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2018


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Probabilistic Solutions To Ordinary Differential Equations As Non-Linear Bayesian Filtering: A New Perspective

Tronarp, F., Kersting, H., Särkkä, S., Hennig, P.

ArXiv preprint 2018, arXiv:1810.03440 [stat.ME], October 2018 (article)

Abstract
We formulate probabilistic numerical approximations to solutions of ordinary differential equations (ODEs) as problems in Gaussian process (GP) regression with non-linear measurement functions. This is achieved by defining the measurement sequence to consists of the observations of the difference between the derivative of the GP and the vector field evaluated at the GP---which are all identically zero at the solution of the ODE. When the GP has a state-space representation, the problem can be reduced to a Bayesian state estimation problem and all widely-used approximations to the Bayesian filtering and smoothing problems become applicable. Furthermore, all previous GP-based ODE solvers, which were formulated in terms of generating synthetic measurements of the vector field, come out as specific approximations. We derive novel solvers, both Gaussian and non-Gaussian, from the Bayesian state estimation problem posed in this paper and compare them with other probabilistic solvers in illustrative experiments.

pn

link (url) Project Page [BibTex]

2018



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Direct Sparse Odometry With Rolling Shutter

Schubert, D., Usenko, V., Demmel, N., Stueckler, J., Cremers, D.

European Conference on Computer Vision (ECCV), September 2018, accepted as oral presentation (conference)

ev

[BibTex]

[BibTex]


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Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry

Yang, N., Wang, R., Stueckler, J., Cremers, D.

European Conference on Computer Vision (ECCV), September 2018, accepted as oral presentation, arXiv 1807.02570 (conference)

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link (url) [BibTex]

link (url) [BibTex]


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Discovering and Teaching Optimal Planning Strategies

Lieder, F., Callaway, F., Krueger, P. M., Das, P., Griffiths, T. L., Gul, S.

In The 14th biannual conference of the German Society for Cognitive Science, GK, September 2018 (inproceedings)

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Project Page [BibTex]

Project Page [BibTex]


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Discovering Rational Heuristics for Risky Choice

Gul, S., Krueger, P. M., Callaway, F., Griffiths, T. L., Lieder, F.

The 14th biannual conference of the German Society for Cognitive Science, GK, The 14th biannual conference of the German Society for Cognitive Science, GK, September 2018 (conference)

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Project Page [BibTex]

Project Page [BibTex]


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Kernel Recursive ABC: Point Estimation with Intractable Likelihood

Kajihara, T., Kanagawa, M., Yamazaki, K., Fukumizu, K.

Proceedings of the 35th International Conference on Machine Learning, pages: 2405-2414, PMLR, July 2018 (conference)

Abstract
We propose a novel approach to parameter estimation for simulator-based statistical models with intractable likelihood. Our proposed method involves recursive application of kernel ABC and kernel herding to the same observed data. We provide a theoretical explanation regarding why the approach works, showing (for the population setting) that, under a certain assumption, point estimates obtained with this method converge to the true parameter, as recursion proceeds. We have conducted a variety of numerical experiments, including parameter estimation for a real-world pedestrian flow simulator, and show that in most cases our method outperforms existing approaches.

pn

Paper [BibTex]

Paper [BibTex]


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Convergence Rates of Gaussian ODE Filters

Kersting, H., Sullivan, T. J., Hennig, P.

arXiv preprint 2018, arXiv:1807.09737 [math.NA], July 2018 (article)

Abstract
A recently-introduced class of probabilistic (uncertainty-aware) solvers for ordinary differential equations (ODEs) applies Gaussian (Kalman) filtering to initial value problems. These methods model the true solution $x$ and its first $q$ derivatives a priori as a Gauss--Markov process $\boldsymbol{X}$, which is then iteratively conditioned on information about $\dot{x}$. We prove worst-case local convergence rates of order $h^{q+1}$ for a wide range of versions of this Gaussian ODE filter, as well as global convergence rates of order $h^q$ in the case of $q=1$ and an integrated Brownian motion prior, and analyse how inaccurate information on $\dot{x}$ coming from approximate evaluations of $f$ affects these rates. Moreover, we present explicit formulas for the steady states and show that the posterior confidence intervals are well calibrated in all considered cases that exhibit global convergence---in the sense that they globally contract at the same rate as the truncation error.

pn

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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The TUM VI Benchmark for Evaluating Visual-Inertial Odometry

Schubert, D., Goll, T., Demmel, N., Usenko, V., Stueckler, J., Cremers, D.

In IEEE International Conference on Intelligent Robots and Systems (IROS), 2018, arXiv:1804.06120 (inproceedings)

ev

[BibTex]

[BibTex]


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Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences

Kanagawa, M., Hennig, P., Sejdinovic, D., Sriperumbudur, B. K.

Arxiv e-prints, arXiv:1805.08845v1 [stat.ML], 2018 (article)

Abstract
This paper is an attempt to bridge the conceptual gaps between researchers working on the two widely used approaches based on positive definite kernels: Bayesian learning or inference using Gaussian processes on the one side, and frequentist kernel methods based on reproducing kernel Hilbert spaces on the other. It is widely known in machine learning that these two formalisms are closely related; for instance, the estimator of kernel ridge regression is identical to the posterior mean of Gaussian process regression. However, they have been studied and developed almost independently by two essentially separate communities, and this makes it difficult to seamlessly transfer results between them. Our aim is to overcome this potential difficulty. To this end, we review several old and new results and concepts from either side, and juxtapose algorithmic quantities from each framework to highlight close similarities. We also provide discussions on subtle philosophical and theoretical differences between the two approaches.

pn

arXiv [BibTex]

arXiv [BibTex]


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Learning to select computations

Callaway, F., Gul, S., Krueger, P., Griffiths, T. L., Lieder, F.

In Uncertainty in Artificial Intelligence: Proceedings of the Thirty-Fourth Conference, 2018 (inproceedings)

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Project Page [BibTex]

Project Page [BibTex]


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Variational Network Quantization

Achterhold, J., Koehler, J. M., Schmeink, A., Genewein, T.

In International Conference on Learning Representations , 2018 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Schema-related cognitive load influences performance, speech, and physiology in a dual-task setting: A continuous multi-measure approach

Wirzberger, M., Herms, R., Esmaeili Bijarsari, S., Eibl, M., Rey, G. D.

Cognitive Research: Principles and Implications, 3:46, Springer Nature, 2018 (article)

Abstract
Schema acquisition processes comprise an essential source of cognitive demands in learning situations. To shed light on related mechanisms and influencing factors, this study applied a continuous multi-measure approach for cognitive load assessment. In a dual-task setting, a sample of 123 student participants learned visually presented symbol combinations with one of two levels of complexity while memorizing auditorily presented number sequences. Learners’ cognitive load during the learning task was addressed by secondary task performance, prosodic speech parameters (pauses, articulation rate), and physiological markers (heart rate, skin conductance response). While results revealed increasing primary and secondary task performance over the trials, decreases in speech and physiological parameters indicated a reduction in the overall level of cognitive load with task progression. In addition, the robustness of the acquired schemata was confirmed by a transfer task that required participants to apply the obtained symbol combinations. Taken together, the observed pattern of evidence supports the idea of a logarithmically decreasing progression of cognitive load with increasing schema acquisition, and further hints on robust and stable transfer performance, even under enhanced transfer demands. Finally, theoretical and practical consequences consider evidence on desirable difficulties in learning as well as the potential of multimodal cognitive load detection in learning applications.

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DOI [BibTex]

DOI [BibTex]


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Numerical Quadrature for Probabilistic Policy Search

Vinogradska, J., Bischoff, B., Achterhold, J., Koller, T., Peters, J.

IEEE Transactions on Pattern Analysis and Machine Intelligence, pages: 1-1, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients

Balles, L., Hennig, P.

In Proceedings of the 35th International Conference on Machine Learning (ICML), 2018 (inproceedings) Accepted

Abstract
The ADAM optimizer is exceedingly popular in the deep learning community. Often it works very well, sometimes it doesn't. Why? We interpret ADAM as a combination of two aspects: for each weight, the update direction is determined by the sign of stochastic gradients, whereas the update magnitude is determined by an estimate of their relative variance. We disentangle these two aspects and analyze them in isolation, gaining insight into the mechanisms underlying ADAM. This analysis also extends recent results on adverse effects of ADAM on generalization, isolating the sign aspect as the problematic one. Transferring the variance adaptation to SGD gives rise to a novel method, completing the practitioner's toolbox for problems where ADAM fails.

pn

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Omnidirectional DSO: Direct Sparse Odometry with Fisheye Cameras

Matsuki, H., von Stumberg, L., Usenko, V., Stueckler, J., Cremers, D.

IEEE Robotics and Automation Letters (RA-L) & Int. Conference on Intelligent Robots and Systems (IROS), Robotics and Automation Letters (RA-L), IEEE, 2018 (article)

ev

[BibTex]

[BibTex]


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Counterfactual Mean Embedding: A Kernel Method for Nonparametric Causal Inference

Muandet, K., Kanagawa, M., Saengkyongam, S., Marukata, S.

Arxiv e-prints, arXiv:1805.08845v1 [stat.ML], 2018 (article)

Abstract
This paper introduces a novel Hilbert space representation of a counterfactual distribution---called counterfactual mean embedding (CME)---with applications in nonparametric causal inference. Counterfactual prediction has become an ubiquitous tool in machine learning applications, such as online advertisement, recommendation systems, and medical diagnosis, whose performance relies on certain interventions. To infer the outcomes of such interventions, we propose to embed the associated counterfactual distribution into a reproducing kernel Hilbert space (RKHS) endowed with a positive definite kernel. Under appropriate assumptions, the CME allows us to perform causal inference over the entire landscape of the counterfactual distribution. The CME can be estimated consistently from observational data without requiring any parametric assumption about the underlying distributions. We also derive a rate of convergence which depends on the smoothness of the conditional mean and the Radon-Nikodym derivative of the underlying marginal distributions. Our framework can deal with not only real-valued outcome, but potentially also more complex and structured outcomes such as images, sequences, and graphs. Lastly, our experimental results on off-policy evaluation tasks demonstrate the advantages of the proposed estimator.

ei pn

arXiv [BibTex]

arXiv [BibTex]


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Model-based Kernel Sum Rule: Kernel Bayesian Inference with Probabilistic Models

Nishiyama, Y., Kanagawa, M., Gretton, A., Fukumizu, K.

Arxiv e-prints, arXiv:1409.5178v2 [stat.ML], 2018 (article)

Abstract
Kernel Bayesian inference is a powerful nonparametric approach to performing Bayesian inference in reproducing kernel Hilbert spaces or feature spaces. In this approach, kernel means are estimated instead of probability distributions, and these estimates can be used for subsequent probabilistic operations (as for inference in graphical models) or in computing the expectations of smooth functions, for instance. Various algorithms for kernel Bayesian inference have been obtained by combining basic rules such as the kernel sum rule (KSR), kernel chain rule, kernel product rule and kernel Bayes' rule. However, the current framework only deals with fully nonparametric inference (i.e., all conditional relations are learned nonparametrically), and it does not allow for flexible combinations of nonparametric and parametric inference, which are practically important. Our contribution is in providing a novel technique to realize such combinations. We introduce a new KSR referred to as the model-based KSR (Mb-KSR), which employs the sum rule in feature spaces under a parametric setting. Incorporating the Mb-KSR into existing kernel Bayesian framework provides a richer framework for hybrid (nonparametric and parametric) kernel Bayesian inference. As a practical application, we propose a novel filtering algorithm for state space models based on the Mb-KSR, which combines the nonparametric learning of an observation process using kernel mean embedding and the additive Gaussian noise model for a state transition process. While we focus on additive Gaussian noise models in this study, the idea can be extended to other noise models, such as the Cauchy and alpha-stable noise models.

pn

arXiv [BibTex]

arXiv [BibTex]


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Attention please! Enhanced attention control abilities compensate for instructional impairments in multimedia learning

Wirzberger, M., Rey, G. D.

Journal of Computers in Education, 5(2):243-257, Springer Nature, 2018 (article)

Abstract
Learners exposed to multimedia learning contexts have to deal with a variety of visual stimuli, demanding a conducive design of learning material to maintain limitations in attentional resources. Within the current study, effects and constraints arising from two selected impairing features are investigated in more detail within a computer-based learning task on factor analysis. A sample of 53 students received a combination of textual and pictorial elements that explained the topic, while impaired attention was systematically induced in a 2 × 2 factorial between-subjects design by interrupting system-notifications (with vs. without) and seductive text passages (with vs. without). Learners’ ability for controlled attention was assessed with a standardized psychological attention inventory. Approaching the results, learners receiving seductive text passages spent significantly more time on the learning material. In addition, a moderation effect of attention control abilities on the relationship between interruptions and retention performance resulted. Explanations for the obtained findings are discussed referring to mechanisms of compensation, load, and activation.

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DOI [BibTex]

DOI [BibTex]


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A probabilistic model for the numerical solution of initial value problems

Schober, M., Särkkä, S., Philipp Hennig,

Statistics and Computing, Springer US, 2018 (article)

Abstract
We study connections between ordinary differential equation (ODE) solvers and probabilistic regression methods in statistics. We provide a new view of probabilistic ODE solvers as active inference agents operating on stochastic differential equation models that estimate the unknown initial value problem (IVP) solution from approximate observations of the solution derivative, as provided by the ODE dynamics. Adding to this picture, we show that several multistep methods of Nordsieck form can be recast as Kalman filtering on q-times integrated Wiener processes. Doing so provides a family of IVP solvers that return a Gaussian posterior measure, rather than a point estimate. We show that some such methods have low computational overhead, nontrivial convergence order, and that the posterior has a calibrated concentration rate. Additionally, we suggest a step size adaptation algorithm which completes the proposed method to a practically useful implementation, which we experimentally evaluate using a representative set of standard codes in the DETEST benchmark set.

pn

PDF Code DOI Project Page [BibTex]


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Light field intrinsics with a deep encoder-decoder network

Alperovich, A., Johannsen, O., Strecke, M., Goldluecke, B.

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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The Computational Challenges of Pursuing Multiple Goals: Network Structure of Goal Systems Predicts Human Performance

Reichman, D., Lieder, F., Bourgin, D. D., Talmon, N., Griffiths, T. L.

PsyArXiv, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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The moderating role of arousal on the seductive detail effect in a multimedia learning setting

Schneider, S., Wirzberger, M., Rey, G. D.

Applied Cognitive Psychology, Wiley, 2018 (article)

Abstract
Arousal has been found to increase learners' attentional resources. In contrast, seductive details (interesting but learning‐irrelevant information) are considered to distract attention away from relevant information and, thus, hinder learning. However, a possibly moderating role of arousal on the seductive detail effect has not been examined yet. In this study, arousal variations were induced via audio files of false heartbeats. In consequence, 100 participants were randomly assigned to a 2 (with or without seductive details) × 2 (lower vs. higher false heart rates) between‐subjects design. Data on learning performance, cognitive load, motivation, heartbeat frequency, and electro‐dermal activity were collected. Results show learning‐inhibiting effects for seductive details and learning‐enhancing effects for higher false heart rates. Cognitive processes mediate both effects. However, the detrimental effect of seductive details was not present when heart rate was higher. Results indicate that the seductive detail effect is moderated by a learner's state of arousal.

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DOI [BibTex]

DOI [BibTex]


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Sublabel-accurate convex relaxation with total generalized variation regularization

(DAGM Best Master's Thesis Award)

Strecke, M., Goldluecke, B.

In German Conference on Pattern Recognition (Proc. GCPR), 2018 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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CoLoSS: Cognitive load corpus with speech and performance data from a symbol-digit dual-task

Herms, R., Wirzberger, M., Eibl, M., Rey, G. D.

In Proceedings of the 11th International Language Resources and Evaluation Conference (LREC 2018), pages: 4312-4317, European Language Resource Association (ELRA), Miyazaki, Japan, 2018 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Rational metareasoning and the plasticity of cognitive control

Lieder, F., Shenhav, A., Musslick, S., Griffiths, T. L.

{PLoS Computational Biology}, 14(4):e1006043, Public Library of Science, 2018 (article)

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Project Page [BibTex]

Project Page [BibTex]


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Influences of system response delay on elderly participants’ performance in a virtual memory training

Wirzberger, M., Schmidt, R., Georgi, M., Hardt, W., Brunnett, G., Rey, G. D.

In Annual Meeting of the Europe Chapter of the Human Factors and Ergonomics Society2018, Technology for an Aging Society, Book of Abstracts, pages: 42, Berlin, 2018 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Over-representation of extreme events in decision making reflects rational use of cognitive resources

Lieder, F., Griffiths, T. L., Hsu, M.

Psychological Review, 125(1):1-32, 2018 (article)

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[BibTex]

[BibTex]


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Cognitive load influences performance, speech and physiological parameters in a multimodal dual-task setting

Wirzberger, M., Herms, R., Esmaeili Bijarsari, S., Rey, G. D., Eibl, M.

In Abstracts of the 60th Conference of Experimental Psychologists, pages: 296, Pabst Science Publishers, Lengerich, 2018 (inproceedings)

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[BibTex]

[BibTex]


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Guidance or Setting? Exploring the learnability of computer-based instructions in a construction task

Esmaeili Bijarsari, S., Wirzberger, M., Rey, G. D.

In Abstracts of the 60th Conference of Experimental Psychologists, pages: 69, Pabst Science Publishers, Lengerich, 2018 (inproceedings)

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[BibTex]

[BibTex]


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A resource-rational analysis of human planning

Callaway, F., Lieder, F., Das, P., Gul, S., Krueger, P. M., Griffiths, T. L.

In Proceedings of the 40th Annual Conference of the Cognitive Science Society, 2018 (inproceedings)

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[BibTex]

[BibTex]


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Guidance or Setting? Exploring the learnability of computer-based instructions in a construction task

Esmaeili Bijarsari, S., Wirzberger, M., Rey, G. D.

In 51st Conference of the German Psychological Society. Abstracts, pages: 509, Pabst Science Publishers, Lengerich, 2018 (inproceedings)

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[BibTex]

[BibTex]

2012


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Quasi-Newton Methods: A New Direction

Hennig, P., Kiefel, M.

In Proceedings of the 29th International Conference on Machine Learning, pages: 25-32, ICML ’12, (Editors: John Langford and Joelle Pineau), Omnipress, New York, NY, USA, ICML, July 2012 (inproceedings)

Abstract
Four decades after their invention, quasi- Newton methods are still state of the art in unconstrained numerical optimization. Although not usually interpreted thus, these are learning algorithms that fit a local quadratic approximation to the objective function. We show that many, including the most popular, quasi-Newton methods can be interpreted as approximations of Bayesian linear regression under varying prior assumptions. This new notion elucidates some shortcomings of classical algorithms, and lights the way to a novel nonparametric quasi-Newton method, which is able to make more efficient use of available information at computational cost similar to its predecessors.

ei ps pn

website+code pdf link (url) [BibTex]

2012


website+code pdf link (url) [BibTex]


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Entropy Search for Information-Efficient Global Optimization

Hennig, P., Schuler, C.

Journal of Machine Learning Research, 13, pages: 1809-1837, -, June 2012 (article)

Abstract
Contemporary global optimization algorithms are based on local measures of utility, rather than a probability measure over location and value of the optimum. They thus attempt to collect low function values, not to learn about the optimum. The reason for the absence of probabilistic global optimizers is that the corresponding inference problem is intractable in several ways. This paper develops desiderata for probabilistic optimization algorithms, then presents a concrete algorithm which addresses each of the computational intractabilities with a sequence of approximations and explicitly adresses the decision problem of maximizing information gain from each evaluation.

ei pn

PDF Web Project Page [BibTex]

PDF Web Project Page [BibTex]


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Learning Tracking Control with Forward Models

Bócsi, B., Hennig, P., Csató, L., Peters, J.

In pages: 259 -264, IEEE International Conference on Robotics and Automation (ICRA), May 2012 (inproceedings)

Abstract
Performing task-space tracking control on redundant robot manipulators is a difficult problem. When the physical model of the robot is too complex or not available, standard methods fail and machine learning algorithms can have advantages. We propose an adaptive learning algorithm for tracking control of underactuated or non-rigid robots where the physical model of the robot is unavailable. The control method is based on the fact that forward models are relatively straightforward to learn and local inversions can be obtained via local optimization. We use sparse online Gaussian process inference to obtain a flexible probabilistic forward model and second order optimization to find the inverse mapping. Physical experiments indicate that this approach can outperform state-of-the-art tracking control algorithms in this context.

ei pn

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Approximate Gaussian Integration using Expectation Propagation

Cunningham, J., Hennig, P., Lacoste-Julien, S.

In pages: 1-11, -, January 2012 (inproceedings) Submitted

Abstract
While Gaussian probability densities are omnipresent in applied mathematics, Gaussian cumulative probabilities are hard to calculate in any but the univariate case. We offer here an empirical study of the utility of Expectation Propagation (EP) as an approximate integration method for this problem. For rectangular integration regions, the approximation is highly accurate. We also extend the derivations to the more general case of polyhedral integration regions. However, we find that in this polyhedral case, EP's answer, though often accurate, can be almost arbitrarily wrong. These unexpected results elucidate an interesting and non-obvious feature of EP not yet studied in detail, both for the problem of Gaussian probabilities and for EP more generally.

ei pn

Web [BibTex]

Web [BibTex]


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Kernel Topic Models

Hennig, P., Stern, D., Herbrich, R., Graepel, T.

In Fifteenth International Conference on Artificial Intelligence and Statistics, 22, pages: 511-519, JMLR Proceedings, (Editors: Lawrence, N. D. and Girolami, M.), JMLR.org, AISTATS , 2012 (inproceedings)

Abstract
Latent Dirichlet Allocation models discrete data as a mixture of discrete distributions, using Dirichlet beliefs over the mixture weights. We study a variation of this concept, in which the documents' mixture weight beliefs are replaced with squashed Gaussian distributions. This allows documents to be associated with elements of a Hilbert space, admitting kernel topic models (KTM), modelling temporal, spatial, hierarchical, social and other structure between documents. The main challenge is efficient approximate inference on the latent Gaussian. We present an approximate algorithm cast around a Laplace approximation in a transformed basis. The KTM can also be interpreted as a type of Gaussian process latent variable model, or as a topic model conditional on document features, uncovering links between earlier work in these areas.

ei pn

PDF Web [BibTex]

PDF Web [BibTex]


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Model Learning and Real-Time Tracking Using Multi-Resolution Surfel Maps

Stueckler, J., Behnke, S.

Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2012 (conference)

ev

link (url) [BibTex]

link (url) [BibTex]


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RoboCup@Home: Demonstrating Everyday Manipulation Skills in RoboCup@Home

Stueckler, J., Holz, D., Behnke, S.

IEEE Robotics and Automation Magazine (RAM), 19(2):34-42, 2012 (article)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Bayesian calibration of the hand-eye kinematics of an anthropomorphic robot

Hubert, U., Stueckler, J., Behnke, S.

In Proc. of the 12th IEEE-RAS Int. Conf. on Humanoid Robots (Humanoids), pages: 618-624, November 2012 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Shape-Primitive Based Object Recognition and Grasping

Nieuwenhuisen, M., Stueckler, J., Berner, A., Klein, R., Behnke, S.

In Proc. of ROBOTIK, VDE-Verlag, 2012 (inproceedings)

ev

link (url) [BibTex]

link (url) [BibTex]


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Semantic mapping using object-class segmentation of RGB-D images

Stueckler, J., Biresev, N., Behnke, S.

In Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), pages: 3005-3010, October 2012 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Efficient Mobile Robot Navigation using 3D Surfel Grid Maps

Kläß, J., Stueckler, J., Behnke, S.

In Proc. of ROBOTIK, VDE-Verlag, 2012 (inproceedings)

ev

link (url) [BibTex]

link (url) [BibTex]


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Integrating depth and color cues for dense multi-resolution scene mapping using RGB-D cameras

Stueckler, J., Behnke, S.

In Proc. of the IEEE Int. Conf. on Multisensor Fusion and Integration for Intelligent Systems (MFI), pages: 162-167, sep 2012 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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SURE: Surface Entropy for Distinctive 3D Features

Fiolka, T., Stueckler, J., Klein, D. A., Schulz, D., Behnke, S.

In Proc. of Spatial Cognition, 2012 (inproceedings)

ev

link (url) [BibTex]

link (url) [BibTex]


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Robust Real-Time Registration of RGB-D Images using Multi-Resolution Surfel Representations

Stueckler, J., Behnke, S.

In Proc. of ROBOTIK, VDE-Verlag, 2012 (inproceedings)

ev

link (url) [BibTex]

link (url) [BibTex]


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Burn-in, bias, and the rationality of anchoring

Lieder, F., Griffiths, T. L., Goodman, N. D.

Advances in Neural Information Processing Systems 25, pages: 2699-2707, 2012 (article)

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[BibTex]

[BibTex]


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Adjustable autonomy for mobile teleoperation of personal service robots

Muszynski, S., Stueckler, J., Behnke, S.

In Proc. of the IEEE Int. Symp. on Robot and Human Interactive Communication, pages: 933-940, sep 2012 (inproceedings)

ev

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Adaptive Multi-cue 3D Tracking of Arbitrary Objects

Garcia, G. M., Klein, D. A., Stueckler, J., Frintrop, S., Cremers, A. B.

In DAGM/OAGM Symposium, 7476, pages: 357-366, Lecture Notes in Computer Science, Springer, 2012 (inproceedings)

ev

[BibTex]

[BibTex]

2007


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Point-spread functions for backscattered imaging in the scanning electron microscope

Hennig, P., Denk, W.

Journal of Applied Physics , 102(12):1-8, December 2007 (article)

Abstract
One knows the imaging system's properties are central to the correct interpretation of any image. In a scanning electron microscope regions of different composition generally interact in a highly nonlinear way during signal generation. Using Monte Carlo simulations we found that in resin-embedded, heavy metal-stained biological specimens staining is sufficiently dilute to allow an approximately linear treatment. We then mapped point-spread functions for backscattered-electron contrast, for primary energies of 3 and 7 keV and for different detector specifications. The point-spread functions are surprisingly well confined (both laterally and in depth) compared even to the distribution of only those scattered electrons that leave the sample again.

ei pn

Web DOI [BibTex]

2007


Web DOI [BibTex]