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2015


Thumb xl 2016 peer grading
Peer grading in a course on algorithms and data structures

Sajjadi, M. S. M., Alamgir, M., von Luxburg, U.

Workshop on Machine Learning for Education (ML4Ed) at the 32th International Conference on Machine Learning (ICML), 2015 (conference)

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Arxiv [BibTex]

2015


Arxiv [BibTex]


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Joint 3D Object and Layout Inference from a single RGB-D Image

(Best Paper Award)

Geiger, A., Wang, C.

In German Conference on Pattern Recognition (GCPR), 9358, pages: 183-195, Lecture Notes in Computer Science, Springer International Publishing, 2015 (inproceedings)

Abstract
Inferring 3D objects and the layout of indoor scenes from a single RGB-D image captured with a Kinect camera is a challenging task. Towards this goal, we propose a high-order graphical model and jointly reason about the layout, objects and superpixels in the image. In contrast to existing holistic approaches, our model leverages detailed 3D geometry using inverse graphics and explicitly enforces occlusion and visibility constraints for respecting scene properties and projective geometry. We cast the task as MAP inference in a factor graph and solve it efficiently using message passing. We evaluate our method with respect to several baselines on the challenging NYUv2 indoor dataset using 21 object categories. Our experiments demonstrate that the proposed method is able to infer scenes with a large degree of clutter and occlusions.

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pdf suppmat video project DOI [BibTex]

pdf suppmat video project DOI [BibTex]


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Removing systematic errors for exoplanet search via latent causes

Schölkopf, B., Hogg, D., Wang, D., Foreman-Mackey, D., Janzing, D., Simon-Gabriel, C. J., Peters, J.

In Proceedings of The 32nd International Conference on Machine Learning, 37, pages: 2218–2226, JMLR Workshop and Conference Proceedings, (Editors: Bach, F. and Blei, D.), JMLR, ICML, 2015 (inproceedings)

ei

Extended version on arXiv link (url) [BibTex]

Extended version on arXiv link (url) [BibTex]


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Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components

Geiger, P., Zhang, K., Schölkopf, B., Gong, M., Janzing, D.

In Proceedings of the 32nd International Conference on Machine Learning, 37, pages: 1917–1925, JMLR Workshop and Conference Proceedings, (Editors: F. Bach and D. Blei), JMLR, ICML, 2015 (inproceedings)

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Brain-Computer Interfacing in Amyotrophic Lateral Sclerosis: Implications of a Resting-State EEG Analysis

Jayaram, V., Widmann, N., Förster, C., Fomina, T., Hohmann, M. R., Müller vom Hagen, J., Synofzik, M., Schölkopf, B., Schöls, L., Grosse-Wentrup, M.

In Proceedings of the 37th IEEE Conference for Engineering in Medicine and Biology, pages: 6979-6982, EMBC, 2015 (inproceedings)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Identification of the Default Mode Network with Electroencephalography

Fomina, T., Hohmann, M. R., Schölkopf, B., Grosse-Wentrup, M.

In Proceedings of the 37th IEEE Conference for Engineering in Medicine and Biology, pages: 7566-7569, EMBC, 2015 (inproceedings)

ei

DOI [BibTex]

DOI [BibTex]


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Towards Cognitive Brain-Computer Interfaces for Patients with Amyotrophic Lateral Sclerosis

Fomina, T., Schölkopf, B., Grosse-Wentrup, M.

In 7th Computer Science and Electronic Engineering Conference, pages: 77-80, Curran Associates, Inc., CEEC, 2015 (inproceedings)

ei

DOI [BibTex]

DOI [BibTex]


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Towards Learning Hierarchical Skills for Multi-Phase Manipulation Tasks

Kroemer, O., Daniel, C., Neumann, G., van Hoof, H., Peters, J.

In IEEE International Conference on Robotics and Automation, pages: 1503 - 1510, ICRA, 2015 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Probabilistic Line Searches for Stochastic Optimization

Mahsereci, M., Hennig, P.

In Advances in Neural Information Processing Systems 28, pages: 181-189, (Editors: C. Cortes, N.D. Lawrence, D.D. Lee, M. Sugiyama and R. Garnett), Curran Associates, Inc., 29th Annual Conference on Neural Information Processing Systems (NIPS), 2015 (inproceedings)

Abstract
In deterministic optimization, line searches are a standard tool ensuring stability and efficiency. Where only stochastic gradients are available, no direct equivalent has so far been formulated, because uncertain gradients do not allow for a strict sequence of decisions collapsing the search space. We construct a probabilistic line search by combining the structure of existing deterministic methods with notions from Bayesian optimization. Our method retains a Gaussian process surrogate of the univariate optimization objective, and uses a probabilistic belief over the Wolfe conditions to monitor the descent. The algorithm has very low computational cost, and no user-controlled parameters. Experiments show that it effectively removes the need to define a learning rate for stochastic gradient descent. [You can find the matlab research code under `attachments' below. The zip-file contains a minimal working example. The docstring in probLineSearch.m contains additional information. A more polished implementation in C++ will be published here at a later point. For comments and questions about the code please write to mmahsereci@tue.mpg.de.]

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Matlab research code link (url) [BibTex]

Matlab research code link (url) [BibTex]


Thumb xl rsz slip objects
Force estimation and slip detection/classification for grip control using a biomimetic tactile sensor

Su, Z., Hausman, K., Chebotar, Y., Molchanov, A., Loeb, G. E., Sukhatme, G. S., Schaal, S.

In IEEE-RAS International Conference on Humanoid Robots (Humanoids), pages: 297-303, 2015 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions

Rothenhäusler, D., Heinze, C., Peters, J., Meinshausen, N.

Advances in Neural Information Processing Systems 28, pages: 1513-1521, (Editors: C. Cortes, N.D. Lawrence, D.D. Lee, M. Sugiyama and R. Garnett), Curran Associates, Inc., 29th Annual Conference on Neural Information Processing Systems (NIPS), 2015 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Particle Gibbs for Infinite Hidden Markov Models

Tripuraneni*, N., Gu*, S., Ge, H., Ghahramani, Z.

Advances in Neural Information Processing Systems 28, pages: 2395-2403, (Editors: Corinna Cortes, Neil D. Lawrence, Daniel D. Lee, Masashi Sugiyama, and Roman Garnett), 29th Annual Conference on Neural Information Processing Systems (NIPS), 2015, *equal contribution (conference)

ei

PDF [BibTex]

PDF [BibTex]


Thumb xl 2016 peer grading
Peer grading in a course on algorithms and data structures

Sajjadi, M. S. M., Alamgir, M., von Luxburg, U.

Workshop on Crowdsourcing and Machine Learning (CrowdML) Workshop on Machine Learning for Education (ML4Ed) at at the 32th International Conference on Machine Learning (ICML), 2015 (conference)

ei

Arxiv [BibTex]

Arxiv [BibTex]


Thumb xl screen shot 2015 05 07 at 11.56.54
3D Object Class Detection in the Wild

Pepik, B., Stark, M., Gehler, P., Ritschel, T., Schiele, B.

In Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE, Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2015 (inproceedings)

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Project Page [BibTex]

Project Page [BibTex]


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Discrete Optimization for Optical Flow

Menze, M., Heipke, C., Geiger, A.

In German Conference on Pattern Recognition (GCPR), 9358, pages: 16-28, Springer International Publishing, 2015 (inproceedings)

Abstract
We propose to look at large-displacement optical flow from a discrete point of view. Motivated by the observation that sub-pixel accuracy is easily obtained given pixel-accurate optical flow, we conjecture that computing the integral part is the hardest piece of the problem. Consequently, we formulate optical flow estimation as a discrete inference problem in a conditional random field, followed by sub-pixel refinement. Naive discretization of the 2D flow space, however, is intractable due to the resulting size of the label set. In this paper, we therefore investigate three different strategies, each able to reduce computation and memory demands by several orders of magnitude. Their combination allows us to estimate large-displacement optical flow both accurately and efficiently and demonstrates the potential of discrete optimization for optical flow. We obtain state-of-the-art performance on MPI Sintel and KITTI.

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pdf suppmat project DOI [BibTex]

pdf suppmat project DOI [BibTex]


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A Random Riemannian Metric for Probabilistic Shortest-Path Tractography

Hauberg, S., Schober, M., Liptrot, M., Hennig, P., Feragen, A.

In 18th International Conference on Medical Image Computing and Computer Assisted Intervention, 9349, pages: 597-604, Lecture Notes in Computer Science, MICCAI, 2015 (inproceedings)

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PDF DOI [BibTex]

PDF DOI [BibTex]


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Recent Methodological Advances in Causal Discovery and Inference

Spirtes, P., Zhang, K.

In 15th Conference on Theoretical Aspects of Rationality and Knowledge, pages: 23-35, (Editors: Ramanujam, R.), TARK, 2015 (inproceedings)

ei

[BibTex]

[BibTex]


Thumb xl screen shot 2015 08 22 at 22.13.35
Policy Learning with Hypothesis Based Local Action Selection

Sankaran, B., Bohg, J., Ratliff, N., Schaal, S.

In Reinforcement Learning and Decision Making, 2015 (inproceedings)

Abstract
For robots to be able to manipulate in unknown and unstructured environments the robot should be capable of operating under partial observability of the environment. Object occlusions and unmodeled environments are some of the factors that result in partial observability. A common scenario where this is encountered is manipulation in clutter. In the case that the robot needs to locate an object of interest and manipulate it, it needs to perform a series of decluttering actions to accurately detect the object of interest. To perform such a series of actions, the robot also needs to account for the dynamics of objects in the environment and how they react to contact. This is a non trivial problem since one needs to reason not only about robot-object interactions but also object-object interactions in the presence of contact. In the example scenario of manipulation in clutter, the state vector would have to account for the pose of the object of interest and the structure of the surrounding environment. The process model would have to account for all the aforementioned robot-object, object-object interactions. The complexity of the process model grows exponentially as the number of objects in the scene increases. This is commonly the case in unstructured environments. Hence it is not reasonable to attempt to model all object-object and robot-object interactions explicitly. Under this setting we propose a hypothesis based action selection algorithm where we construct a hypothesis set of the possible poses of an object of interest given the current evidence in the scene and select actions based on our current set of hypothesis. This hypothesis set tends to represent the belief about the structure of the environment and the number of poses the object of interest can take. The agent's only stopping criterion is when the uncertainty regarding the pose of the object is fully resolved.

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Web Project Page [BibTex]


Thumb xl isa
Joint 3D Estimation of Vehicles and Scene Flow

Menze, M., Heipke, C., Geiger, A.

In Proc. of the ISPRS Workshop on Image Sequence Analysis (ISA), 2015 (inproceedings)

Abstract
Three-dimensional reconstruction of dynamic scenes is an important prerequisite for applications like mobile robotics or autonomous driving. While much progress has been made in recent years, imaging conditions in natural outdoor environments are still very challenging for current reconstruction and recognition methods. In this paper, we propose a novel unified approach which reasons jointly about 3D scene flow as well as the pose, shape and motion of vehicles in the scene. Towards this goal, we incorporate a deformable CAD model into a slanted-plane conditional random field for scene flow estimation and enforce shape consistency between the rendered 3D models and the parameters of all superpixels in the image. The association of superpixels to objects is established by an index variable which implicitly enables model selection. We evaluate our approach on the challenging KITTI scene flow dataset in terms of object and scene flow estimation. Our results provide a prove of concept and demonstrate the usefulness of our method.

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PDF [BibTex]

PDF [BibTex]


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Learning Optimal Striking Points for A Ping-Pong Playing Robot

Huang, Y., Schölkopf, B., Peters, J.

In IEEE/RSJ International Conference on Intelligent Robots and Systems, pages: 4587-4592, IROS, 2015 (inproceedings)

am ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Model-Based Relative Entropy Stochastic Search

Abdolmaleki, A., Peters, J., Neumann, G.

In Advances in Neural Information Processing Systems 28, pages: 3523-3531, (Editors: C. Cortes, N.D. Lawrence, D.D. Lee, M. Sugiyama and R. Garnett), Curran Associates, Inc., 29th Annual Conference on Neural Information Processing Systems (NIPS), 2015 (inproceedings)

am ei

link (url) [BibTex]

link (url) [BibTex]


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Modeling Spatio-Temporal Variability in Human-Robot Interaction with Probabilistic Movement Primitives

Ewerton, M., Neumann, G., Lioutikov, R., Ben Amor, H., Peters, J., Maeda, G.

In Workshop on Machine Learning for Social Robotics, ICRA, 2015 (inproceedings)

am ei

link (url) [BibTex]

link (url) [BibTex]


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Extracting Low-Dimensional Control Variables for Movement Primitives

Rueckert, E., Mundo, J., Paraschos, A., Peters, J., Neumann, G.

In IEEE International Conference on Robotics and Automation, pages: 1511-1518, ICRA, 2015 (inproceedings)

am ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Self-calibration of optical lenses

Hirsch, M., Schölkopf, B.

In IEEE International Conference on Computer Vision (ICCV 2015), pages: 612-620, IEEE, 2015 (inproceedings)

ei

DOI [BibTex]

DOI [BibTex]


Thumb xl subimage
Smooth Loops from Unconstrained Video

Sevilla-Lara, L., Wulff, J., Sunkavalli, K., Shechtman, E.

In Computer Graphics Forum (Proceedings of EGSR), 34(4):99-107, Eurographics Symposium on Rendering, 2015 (inproceedings)

Abstract
Converting unconstrained video sequences into videos that loop seamlessly is an extremely challenging problem. In this work, we take the first steps towards automating this process by focusing on an important subclass of videos containing a single dominant foreground object. Our technique makes two novel contributions over previous work: first, we propose a correspondence-based similarity metric to automatically identify a good transition point in the video where the appearance and dynamics of the foreground are most consistent. Second, we develop a technique that aligns both the foreground and background about this transition point using a combination of global camera path planning and patch-based video morphing. We demonstrate that this allows us to create natural, compelling, loopy videos from a wide range of videos collected from the internet.

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pdf link (url) DOI Project Page [BibTex]

pdf link (url) DOI Project Page [BibTex]


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Telling cause from effect in deterministic linear dynamical systems

Shajarisales, N., Janzing, D., Schölkopf, B., Besserve, M.

In Proceedings of the 32nd International Conference on Machine Learning, 37, pages: 285–294, JMLR Workshop and Conference Proceedings, (Editors: F. Bach and D. Blei), JMLR, ICML, 2015 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


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A New Perspective and Extension of the Gaussian Filter

Wüthrich, M., Trimpe, S., Kappler, D., Schaal, S.

In Robotics: Science and Systems, 2015 (inproceedings)

Abstract
The Gaussian Filter (GF) is one of the most widely used filtering algorithms; instances are the Extended Kalman Filter, the Unscented Kalman Filter and the Divided Difference Filter. GFs represent the belief of the current state by a Gaussian with the mean being an affine function of the measurement. We show that this representation can be too restrictive to accurately capture the dependencies in systems with nonlinear observation models, and we investigate how the GF can be generalized to alleviate this problem. To this end we view the GF from a variational-inference perspective, and analyze how restrictions on the form of the belief can be relaxed while maintaining simplicity and efficiency. This analysis provides a basis for generalizations of the GF. We propose one such generalization which coincides with a GF using a virtual measurement, obtained by applying a nonlinear function to the actual measurement. Numerical experiments show that the proposed Feature Gaussian Filter (FGF) can have a substantial performance advantage over the standard GF for systems with nonlinear observation models.

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Web PDF Project Page [BibTex]


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A Cognitive Brain-Computer Interface for Patients with Amyotrophic Lateral Sclerosis

Hohmann, M. R., Fomina, T., Jayaram, V., Widmann, N., Förster, C., Müller vom Hagen, J., Synofzik, M., Schölkopf, B., Schöls, L., Grosse-Wentrup, M.

In Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics, pages: 3187-3191, SMC, 2015 (inproceedings)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Efficient Learning of Linear Separators under Bounded Noise

Awasthi, P., Balcan, M., Haghtalab, N., Urner, R.

In Proceedings of the 28th Conference on Learning Theory, 40, pages: 167-190, (Editors: Grünwald, P. and Hazan, E. and Kale, S.), JMLR, COLT, 2015 (inproceedings)

ei

link (url) [BibTex]

link (url) [BibTex]


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Learning multiple collaborative tasks with a mixture of Interaction Primitives

Ewerton, M., Neumann, G., Lioutikov, R., Ben Amor, H., Peters, J., Maeda, G.

In IEEE International Conference on Robotics and Automation, pages: 1535-1542, ICRA, 2015 (inproceedings)

am ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Whole-body motor strategies for balancing on a beam when changing the number of available degrees of freedom

Chiovetto, E, Huber, M, Righetti, L., Schaal, S., Sternad, D, Giese, M.

In Progress in Motor Control X, Budapest, Hungry, 2015 (inproceedings)

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[BibTex]

[BibTex]


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Combined FORC and x-ray microscopy study of magnetisation reversal in antidot lattices

Gräfe, J., Haering, F., Stahl, C., Weigand, M., Skripnik, M., Nowak, U., Ziemann, P., Wiedwald, U., Schütz, G., Goering, E.

In IEEE International Magnetics Conference (INTERMAG 2015), IEEE, Beijing, China, 2015 (inproceedings)

mms

DOI Project Page Project Page [BibTex]

DOI Project Page Project Page [BibTex]


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Local control of domain wall dynamics in ferromagnetic rings

Richter, K., Mawass, M., Krone, A., Krüger, B., Weigand, M., Stoll, H., Schütz, G., Kläui, M.

In IEEE International Magnetics Conference (INTERMAG 2015), IEEE, Beijing, China, 2015 (inproceedings)

mms

DOI [BibTex]

DOI [BibTex]


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Developing neural networks with neurons competing for survival

Peng, Z, Braun, DA

pages: 152-153, IEEE, Piscataway, NJ, USA, 5th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (IEEE ICDL-EPIROB), August 2015 (conference)

Abstract
We study developmental growth in a feedforward neural network model inspired by the survival principle in nature. Each neuron has to select its incoming connections in a way that allow it to fire, as neurons that are not able to fire over a period of time degenerate and die. In order to survive, neurons have to find reoccurring patterns in the activity of the neurons in the preceding layer, because each neuron requires more than one active input at any one time to have enough activation for firing. The sensory input at the lowest layer therefore provides the maximum amount of activation that all neurons compete for. The whole network grows dynamically over time depending on how many patterns can be found and how many neurons can maintain themselves accordingly. We show in simulations that this naturally leads to abstractions in higher layers that emerge in a unsupervised fashion. When evaluating the network in a supervised learning paradigm, it is clear that our network is not competitive. What is interesting though is that this performance was achieved by neurons that simply struggle for survival and do not know about performance error. In contrast to most studies on neural evolution that rely on a network-wide fitness function, our goal was to show that learning behaviour can appear in a system without being driven by any specific utility function or reward signal.

ei

DOI [BibTex]

DOI [BibTex]


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From Humans to Robots and Back: Role of Arm Movement in Medio-lateral Balance Control

Huber, M, Chiovetto, E, Schaal, S., Giese, M., Sternad, D

In Annual Meeting of Neural Control of Movement, Charleston, NC, 2015 (inproceedings)

am

[BibTex]

[BibTex]


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Ultrafast demagnetization after laser pulse irradiation in Ni: Ab-initio electron-phonon scattering and phase space calculations

Illg, C., Haag, M., Fähnle, M.

In Ultrafast Magnetism I. Proceedings of the International Conference UMC 2013, 159, pages: 131-133, Springer Proceedings in Physics, Springer, Strasbourg, 2015 (inproceedings)

mms

DOI [BibTex]

DOI [BibTex]


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Trajectory generation for multi-contact momentum control

Herzog, A., Rotella, N., Schaal, S., Righetti, L.

In 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), pages: 874-880, IEEE, Seoul, South Korea, 2015 (inproceedings)

Abstract
Simplified models of the dynamics such as the linear inverted pendulum model (LIPM) have proven to perform well for biped walking on flat ground. However, for more complex tasks the assumptions of these models can become limiting. For example, the LIPM does not allow for the control of contact forces independently, is limited to co-planar contacts and assumes that the angular momentum is zero. In this paper, we propose to use the full momentum equations of a humanoid robot in a trajectory optimization framework to plan its center of mass, linear and angular momentum trajectories. The model also allows for planning desired contact forces for each end-effector in arbitrary contact locations. We extend our previous results on linear quadratic regulator (LQR) design for momentum control by computing the (linearized) optimal momentum feedback law in a receding horizon fashion. The resulting desired momentum and the associated feedback law are then used in a hierarchical whole body control approach. Simulation experiments show that the approach is computationally fast and is able to generate plans for locomotion on complex terrains while demonstrating good tracking performance for the full humanoid control.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Automotive domain wall propagation in ferromagnetic rings

Richter, K., Mawass, M., Krone, A., Krüger, B., Weigand, M., Schütz, G., Stoll, H., Kläui, M.

In IEEE International Magnetics Conference (INTERMAG 2015), IEEE, Beijing, China, 2015 (inproceedings)

mms

DOI [BibTex]

DOI [BibTex]


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Humanoid Momentum Estimation Using Sensed Contact Wrenches

Rotella, N., Herzog, A., Schaal, S., Righetti, L.

In 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), pages: 556-563, IEEE, Seoul, South Korea, 2015 (inproceedings)

Abstract
This work presents approaches for the estimation of quantities important for the control of the momentum of a humanoid robot. In contrast to previous approaches which use simplified models such as the Linear Inverted Pendulum Model, we present estimators based on the momentum dynamics of the robot. By using this simple yet dynamically-consistent model, we avoid the issues of using simplified models for estimation. We develop an estimator for the center of mass and full momentum which can be reformulated to estimate center of mass offsets as well as external wrenches applied to the robot. The observability of these estimators is investigated and their performance is evaluated in comparison to previous approaches.

am mg

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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The third dimension: Vortex core reversal by interaction with \textquotesingleflexure modes’

Noske, M., Stoll, H., Fähnle, M., Weigand, M., Dieterle, G., Förster, J., Gangwar, A., Slavin, A., Back, C. H., Schütz, G.

In IEEE International Magnetics Conference (INTERMAG 2015), IEEE, Beijing, China, 2015 (inproceedings)

mms

DOI [BibTex]

DOI [BibTex]


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Skyrmions at room temperature in magnetic multilayers

Moreau-Luchaire, C., Reyren, N., Moutafis, C., Sampaio, J., Van Horne, N., Vaz, C. A., Warnicke, P., Garcia, K., Weigand, M., Bouzehouane, K., Deranlot, C., George, J., Raabe, J., Cros, V., Fert, A.

In IEEE International Magnetics Conference (INTERMAG 2015), IEEE, Beijing, China, 2015 (inproceedings)

mms

DOI [BibTex]

DOI [BibTex]

2014


Thumb xl iros15 front
Learning Robot Tactile Sensing for Object Manipulation

Chebotar, Y., Kroemer, O., Peters, J.

In International Conference on Intelligent Robots and Systems (IROS), IEEE/RSJ International Conference on Intelligent Robots and System, October 2014 (inproceedings)

pdf [BibTex]

2014


pdf [BibTex]


Thumb xl thumb grouped teaser
Hough-based Object Detection with Grouped Features

Srikantha, A., Gall, J.

International Conference on Image Processing, pages: 1653-1657, Paris, France, IEEE International Conference on Image Processing , October 2014 (conference)

Abstract
Hough-based voting approaches have been successfully applied to object detection. While these methods can be efficiently implemented by random forests, they estimate the probability for an object hypothesis for each feature independently. In this work, we address this problem by grouping features in a local neighborhood to obtain a better estimate of the probability. To this end, we propose oblique classification-regression forests that combine features of different trees. We further investigate the benefit of combining independent and grouped features and evaluate the approach on RGB and RGB-D datasets.

ps

pdf poster DOI Project Page [BibTex]

pdf poster DOI Project Page [BibTex]


Thumb xl thumb schoenbein2014iros
Omnidirectional 3D Reconstruction in Augmented Manhattan Worlds

Schoenbein, M., Geiger, A.

International Conference on Intelligent Robots and Systems, pages: 716 - 723, IEEE, Chicago, IL, USA, IEEE/RSJ International Conference on Intelligent Robots and System, October 2014 (conference)

Abstract
This paper proposes a method for high-quality omnidirectional 3D reconstruction of augmented Manhattan worlds from catadioptric stereo video sequences. In contrast to existing works we do not rely on constructing virtual perspective views, but instead propose to optimize depth jointly in a unified omnidirectional space. Furthermore, we show that plane-based prior models can be applied even though planes in 3D do not project to planes in the omnidirectional domain. Towards this goal, we propose an omnidirectional slanted-plane Markov random field model which relies on plane hypotheses extracted using a novel voting scheme for 3D planes in omnidirectional space. To quantitatively evaluate our method we introduce a dataset which we have captured using our autonomous driving platform AnnieWAY which we equipped with two horizontally aligned catadioptric cameras and a Velodyne HDL-64E laser scanner for precise ground truth depth measurements. As evidenced by our experiments, the proposed method clearly benefits from the unified view and significantly outperforms existing stereo matching techniques both quantitatively and qualitatively. Furthermore, our method is able to reduce noise and the obtained depth maps can be represented very compactly by a small number of image segments and plane parameters.

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pdf DOI [BibTex]

pdf DOI [BibTex]


Thumb xl publications toc
Geckogripper: A soft, inflatable robotic gripper using gecko-inspired elastomer micro-fiber adhesives

Song, S., Majidi, C., Sitti, M.

In Intelligent Robots and Systems (IROS 2014), 2014 IEEE/RSJ International Conference on, pages: 4624-4629, September 2014 (inproceedings)

Abstract
This paper proposes GeckoGripper, a novel soft, inflatable gripper based on the controllable adhesion mechanism of gecko-inspired micro-fiber adhesives, to pick-and-place complex and fragile non-planar or planar parts serially or in parallel. Unlike previous fibrillar structures that use peel angle to control the manipulation of parts, we developed an elastomer micro-fiber adhesive that is fabricated on a soft, flexible membrane, increasing the adaptability to non-planar three-dimensional (3D) geometries and controllability in adhesion. The adhesive switching ratio (the ratio between the maximum and minimum adhesive forces) of the developed gripper was measured to be around 204, which is superior to previous works based on peel angle-based release control methods. Adhesion control mechanism based on the stretch of the membrane and superior adaptability to non-planar 3D geometries enable the micro-fibers to pick-and-place various 3D parts as shown in demonstrations.

pi

DOI [BibTex]

DOI [BibTex]


Thumb xl fop
Human Pose Estimation with Fields of Parts

Kiefel, M., Gehler, P.

In Computer Vision – ECCV 2014, LNCS 8693, pages: 331-346, Lecture Notes in Computer Science, (Editors: Fleet, David and Pajdla, Tomas and Schiele, Bernt and Tuytelaars, Tinne), Springer, 13th European Conference on Computer Vision, September 2014 (inproceedings)

Abstract
This paper proposes a new formulation of the human pose estimation problem. We present the Fields of Parts model, a binary Conditional Random Field model designed to detect human body parts of articulated people in single images. The Fields of Parts model is inspired by the idea of Pictorial Structures, it models local appearance and joint spatial configuration of the human body. However the underlying graph structure is entirely different. The idea is simple: we model the presence and absence of a body part at every possible position, orientation, and scale in an image with a binary random variable. This results into a vast number of random variables, however, we show that approximate inference in this model is efficient. Moreover we can encode the very same appearance and spatial structure as in Pictorial Structures models. This approach allows us to combine ideas from segmentation and pose estimation into a single model. The Fields of Parts model can use evidence from the background, include local color information, and it is connected more densely than a kinematic chain structure. On the challenging Leeds Sports Poses dataset we improve over the Pictorial Structures counterpart by 5.5% in terms of Average Precision of Keypoints (APK).

ei ps

website pdf DOI Project Page [BibTex]

website pdf DOI Project Page [BibTex]


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Capturing Hand Motion with an RGB-D Sensor, Fusing a Generative Model with Salient Points

Tzionas, D., Srikantha, A., Aponte, P., Gall, J.

In German Conference on Pattern Recognition (GCPR), pages: 1-13, Lecture Notes in Computer Science, Springer, GCPR, September 2014 (inproceedings)

Abstract
Hand motion capture has been an active research topic in recent years, following the success of full-body pose tracking. Despite similarities, hand tracking proves to be more challenging, characterized by a higher dimensionality, severe occlusions and self-similarity between fingers. For this reason, most approaches rely on strong assumptions, like hands in isolation or expensive multi-camera systems, that limit the practical use. In this work, we propose a framework for hand tracking that can capture the motion of two interacting hands using only a single, inexpensive RGB-D camera. Our approach combines a generative model with collision detection and discriminatively learned salient points. We quantitatively evaluate our approach on 14 new sequences with challenging interactions.

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pdf Supplementary pdf Supplementary Material Project Page DOI Project Page [BibTex]

pdf Supplementary pdf Supplementary Material Project Page DOI Project Page [BibTex]


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OpenDR: An Approximate Differentiable Renderer

Loper, M. M., Black, M. J.

In Computer Vision – ECCV 2014, 8695, pages: 154-169, Lecture Notes in Computer Science, (Editors: D. Fleet and T. Pajdla and B. Schiele and T. Tuytelaars ), Springer International Publishing, 13th European Conference on Computer Vision, September 2014 (inproceedings)

Abstract
Inverse graphics attempts to take sensor data and infer 3D geometry, illumination, materials, and motions such that a graphics renderer could realistically reproduce the observed scene. Renderers, however, are designed to solve the forward process of image synthesis. To go in the other direction, we propose an approximate di fferentiable renderer (DR) that explicitly models the relationship between changes in model parameters and image observations. We describe a publicly available OpenDR framework that makes it easy to express a forward graphics model and then automatically obtain derivatives with respect to the model parameters and to optimize over them. Built on a new autodiff erentiation package and OpenGL, OpenDR provides a local optimization method that can be incorporated into probabilistic programming frameworks. We demonstrate the power and simplicity of programming with OpenDR by using it to solve the problem of estimating human body shape from Kinect depth and RGB data.

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pdf Code Chumpy Supplementary video of talk DOI Project Page [BibTex]

pdf Code Chumpy Supplementary video of talk DOI Project Page [BibTex]


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Discovering Object Classes from Activities

Srikantha, A., Gall, J.

In European Conference on Computer Vision, 8694, pages: 415-430, Lecture Notes in Computer Science, (Editors: D. Fleet and T. Pajdla and B. Schiele and T. Tuytelaars ), Springer International Publishing, 13th European Conference on Computer Vision, September 2014 (inproceedings)

Abstract
In order to avoid an expensive manual labeling process or to learn object classes autonomously without human intervention, object discovery techniques have been proposed that extract visual similar objects from weakly labelled videos. However, the problem of discovering small or medium sized objects is largely unexplored. We observe that videos with activities involving human-object interactions can serve as weakly labelled data for such cases. Since neither object appearance nor motion is distinct enough to discover objects in these videos, we propose a framework that samples from a space of algorithms and their parameters to extract sequences of object proposals. Furthermore, we model similarity of objects based on appearance and functionality, which is derived from human and object motion. We show that functionality is an important cue for discovering objects from activities and demonstrate the generality of the model on three challenging RGB-D and RGB datasets.

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pdf anno poster DOI Project Page [BibTex]

pdf anno poster DOI Project Page [BibTex]


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Probabilistic Progress Bars

Kiefel, M., Schuler, C., Hennig, P.

In Conference on Pattern Recognition (GCPR), 8753, pages: 331-341, Lecture Notes in Computer Science, (Editors: Jiang, X., Hornegger, J., and Koch, R.), Springer, GCPR, September 2014 (inproceedings)

Abstract
Predicting the time at which the integral over a stochastic process reaches a target level is a value of interest in many applications. Often, such computations have to be made at low cost, in real time. As an intuitive example that captures many features of this problem class, we choose progress bars, a ubiquitous element of computer user interfaces. These predictors are usually based on simple point estimators, with no error modelling. This leads to fluctuating behaviour confusing to the user. It also does not provide a distribution prediction (risk values), which are crucial for many other application areas. We construct and empirically evaluate a fast, constant cost algorithm using a Gauss-Markov process model which provides more information to the user.

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website+code pdf DOI [BibTex]

website+code pdf DOI [BibTex]