review process. The workshop had 42 registered participants. The focus of the meeting was on recent progress in the application of - vanced statistical methods to solve computer vision tasks. The one-day scienti?c program covered areas of high interest in vision research, such as dense rec- struction of 3D scenes, multibody motion segmentation, 3D shape inference, errors-in-variables estimation, probabilistic tracking, information fusion, optical ?owcomputation,learningfornonstationaryvideodata,noveltydetectionin- namic backgrounds, background modeling, grouping using feature uncertainty, and crowd segmentation from video. We wish to thank the authors of all submitted papers for their interest in the *** external reviewers for their commitment of time and e?ort in providing valuable recommendations for each submission. We are thankful to Vaclav Hlavac, the General Chair of ECCV 2004, and to Radim Sara, for the local organization of the workshop and registration management. We hope you will ?nd these proceedings both inspiring and of high scienti?c quality.

关键词： Image processing and Computer Vision Computer Graphics Pattern Recognition Probability and Statistics in Computer Science Artificial Intelligence Algorithm Analysis and Problem Complexity

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Pattern Recognition 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Rudolf Mester Michael Felsberg

ISBN: (数字)9783642231230

ISBN: (纸本)9783642231223

This book constitutes the refereed proceedings of the 33rd Symposium of the German Association for Pattern Recognition, DAGM 2011, held in Frankfurt/Main, Germany, in August/September 2011. The 20 revised full papers and 22 revised poster papers were carefully reviewed and selected from 98 submissions. The papers are organized in topical sections on object recognition, adverse vision conditions challenge, shape and matching, segmentation and early vision, robot vision, machine learning, and motion. The volume also includes the young researcher's forum, a section where a carefully jury-selected ensemble of young researchers present their Master thesis work.

关键词： Pattern Recognition Image processing and Computer Vision Artificial Intelligence Computer Graphics Algorithm Analysis and Problem Complexity information Systems Applications (incl. Internet)

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