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2018


Deep Inertial Poser: Learning to Reconstruct Human Pose from Sparse Inertial Measurements in Real Time
Deep Inertial Poser: Learning to Reconstruct Human Pose from Sparse Inertial Measurements in Real Time

Huang, Y., Kaufmann, M., Aksan, E., Black, M. J., Hilliges, O., Pons-Moll, G.

ACM Transactions on Graphics, (Proc. SIGGRAPH Asia), 37, pages: 185:1-185:15, ACM, November 2018, Two first authors contributed equally (article)

Abstract
We demonstrate a novel deep neural network capable of reconstructing human full body pose in real-time from 6 Inertial Measurement Units (IMUs) worn on the user's body. In doing so, we address several difficult challenges. First, the problem is severely under-constrained as multiple pose parameters produce the same IMU orientations. Second, capturing IMU data in conjunction with ground-truth poses is expensive and difficult to do in many target application scenarios (e.g., outdoors). Third, modeling temporal dependencies through non-linear optimization has proven effective in prior work but makes real-time prediction infeasible. To address this important limitation, we learn the temporal pose priors using deep learning. To learn from sufficient data, we synthesize IMU data from motion capture datasets. A bi-directional RNN architecture leverages past and future information that is available at training time. At test time, we deploy the network in a sliding window fashion, retaining real time capabilities. To evaluate our method, we recorded DIP-IMU, a dataset consisting of 10 subjects wearing 17 IMUs for validation in 64 sequences with 330,000 time instants; this constitutes the largest IMU dataset publicly available. We quantitatively evaluate our approach on multiple datasets and show results from a real-time implementation. DIP-IMU and the code are available for research purposes.

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data code pdf preprint errata video DOI Project Page [BibTex]

2018


data code pdf preprint errata video DOI Project Page [BibTex]


A Value-Driven Eldercare Robot: Virtual and Physical Instantiations of a Case-Supported Principle-Based Behavior Paradigm
A Value-Driven Eldercare Robot: Virtual and Physical Instantiations of a Case-Supported Principle-Based Behavior Paradigm

Anderson, M., Anderson, S., Berenz, V.

Proceedings of the IEEE, pages: 1,15, October 2018 (article)

Abstract
In this paper, a case-supported principle-based behavior paradigm is proposed to help ensure ethical behavior of autonomous machines. We argue that ethically significant behavior of autonomous systems should be guided by explicit ethical principles determined through a consensus of ethicists. Such a consensus is likely to emerge in many areas in which autonomous systems are apt to be deployed and for the actions they are liable to undertake. We believe that this is the case since we are more likely to agree on how machines ought to treat us than on how human beings ought to treat one another. Given such a consensus, particular cases of ethical dilemmas where ethicists agree on the ethically relevant features and the right course of action can be used to help discover principles that balance these features when they are in conflict. Such principles not only help ensure ethical behavior of complex and dynamic systems but also can serve as a basis for justification of this behavior. The requirements, methods, implementation, and evaluation components of the paradigm are detailed as well as its instantiation in both a simulated and real robot functioning in the domain of eldercare.

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link (url) DOI [BibTex]


Deep Neural Network-based Cooperative Visual Tracking through Multiple Micro Aerial Vehicles
Deep Neural Network-based Cooperative Visual Tracking through Multiple Micro Aerial Vehicles

Price, E., Lawless, G., Ludwig, R., Martinovic, I., Buelthoff, H. H., Black, M. J., Ahmad, A.

IEEE Robotics and Automation Letters, Robotics and Automation Letters, 3(4):3193-3200, IEEE, October 2018, Also accepted and presented in the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). (article)

Abstract
Multi-camera tracking of humans and animals in outdoor environments is a relevant and challenging problem. Our approach to it involves a team of cooperating micro aerial vehicles (MAVs) with on-board cameras only. DNNs often fail at objects with small scale or far away from the camera, which are typical characteristics of a scenario with aerial robots. Thus, the core problem addressed in this paper is how to achieve on-board, online, continuous and accurate vision-based detections using DNNs for visual person tracking through MAVs. Our solution leverages cooperation among multiple MAVs and active selection of most informative regions of image. We demonstrate the efficiency of our approach through simulations with up to 16 robots and real robot experiments involving two aerial robots tracking a person, while maintaining an active perception-driven formation. ROS-based source code is provided for the benefit of the community.

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Published Version link (url) DOI [BibTex]

Published Version link (url) DOI [BibTex]


First Impressions of Personality Traits From Body Shapes
First Impressions of Personality Traits From Body Shapes

Hu, Y., Parde, C. J., Hill, M. Q., Mahmood, N., O’Toole, A. J.

Psychological Science, 29(12):1969-–1983, October 2018 (article)

Abstract
People infer the personalities of others from their facial appearance. Whether they do so from body shapes is less studied. We explored personality inferences made from body shapes. Participants rated personality traits for male and female bodies generated with a three-dimensional body model. Multivariate spaces created from these ratings indicated that people evaluate bodies on valence and agency in ways that directly contrast positive and negative traits from the Big Five domains. Body-trait stereotypes based on the trait ratings revealed a myriad of diverse body shapes that typify individual traits. Personality-trait profiles were predicted reliably from a subset of the body-shape features used to specify the three-dimensional bodies. Body features related to extraversion and conscientiousness were predicted with the highest consensus, followed by openness traits. This study provides the first comprehensive look at the range, diversity, and reliability of personality inferences that people make from body shapes.

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publisher site pdf DOI [BibTex]

publisher site pdf DOI [BibTex]


Playful: Reactive Programming for Orchestrating Robotic Behavior
Playful: Reactive Programming for Orchestrating Robotic Behavior

Berenz, V., Schaal, S.

IEEE Robotics Automation Magazine, 25(3):49-60, September 2018 (article) In press

Abstract
For many service robots, reactivity to changes in their surroundings is a must. However, developing software suitable for dynamic environments is difficult. Existing robotic middleware allows engineers to design behavior graphs by organizing communication between components. But because these graphs are structurally inflexible, they hardly support the development of complex reactive behavior. To address this limitation, we propose Playful, a software platform that applies reactive programming to the specification of robotic behavior.

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playful website playful_IEEE_RAM link (url) DOI [BibTex]


ClusterNet: Instance Segmentation in RGB-D Images
ClusterNet: Instance Segmentation in RGB-D Images

Shao, L., Tian, Y., Bohg, J.

arXiv, September 2018, Submitted to ICRA'19 (article) Submitted

Abstract
We propose a method for instance-level segmentation that uses RGB-D data as input and provides detailed information about the location, geometry and number of {\em individual\/} objects in the scene. This level of understanding is fundamental for autonomous robots. It enables safe and robust decision-making under the large uncertainty of the real-world. In our model, we propose to use the first and second order moments of the object occupancy function to represent an object instance. We train an hourglass Deep Neural Network (DNN) where each pixel in the output votes for the 3D position of the corresponding object center and for the object's size and pose. The final instance segmentation is achieved through clustering in the space of moments. The object-centric training loss is defined on the output of the clustering. Our method outperforms the state-of-the-art instance segmentation method on our synthesized dataset. We show that our method generalizes well on real-world data achieving visually better segmentation results.

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link (url) [BibTex]

link (url) [BibTex]


Visual Perception and Evaluation of Photo-Realistic Self-Avatars From {3D} Body Scans in Males and Females
Visual Perception and Evaluation of Photo-Realistic Self-Avatars From 3D Body Scans in Males and Females

Thaler, A., Piryankova, I., Stefanucci, J. K., Pujades, S., de la Rosa, S., Streuber, S., Romero, J., Black, M. J., Mohler, B. J.

Frontiers in ICT, 5, pages: 1-14, September 2018 (article)

Abstract
The creation or streaming of photo-realistic self-avatars is important for virtual reality applications that aim for perception and action to replicate real world experience. The appearance and recognition of a digital self-avatar may be especially important for applications related to telepresence, embodied virtual reality, or immersive games. We investigated gender differences in the use of visual cues (shape, texture) of a self-avatar for estimating body weight and evaluating avatar appearance. A full-body scanner was used to capture each participant's body geometry and color information and a set of 3D virtual avatars with realistic weight variations was created based on a statistical body model. Additionally, a second set of avatars was created with an average underlying body shape matched to each participant’s height and weight. In four sets of psychophysical experiments, the influence of visual cues on the accuracy of body weight estimation and the sensitivity to weight changes was assessed by manipulating body shape (own, average) and texture (own photo-realistic, checkerboard). The avatars were presented on a large-screen display, and participants responded to whether the avatar's weight corresponded to their own weight. Participants also adjusted the avatar's weight to their desired weight and evaluated the avatar's appearance with regard to similarity to their own body, uncanniness, and their willingness to accept it as a digital representation of the self. The results of the psychophysical experiments revealed no gender difference in the accuracy of estimating body weight in avatars. However, males accepted a larger weight range of the avatars as corresponding to their own. In terms of the ideal body weight, females but not males desired a thinner body. With regard to the evaluation of avatar appearance, the questionnaire responses suggest that own photo-realistic texture was more important to males for higher similarity ratings, while own body shape seemed to be more important to females. These results argue for gender-specific considerations when creating self-avatars.

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pdf DOI [BibTex]

pdf DOI [BibTex]


Robust Physics-based Motion Retargeting with Realistic Body Shapes
Robust Physics-based Motion Retargeting with Realistic Body Shapes

Borno, M. A., Righetti, L., Black, M. J., Delp, S. L., Fiume, E., Romero, J.

Computer Graphics Forum, 37, pages: 6:1-12, July 2018 (article)

Abstract
Motion capture is often retargeted to new, and sometimes drastically different, characters. When the characters take on realistic human shapes, however, we become more sensitive to the motion looking right. This means adapting it to be consistent with the physical constraints imposed by different body shapes. We show how to take realistic 3D human shapes, approximate them using a simplified representation, and animate them so that they move realistically using physically-based retargeting. We develop a novel spacetime optimization approach that learns and robustly adapts physical controllers to new bodies and constraints. The approach automatically adapts the motion of the mocap subject to the body shape of a target subject. This motion respects the physical properties of the new body and every body shape results in a different and appropriate movement. This makes it easy to create a varied set of motions from a single mocap sequence by simply varying the characters. In an interactive environment, successful retargeting requires adapting the motion to unexpected external forces. We achieve robustness to such forces using a novel LQR-tree formulation. We show that the simulated motions look appropriate to each character’s anatomy and their actions are robust to perturbations.

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pdf video Project Page Project Page [BibTex]

pdf video Project Page Project Page [BibTex]


Real-time Perception meets Reactive Motion Generation
Real-time Perception meets Reactive Motion Generation

(Best Systems Paper Finalists - Amazon Robotics Best Paper Awards in Manipulation)

Kappler, D., Meier, F., Issac, J., Mainprice, J., Garcia Cifuentes, C., Wüthrich, M., Berenz, V., Schaal, S., Ratliff, N., Bohg, J.

IEEE Robotics and Automation Letters, 3(3):1864-1871, July 2018 (article)

Abstract
We address the challenging problem of robotic grasping and manipulation in the presence of uncertainty. This uncertainty is due to noisy sensing, inaccurate models and hard-to-predict environment dynamics. Our approach emphasizes the importance of continuous, real-time perception and its tight integration with reactive motion generation methods. We present a fully integrated system where real-time object and robot tracking as well as ambient world modeling provides the necessary input to feedback controllers and continuous motion optimizers. Specifically, they provide attractive and repulsive potentials based on which the controllers and motion optimizer can online compute movement policies at different time intervals. We extensively evaluate the proposed system on a real robotic platform in four scenarios that exhibit either challenging workspace geometry or a dynamic environment. We compare the proposed integrated system with a more traditional sense-plan-act approach that is still widely used. In 333 experiments, we show the robustness and accuracy of the proposed system.

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arxiv video video link (url) DOI Project Page [BibTex]


Oncilla robot: a versatile open-source quadruped research robot with compliant pantograph legs
Oncilla robot: a versatile open-source quadruped research robot with compliant pantograph legs

Sproewitz, A., Tuleu, A., Ajallooeian, M., Vespignani, M., Moeckel, R., Eckert, P., D’Haene, M., Degrave, J., Nordmann, A., Schrauwen, B., Steil, J., Ijspeert, A. J.

Frontiers in Robotics and AI, 5(67), June 2018, arXiv: 1803.06259 (article)

Abstract
We present Oncilla robot, a novel mobile, quadruped legged locomotion machine. This large-cat sized, 5.1 robot is one of a kind of a recent, bioinspired legged robot class designed with the capability of model-free locomotion control. Animal legged locomotion in rough terrain is clearly shaped by sensor feedback systems. Results with Oncilla robot show that agile and versatile locomotion is possible without sensory signals to some extend, and tracking becomes robust when feedback control is added (Ajaoolleian 2015). By incorporating mechanical and control blueprints inspired from animals, and by observing the resulting robot locomotion characteristics, we aim to understand the contribution of individual components. Legged robots have a wide mechanical and control design parameter space, and a unique potential as research tools to investigate principles of biomechanics and legged locomotion control. But the hardware and controller design can be a steep initial hurdle for academic research. To facilitate the easy start and development of legged robots, Oncilla-robot's blueprints are available through open-source. [...]

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link (url) DOI Project Page [BibTex]

link (url) DOI Project Page [BibTex]


Assessing body image in anorexia nervosa using biometric self-avatars in virtual reality: Attitudinal components rather than visual body size estimation are distorted
Assessing body image in anorexia nervosa using biometric self-avatars in virtual reality: Attitudinal components rather than visual body size estimation are distorted

Mölbert, S. C., Thaler, A., Mohler, B. J., Streuber, S., Romero, J., Black, M. J., Zipfel, S., Karnath, H., Giel, K. E.

Psychological Medicine, 48(4):642-653, March 2018 (article)

Abstract
Background: Body image disturbance (BID) is a core symptom of anorexia nervosa (AN), but as yet distinctive features of BID are unknown. The present study aimed at disentangling perceptual and attitudinal components of BID in AN. Methods: We investigated n=24 women with AN and n=24 controls. Based on a 3D body scan, we created realistic virtual 3D bodies (avatars) for each participant that were varied through a range of ±20% of the participants' weights. Avatars were presented in a virtual reality mirror scenario. Using different psychophysical tasks, participants identified and adjusted their actual and their desired body weight. To test for general perceptual biases in estimating body weight, a second experiment investigated perception of weight and shape matched avatars with another identity. Results: Women with AN and controls underestimated their weight, with a trend that women with AN underestimated more. The average desired body of controls had normal weight while the average desired weight of women with AN corresponded to extreme AN (DSM-5). Correlation analyses revealed that desired body weight, but not accuracy of weight estimation, was associated with eating disorder symptoms. In the second experiment, both groups estimated accurately while the most attractive body was similar to Experiment 1. Conclusions: Our results contradict the widespread assumption that patients with AN overestimate their body weight due to visual distortions. Rather, they illustrate that BID might be driven by distorted attitudes with regard to the desired body. Clinical interventions should aim at helping patients with AN to change their desired weight.

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doi pdf DOI Project Page [BibTex]


Body size estimation of self and others in females varying in {BMI}
Body size estimation of self and others in females varying in BMI

Thaler, A., Geuss, M. N., Mölbert, S. C., Giel, K. E., Streuber, S., Romero, J., Black, M. J., Mohler, B. J.

PLoS ONE, 13(2), Febuary 2018 (article)

Abstract
Previous literature suggests that a disturbed ability to accurately identify own body size may contribute to overweight. Here, we investigated the influence of personal body size, indexed by body mass index (BMI), on body size estimation in a non-clinical population of females varying in BMI. We attempted to disentangle general biases in body size estimates and attitudinal influences by manipulating whether participants believed the body stimuli (personalized avatars with realistic weight variations) represented their own body or that of another person. Our results show that the accuracy of own body size estimation is predicted by personal BMI, such that participants with lower BMI underestimated their body size and participants with higher BMI overestimated their body size. Further, participants with higher BMI were less likely to notice the same percentage of weight gain than participants with lower BMI. Importantly, these results were only apparent when participants were judging a virtual body that was their own identity (Experiment 1), but not when they estimated the size of a body with another identity and the same underlying body shape (Experiment 2a). The different influences of BMI on accuracy of body size estimation and sensitivity to weight change for self and other identity suggests that effects of BMI on visual body size estimation are self-specific and not generalizable to other bodies.

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pdf DOI Project Page [BibTex]


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Distributed Event-Based State Estimation for Networked Systems: An LMI Approach

Muehlebach, M., Trimpe, S.

IEEE Transactions on Automatic Control, 63(1):269-276, January 2018 (article)

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arXiv (extended version) DOI Project Page [BibTex]

arXiv (extended version) DOI Project Page [BibTex]


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Memristor-enhanced humanoid robot control system–Part I: theory behind the novel memcomputing paradigm

Ascoli, A., Baumann, D., Tetzlaff, R., Chua, L. O., Hild, M.

International Journal of Circuit Theory and Applications, 46(1):155-183, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


Combining learned and analytical models for predicting action effects
Combining learned and analytical models for predicting action effects

Kloss, A., Schaal, S., Bohg, J.

arXiv, 2018 (article) Submitted

Abstract
One of the most basic skills a robot should possess is predicting the effect of physical interactions with objects in the environment. This enables optimal action selection to reach a certain goal state. Traditionally, dynamics are approximated by physics-based analytical models. These models rely on specific state representations that may be hard to obtain from raw sensory data, especially if no knowledge of the object shape is assumed. More recently, we have seen learning approaches that can predict the effect of complex physical interactions directly from sensory input. It is however an open question how far these models generalize beyond their training data. In this work, we investigate the advantages and limitations of neural network based learning approaches for predicting the effects of actions based on sensory input and show how analytical and learned models can be combined to leverage the best of both worlds. As physical interaction task, we use planar pushing, for which there exists a well-known analytical model and a large real-world dataset. We propose to use a convolutional neural network to convert raw depth images or organized point clouds into a suitable representation for the analytical model and compare this approach to using neural networks for both, perception and prediction. A systematic evaluation of the proposed approach on a very large real-world dataset shows two main advantages of the hybrid architecture. Compared to a pure neural network, it significantly (i) reduces required training data and (ii) improves generalization to novel physical interaction.

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arXiv pdf link (url) [BibTex]


Temporal Human Action Segmentation via Dynamic Clustering
Temporal Human Action Segmentation via Dynamic Clustering

Zhang, Y., Sun, H., Tang, S., Neumann, H.

arXiv preprint arXiv:1803.05790, 2018 (article)

Abstract
We present an effective dynamic clustering algorithm for the task of temporal human action segmentation, which has comprehensive applications such as robotics, motion analysis, and patient monitoring. Our proposed algorithm is unsupervised, fast, generic to process various types of features, and applica- ble in both the online and offline settings. We perform extensive experiments of processing data streams, and show that our algorithm achieves the state-of- the-art results for both online and offline settings.

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link (url) [BibTex]

link (url) [BibTex]


Motion Segmentation & Multiple Object Tracking by Correlation Co-Clustering
Motion Segmentation & Multiple Object Tracking by Correlation Co-Clustering

Keuper, M., Tang, S., Andres, B., Brox, T., Schiele, B.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018 (article)

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pdf DOI Project Page [BibTex]

pdf DOI Project Page [BibTex]


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Memristor-enhanced humanoid robot control system–Part II: circuit theoretic model and performance analysis

Baumann, D., Ascoli, A., Tetzlaff, R., Chua, L. O., Hild, M.

International Journal of Circuit Theory and Applications, 46(1):184-220, 2018 (article)

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DOI [BibTex]

DOI [BibTex]

2014


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Wenn es was zu sagen gibt

(Klaus Tschira Award 2014 in Computer Science)

Trimpe, S.

Bild der Wissenschaft, pages: 20-23, November 2014, (popular science article in German) (article)

am ics

PDF Project Page [BibTex]

2014


PDF Project Page [BibTex]


{MoSh}: Motion and Shape Capture from Sparse Markers
MoSh: Motion and Shape Capture from Sparse Markers

Loper, M. M., Mahmood, N., Black, M. J.

ACM Transactions on Graphics, (Proc. SIGGRAPH Asia), 33(6):220:1-220:13, ACM, New York, NY, USA, November 2014 (article)

Abstract
Marker-based motion capture (mocap) is widely criticized as producing lifeless animations. We argue that important information about body surface motion is present in standard marker sets but is lost in extracting a skeleton. We demonstrate a new approach called MoSh (Motion and Shape capture), that automatically extracts this detail from mocap data. MoSh estimates body shape and pose together using sparse marker data by exploiting a parametric model of the human body. In contrast to previous work, MoSh solves for the marker locations relative to the body and estimates accurate body shape directly from the markers without the use of 3D scans; this effectively turns a mocap system into an approximate body scanner. MoSh is able to capture soft tissue motions directly from markers by allowing body shape to vary over time. We evaluate the effect of different marker sets on pose and shape accuracy and propose a new sparse marker set for capturing soft-tissue motion. We illustrate MoSh by recovering body shape, pose, and soft-tissue motion from archival mocap data and using this to produce animations with subtlety and realism. We also show soft-tissue motion retargeting to new characters and show how to magnify the 3D deformations of soft tissue to create animations with appealing exaggerations.

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pdf video data pdf from publisher link (url) DOI Project Page Project Page Project Page [BibTex]

pdf video data pdf from publisher link (url) DOI Project Page Project Page Project Page [BibTex]


Can I recognize my body’s weight? The influence of shape and texture on the perception of self
Can I recognize my body’s weight? The influence of shape and texture on the perception of self

Piryankova, I., Stefanucci, J., Romero, J., de la Rosa, S., Black, M., Mohler, B.

ACM Transactions on Applied Perception for the Symposium on Applied Perception, 11(3):13:1-13:18, September 2014 (article)

Abstract
The goal of this research was to investigate women’s sensitivity to changes in their perceived weight by altering the body mass index (BMI) of the participants’ personalized avatars displayed on a large-screen immersive display. We created the personalized avatars with a full-body 3D scanner that records both the participants’ body geometry and texture. We altered the weight of the personalized avatars to produce changes in BMI while keeping height, arm length and inseam fixed and exploited the correlation between body geometry and anthropometric measurements encapsulated in a statistical body shape model created from thousands of body scans. In a 2x2 psychophysical experiment, we investigated the relative importance of visual cues, namely shape (own shape vs. an average female body shape with equivalent height and BMI to the participant) and texture (own photo-realistic texture or checkerboard pattern texture) on the ability to accurately perceive own current body weight (by asking them ‘Is the avatar the same weight as you?’). Our results indicate that shape (where height and BMI are fixed) had little effect on the perception of body weight. Interestingly, the participants perceived their body weight veridically when they saw their own photo-realistic texture and significantly underestimated their body weight when the avatar had a checkerboard patterned texture. The range that the participants accepted as their own current weight was approximately a 0.83 to −6.05 BMI% change tolerance range around their perceived weight. Both the shape and the texture had an effect on the reported similarity of the body parts and the whole avatar to the participant’s body. This work has implications for new measures for patients with body image disorders, as well as researchers interested in creating personalized avatars for games, training applications or virtual reality.

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pdf DOI Project Page Project Page [BibTex]

pdf DOI Project Page Project Page [BibTex]


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Robotics and Neuroscience

Floreano, Dario, Ijspeert, Auke Jan, Schaal, S.

Current Biology, 24(18):R910-R920, sep 2014 (article)

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[BibTex]

[BibTex]


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3D to 2D bijection for spherical objects under equidistant fisheye projection

Ahmad, A., Xavier, J., Santos-Victor, J., Lima, P.

Computer Vision and Image Understanding, 125, pages: 172-183, August 2014 (article)

Abstract
The core problem addressed in this article is the 3D position detection of a spherical object of known-radius in a single image frame, obtained by a dioptric vision system consisting of only one fisheye lens camera that follows equidistant projection model. The central contribution is a bijection principle between a known-radius spherical object’s 3D world position and its 2D projected image curve, that we prove, thus establishing that for every possible 3D world position of the spherical object, there exists a unique curve on the image plane if the object is projected through a fisheye lens that follows equidistant projection model. Additionally, we present a setup for the experimental verification of the principle’s correctness. In previously published works we have applied this principle to detect and subsequently track a known-radius spherical object.

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DOI [BibTex]

DOI [BibTex]


Breathing Life into Shape: Capturing, Modeling and Animating {3D} Human Breathing
Breathing Life into Shape: Capturing, Modeling and Animating 3D Human Breathing

Tsoli, A., Mahmood, N., Black, M. J.

ACM Transactions on Graphics, (Proc. SIGGRAPH), 33(4):52:1-52:11, ACM, New York, NY, July 2014 (article)

Abstract
Modeling how the human body deforms during breathing is important for the realistic animation of lifelike 3D avatars. We learn a model of body shape deformations due to breathing for different breathing types and provide simple animation controls to render lifelike breathing regardless of body shape. We capture and align high-resolution 3D scans of 58 human subjects. We compute deviations from each subject’s mean shape during breathing, and study the statistics of such shape changes for different genders, body shapes, and breathing types. We use the volume of the registered scans as a proxy for lung volume and learn a novel non-linear model relating volume and breathing type to 3D shape deformations and pose changes. We then augment a SCAPE body model so that body shape is determined by identity, pose, and the parameters of the breathing model. These parameters provide an intuitive interface with which animators can synthesize 3D human avatars with realistic breathing motions. We also develop a novel interface for animating breathing using a spirometer, which measures the changes in breathing volume of a “breath actor.”

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pdf video link (url) DOI Project Page Project Page Project Page [BibTex]


Nonmyopic View Planning for Active Object Classification and Pose Estimation
Nonmyopic View Planning for Active Object Classification and Pose Estimation

Atanasov, N., Sankaran, B., Le Ny, J., Pappas, G., Daniilidis, K.

IEEE Transactions on Robotics, May 2014, clmc (article)

Abstract
One of the central problems in computer vision is the detection of semantically important objects and the estimation of their pose. Most of the work in object detection has been based on single image processing and its performance is limited by occlusions and ambiguity in appearance and geometry. This paper proposes an active approach to object detection by controlling the point of view of a mobile depth camera. When an initial static detection phase identifies an object of interest, several hypotheses are made about its class and orientation. The sensor then plans a sequence of viewpoints, which balances the amount of energy used to move with the chance of identifying the correct hypothesis. We formulate an active M-ary hypothesis testing problem, which includes sensor mobility, and solve it using a point-based approximate POMDP algorithm. The validity of our approach is verified through simulation and real-world experiments with the PR2 robot. The results suggest a significant improvement over static object detection

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Web pdf link (url) [BibTex]

Web pdf link (url) [BibTex]


3D Traffic Scene Understanding from Movable Platforms
3D Traffic Scene Understanding from Movable Platforms

Geiger, A., Lauer, M., Wojek, C., Stiller, C., Urtasun, R.

IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 36(5):1012-1025, published, IEEE, Los Alamitos, CA, May 2014 (article)

Abstract
In this paper, we present a novel probabilistic generative model for multi-object traffic scene understanding from movable platforms which reasons jointly about the 3D scene layout as well as the location and orientation of objects in the scene. In particular, the scene topology, geometry and traffic activities are inferred from short video sequences. Inspired by the impressive driving capabilities of humans, our model does not rely on GPS, lidar or map knowledge. Instead, it takes advantage of a diverse set of visual cues in the form of vehicle tracklets, vanishing points, semantic scene labels, scene flow and occupancy grids. For each of these cues we propose likelihood functions that are integrated into a probabilistic generative model. We learn all model parameters from training data using contrastive divergence. Experiments conducted on videos of 113 representative intersections show that our approach successfully infers the correct layout in a variety of very challenging scenarios. To evaluate the importance of each feature cue, experiments using different feature combinations are conducted. Furthermore, we show how by employing context derived from the proposed method we are able to improve over the state-of-the-art in terms of object detection and object orientation estimation in challenging and cluttered urban environments.

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pdf link (url) [BibTex]

pdf link (url) [BibTex]


Data-Driven Grasp Synthesis - A Survey
Data-Driven Grasp Synthesis - A Survey

Bohg, J., Morales, A., Asfour, T., Kragic, D.

IEEE Transactions on Robotics, 30, pages: 289 - 309, IEEE, April 2014 (article)

Abstract
We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar or unknown objects. This structure allows us to identify common object representations and perceptual processes that facilitate the employed data-driven grasp synthesis technique. In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching to a set of previously encountered objects. Finally for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps. Our survey provides an overview of the different methodologies and discusses open problems in the area of robot grasping. We also draw a parallel to the classical approaches that rely on analytic formulations.

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PDF link (url) DOI Project Page [BibTex]

PDF link (url) DOI Project Page [BibTex]


Adaptive Offset Correction for Intracortical Brain Computer Interfaces
Adaptive Offset Correction for Intracortical Brain Computer Interfaces

Homer, M. L., Perge, J. A., Black, M. J., Harrison, M. T., Cash, S. S., Hochberg, L. R.

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 22(2):239-248, March 2014 (article)

Abstract
Intracortical brain computer interfaces (iBCIs) decode intended movement from neural activity for the control of external devices such as a robotic arm. Standard approaches include a calibration phase to estimate decoding parameters. During iBCI operation, the statistical properties of the neural activity can depart from those observed during calibration, sometimes hindering a user’s ability to control the iBCI. To address this problem, we adaptively correct the offset terms within a Kalman filter decoder via penalized maximum likelihood estimation. The approach can handle rapid shifts in neural signal behavior (on the order of seconds) and requires no knowledge of the intended movement. The algorithm, called MOCA, was tested using simulated neural activity and evaluated retrospectively using data collected from two people with tetraplegia operating an iBCI. In 19 clinical research test cases, where a nonadaptive Kalman filter yielded relatively high decoding errors, MOCA significantly reduced these errors (10.6 ± 10.1\%; p < 0.05, pairwise t-test). MOCA did not significantly change the error in the remaining 23 cases where a nonadaptive Kalman filter already performed well. These results suggest that MOCA provides more robust decoding than the standard Kalman filter for iBCIs.

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pdf DOI Project Page [BibTex]

pdf DOI Project Page [BibTex]


A physically-based approach to reflection separation: from physical modeling to constrained optimization
A physically-based approach to reflection separation: from physical modeling to constrained optimization

Kong, N., Tai, Y., Shin, J. S.

IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 36(2):209-221, IEEE Computer Society, Febuary 2014 (article)

Abstract
We propose a physically-based approach to separate reflection using multiple polarized images with a background scene captured behind glass. The input consists of three polarized images, each captured from the same view point but with a different polarizer angle separated by 45 degrees. The output is the high-quality separation of the reflection and background layers from each of the input images. A main technical challenge for this problem is that the mixing coefficient for the reflection and background layers depends on the angle of incidence and the orientation of the plane of incidence, which are spatially varying over the pixels of an image. Exploiting physical properties of polarization for a double-surfaced glass medium, we propose a multiscale scheme which automatically finds the optimal separation of the reflection and background layers. Through experiments, we demonstrate that our approach can generate superior results to those of previous methods.

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Publisher site [BibTex]

Publisher site [BibTex]


Simpler, faster, more accurate melanocytic lesion segmentation through MEDS
Simpler, faster, more accurate melanocytic lesion segmentation through MEDS

Peruch, F., Bogo, F., Bonazza, M., Cappelleri, V., Peserico, E.

IEEE Transactions on Biomedical Engineering, 61(2):557-565, February 2014 (article)

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DOI [BibTex]

DOI [BibTex]


Targets-Drives-Means: {A} declarative approach to dynamic behavior specification with higher usability
Targets-Drives-Means: A declarative approach to dynamic behavior specification with higher usability

Berenz, V., Suzuki, K.

Robotics and Autonomous Systems, 62(4):545-555, 2014 (article)

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link (url) DOI [BibTex]


Roombots: A hardware perspective on 3D self-reconfiguration and locomotion with a homogeneous modular robot
Roombots: A hardware perspective on 3D self-reconfiguration and locomotion with a homogeneous modular robot

Spröwitz, A., Moeckel, R., Vespignani, M., Bonardi, S., Ijspeert, A. J.

{Robotics and Autonomous Systems}, 62(7):1016-1033, Elsevier, Amsterdam, 2014 (article)

Abstract
In this work we provide hands-on experience on designing and testing a self-reconfiguring modular robotic system, Roombots (RB), to be used among others for adaptive furniture. In the long term, we envision that RB can be used to create sets of furniture, such as stools, chairs and tables that can move in their environment and that change shape and functionality during the day. In this article, we present the first, incremental results towards that long term vision. We demonstrate locomotion and reconfiguration of single and metamodule RB over 3D surfaces, in a structured environment equipped with embedded connection ports. RB assemblies can move around in non-structured environments, by using rotational or wheel-like locomotion. We show a proof of concept for transferring a Roombots metamodule (two in-series coupled RB modules) from the non-structured environment back into the structured grid, by aligning the RB metamodule in an entrapment mechanism. Finally, we analyze the remaining challenges to master the full Roombots scenario, and discuss the impact on future Roombots hardware.

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DOI [BibTex]

DOI [BibTex]


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A Limiting Property of the Matrix Exponential

Trimpe, S., D’Andrea, R.

IEEE Transactions on Automatic Control, 59(4):1105-1110, 2014 (article)

am ics

PDF DOI [BibTex]

PDF DOI [BibTex]


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Event-Based State Estimation With Variance-Based Triggering

Trimpe, S., D’Andrea, R.

IEEE Transactions on Automatic Control, 59(12):3266-3281, 2014 (article)

am ics

PDF Supplementary material DOI Project Page [BibTex]

PDF Supplementary material DOI Project Page [BibTex]


A freely-moving monkey treadmill model
A freely-moving monkey treadmill model

Foster, J., Nuyujukian, P., Freifeld, O., Gao, H., Walker, R., Ryu, S., Meng, T., Murmann, B., Black, M., Shenoy, K.

J. of Neural Engineering, 11(4):046020, 2014 (article)

Abstract
Objective: Motor neuroscience and brain-machine interface (BMI) design is based on examining how the brain controls voluntary movement, typically by recording neural activity and behavior from animal models. Recording technologies used with these animal models have traditionally limited the range of behaviors that can be studied, and thus the generality of science and engineering research. We aim to design a freely-moving animal model using neural and behavioral recording technologies that do not constrain movement. Approach: We have established a freely-moving rhesus monkey model employing technology that transmits neural activity from an intracortical array using a head-mounted device and records behavior through computer vision using markerless motion capture. We demonstrate the excitability and utility of this new monkey model, including the fi rst recordings from motor cortex while rhesus monkeys walk quadrupedally on a treadmill. Main results: Using this monkey model, we show that multi-unit threshold-crossing neural activity encodes the phase of walking and that the average ring rate of the threshold crossings covaries with the speed of individual steps. On a population level, we find that neural state-space trajectories of walking at diff erent speeds have similar rotational dynamics in some dimensions that evolve at the step rate of walking, yet robustly separate by speed in other state-space dimensions. Significance: Freely-moving animal models may allow neuroscientists to examine a wider range of behaviors and can provide a flexible experimental paradigm for examining the neural mechanisms that underlie movement generation across behaviors and environments. For BMIs, freely-moving animal models have the potential to aid prosthetic design by examining how neural encoding changes with posture, environment, and other real-world context changes. Understanding this new realm of behavior in more naturalistic settings is essential for overall progress of basic motor neuroscience and for the successful translation of BMIs to people with paralysis.

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pdf Supplementary DOI Project Page [BibTex]

pdf Supplementary DOI Project Page [BibTex]


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Perspective: Intelligent Systems: Bits and Bots

Spatz, J. P., Schaal, S.

Nature, (509), 2014, clmc (article)

Abstract
What is intelligence, and can we create it? Animals can perceive, reason, react and learn, but they are just one example of an intelligent system. Intelligent systems could be robots as large as humans, helping with search-and- rescue operations in dangerous places, or smart devices as tiny as a cell, delivering drugs to a target within the body. Even computing systems can be intelligent, by perceiving the world, crawling the web and processing â??big dataâ?? to extract and learn from complex information.Understanding not only how intelligence can be reproduced, but also how to build systems that put these ideas into practice, will be a challenge. Small intelligent systems will require new materials and fabrication methods, as well as com- pact information processors and power sources. And for nano-sized systems, the rules change altogether. The laws of physics operate very differently at tiny scales: for a nanorobot, swimming through water is like struggling through treacle.Researchers at the Max Planck Institute for Intelligent Systems have begun to solve these problems by developing new computational methods, experiment- ing with unique robotic systems and fabricating tiny, artificial propellers, like bacterial flagella, to propel nanocreations through their environment.

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PDF link (url) [BibTex]

PDF link (url) [BibTex]


Detection and Tracking of Occluded People
Detection and Tracking of Occluded People

Tang, S., Andriluka, M., Schiele, B.

International Journal of Computer Vision, 110, pages: 58-69, 2014 (article)

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PDF [BibTex]

PDF [BibTex]


Kinematic primitives for walking and trotting gaits of a quadruped robot with compliant legs
Kinematic primitives for walking and trotting gaits of a quadruped robot with compliant legs

Spröwitz, A. T., Ajallooeian, M., Tuleu, A., Ijspeert, A. J.

Frontiers in Computational Neuroscience, 8(27):1-13, 2014 (article)

Abstract
In this work we research the role of body dynamics in the complexity of kinematic patterns in a quadruped robot with compliant legs. Two gait patterns, lateral sequence walk and trot, along with leg length control patterns of different complexity were implemented in a modular, feed-forward locomotion controller. The controller was tested on a small, quadruped robot with compliant, segmented leg design, and led to self-stable and self-stabilizing robot locomotion. In-air stepping and on-ground locomotion leg kinematics were recorded, and the number and shapes of motion primitives accounting for 95\% of the variance of kinematic leg data were extracted. This revealed that kinematic patterns resulting from feed-forward control had a lower complexity (in-air stepping, 2–3 primitives) than kinematic patterns from on-ground locomotion (νm4 primitives), although both experiments applied identical motor patterns. The complexity of on-ground kinematic patterns had increased, through ground contact and mechanical entrainment. The complexity of observed kinematic on-ground data matches those reported from level-ground locomotion data of legged animals. Results indicate that a very low complexity of modular, rhythmic, feed-forward motor control is sufficient for level-ground locomotion in combination with passive compliant legged hardware.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


Segmentation of Biomedical Images Using Active Contour Model with Robust Image Feature and Shape Prior
Segmentation of Biomedical Images Using Active Contour Model with Robust Image Feature and Shape Prior

S. Y. Yeo, X. Xie, I. Sazonov, P. Nithiarasu

International Journal for Numerical Methods in Biomedical Engineering, 30(2):232- 248, 2014 (article)

Abstract
In this article, a new level set model is proposed for the segmentation of biomedical images. The image energy of the proposed model is derived from a robust image gradient feature which gives the active contour a global representation of the geometric configuration, making it more robust in dealing with image noise, weak edges, and initial configurations. Statistical shape information is incorporated using nonparametric shape density distribution, which allows the shape model to handle relatively large shape variations. The segmentation of various shapes from both synthetic and real images depict the robustness and efficiency of the proposed method.

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[BibTex]

[BibTex]


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An autonomous manipulation system based on force control and optimization

Righetti, L., Kalakrishnan, M., Pastor, P., Binney, J., Kelly, J., Voorhies, R. C., Sukhatme, G. S., Schaal, S.

Autonomous Robots, 36(1-2):11-30, January 2014 (article)

Abstract
In this paper we present an architecture for autonomous manipulation. Our approach is based on the belief that contact interactions during manipulation should be exploited to improve dexterity and that optimizing motion plans is useful to create more robust and repeatable manipulation behaviors. We therefore propose an architecture where state of the art force/torque control and optimization-based motion planning are the core components of the system. We give a detailed description of the modules that constitute the complete system and discuss the challenges inherent to creating such a system. We present experimental results for several grasping and manipulation tasks to demonstrate the performance and robustness of our approach.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Learning of grasp selection based on shape-templates

Herzog, A., Pastor, P., Kalakrishnan, M., Righetti, L., Bohg, J., Asfour, T., Schaal, S.

Autonomous Robots, 36(1-2):51-65, January 2014 (article)

Abstract
The ability to grasp unknown objects still remains an unsolved problem in the robotics community. One of the challenges is to choose an appropriate grasp configuration, i.e., the 6D pose of the hand relative to the object and its finger configuration. In this paper, we introduce an algorithm that is based on the assumption that similarly shaped objects can be grasped in a similar way. It is able to synthesize good grasp poses for unknown objects by finding the best matching object shape templates associated with previously demonstrated grasps. The grasp selection algorithm is able to improve over time by using the information of previous grasp attempts to adapt the ranking of the templates to new situations. We tested our approach on two different platforms, the Willow Garage PR2 and the Barrett WAM robot, which have very different hand kinematics. Furthermore, we compared our algorithm with other grasp planners and demonstrated its superior performance. The results presented in this paper show that the algorithm is able to find good grasp configurations for a large set of unknown objects from a relatively small set of demonstrations, and does improve its performance over time.

am mg

link (url) DOI [BibTex]


A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles behind Them
A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles behind Them

Sun, D., Roth, S., Black, M. J.

International Journal of Computer Vision (IJCV), 106(2):115-137, 2014 (article)

Abstract
The accuracy of optical flow estimation algorithms has been improving steadily as evidenced by results on the Middlebury optical flow benchmark. The typical formulation, however, has changed little since the work of Horn and Schunck. We attempt to uncover what has made recent advances possible through a thorough analysis of how the objective function, the optimization method, and modern implementation practices influence accuracy. We discover that "classical'' flow formulations perform surprisingly well when combined with modern optimization and implementation techniques. One key implementation detail is the median filtering of intermediate flow fields during optimization. While this improves the robustness of classical methods it actually leads to higher energy solutions, meaning that these methods are not optimizing the original objective function. To understand the principles behind this phenomenon, we derive a new objective function that formalizes the median filtering heuristic. This objective function includes a non-local smoothness term that robustly integrates flow estimates over large spatial neighborhoods. By modifying this new term to include information about flow and image boundaries we develop a method that can better preserve motion details. To take advantage of the trend towards video in wide-screen format, we further introduce an asymmetric pyramid downsampling scheme that enables the estimation of longer range horizontal motions. The methods are evaluated on Middlebury, MPI Sintel, and KITTI datasets using the same parameter settings.

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pdf full text code [BibTex]

pdf full text code [BibTex]


Automatic 4D Reconstruction of Patient-Specific Cardiac Mesh with 1- to-1 Vertex Correspondence from Segmented Contours Lines
Automatic 4D Reconstruction of Patient-Specific Cardiac Mesh with 1- to-1 Vertex Correspondence from Segmented Contours Lines

C. W. Lim, Y. Su, S. Y. Yeo, G. M. Ng, V. T. Nguyen, L. Zhong, R. S. Tan, K. K. Poh, P. Chai,

PLOS ONE, 9(4), 2014 (article)

Abstract
We propose an automatic algorithm for the reconstruction of patient-specific cardiac mesh models with 1-to-1 vertex correspondence. In this framework, a series of 3D meshes depicting the endocardial surface of the heart at each time step is constructed, based on a set of border delineated magnetic resonance imaging (MRI) data of the whole cardiac cycle. The key contribution in this work involves a novel reconstruction technique to generate a 4D (i.e., spatial–temporal) model of the heart with 1-to-1 vertex mapping throughout the time frames. The reconstructed 3D model from the first time step is used as a base template model and then deformed to fit the segmented contours from the subsequent time steps. A method to determine a tree-based connectivity relationship is proposed to ensure robust mapping during mesh deformation. The novel feature is the ability to handle intra- and inter-frame 2D topology changes of the contours, which manifests as a series of merging and splitting of contours when the images are viewed either in a spatial or temporal sequence. Our algorithm has been tested on five acquisitions of cardiac MRI and can successfully reconstruct the full 4D heart model in around 30 minutes per subject. The generated 4D heart model conforms very well with the input segmented contours and the mesh element shape is of reasonably good quality. The work is important in the support of downstream computational simulation activities.

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[BibTex]

[BibTex]

2007


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Learning static Gestalt laws through dynamic experience

Ostrovsky, Y., Wulff, J., Sinha, P.

Journal of Vision, 7(9):315-315, ARVO, June 2007 (article)

Abstract
The Gestalt laws (Wertheimer 1923) are widely regarded as the rules that help us parse the world into objects. However, it is unclear as to how these laws are acquired by an infant's visual system. Classically, these “laws” have been presumed to be innate (Kellman and Spelke 1983). But, more recent work in infant development, showing the protracted time-course over which these grouping principles emerge (e.g., Johnson and Aslin 1995; Craton 1996), suggests that visual experience might play a role in their genesis. Specifically, our studies of patients with late-onset vision (Project Prakash; VSS 2006) and evidence from infant development both point to an early role of common motion cues for object grouping. Here we explore the possibility that the privileged status of motion in the developmental timeline is not happenstance, but rather serves to bootstrap the learning of static Gestalt cues. Our approach involves computational analyses of real-world motion sequences to investigate whether primitive optic flow information is correlated with static figural cues that could eventually come to serve as proxies for grouping in the form of Gestalt principles. We calculated local optic flow maps and then examined how similarity of motion across image patches co-varied with similarity of certain figural properties in static frames. Results indicate that patches with similar motion are much more likely to have similar luminance, color, and orientation as compared to patches with dissimilar motion vectors. This regularity suggests that, in principle, common motion extracted from dynamic visual experience can provide enough information to bootstrap region grouping based on luminance and color and contour continuation mechanisms in static scenes. These observations, coupled with the cited experimental studies, lend credence to the hypothesis that static Gestalt laws might be learned through a bootstrapping process based on early dynamic experience.

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link (url) DOI [BibTex]

2007


link (url) DOI [BibTex]


Neuromotor prosthesis development
Neuromotor prosthesis development

Donoghue, J., Hochberg, L., Nurmikko, A., Black, M., Simeral, J., Friehs, G.

Medicine & Health Rhode Island, 90(1):12-15, January 2007 (article)

Abstract
Article describes a neuromotor prosthesis (NMP), in development at Brown University, that records human brain signals, decodes them, and transforms them into movement commands. An NMP is described as a system consisting of a neural interface, a decoding system, and a user interface, also called an effector; a closed-loop system would be completed by a feedback signal from the effector to the brain. The interface is based on neural spiking, a source of information-rich, rapid, complex control signals from the nervous system. The NMP described, named BrainGate, consists of a match-head sized platform with 100 thread-thin electrodes implanted just into the surface of the motor cortex where commands to move the hand emanate. Neural signals are decoded by a rack of computers that displays the resultant output as the motion of a cursor on a computer monitor. While computer cursor motion represents a form of virtual device control, this same command signal could be routed to a device to command motion of paralyzed muscles or the actions of prosthetic limbs. The researchers’ overall goal is the development of a fully implantable, wireless multi-neuron sensor for broad research, neural prosthetic, and human neurodiagnostic applications.

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pdf [BibTex]

pdf [BibTex]


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The new robotics - towards human-centered machines

Schaal, S.

HFSP Journal Frontiers of Interdisciplinary Research in the Life Sciences, 1(2):115-126, 2007, clmc (article)

Abstract
Research in robotics has moved away from its primary focus on industrial applications. The New Robotics is a vision that has been developed in past years by our own university and many other national and international research instiutions and addresses how increasingly more human-like robots can live among us and take over tasks where our current society has shortcomings. Elder care, physical therapy, child education, search and rescue, and general assistance in daily life situations are some of the examples that will benefit from the New Robotics in the near future. With these goals in mind, research for the New Robotics has to embrace a broad interdisciplinary approach, ranging from traditional mathematical issues of robotics to novel issues in psychology, neuroscience, and ethics. This paper outlines some of the important research problems that will need to be resolved to make the New Robotics a reality.

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link (url) [BibTex]

link (url) [BibTex]


On the spatial statistics of optical flow
On the spatial statistics of optical flow

Roth, S., Black, M. J.

International Journal of Computer Vision, 74(1):33-50, 2007 (article)

Abstract
We present an analysis of the spatial and temporal statistics of "natural" optical flow fields and a novel flow algorithm that exploits their spatial statistics. Training flow fields are constructed using range images of natural scenes and 3D camera motions recovered from hand-held and car-mounted video sequences. A detailed analysis of optical flow statistics in natural scenes is presented and machine learning methods are developed to learn a Markov random field model of optical flow. The prior probability of a flow field is formulated as a Field-of-Experts model that captures the spatial statistics in overlapping patches and is trained using contrastive divergence. This new optical flow prior is compared with previous robust priors and is incorporated into a recent, accurate algorithm for dense optical flow computation. Experiments with natural and synthetic sequences illustrate how the learned optical flow prior quantitatively improves flow accuracy and how it captures the rich spatial structure found in natural scene motion.

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pdf preprint pdf from publisher [BibTex]

pdf preprint pdf from publisher [BibTex]


Assistive technology and robotic control using {MI} ensemble-based neural interface systems in humans with tetraplegia
Assistive technology and robotic control using MI ensemble-based neural interface systems in humans with tetraplegia

Donoghue, J. P., Nurmikko, A., Black, M. J., Hochberg, L.

Journal of Physiology, Special Issue on Brain Computer Interfaces, 579, pages: 603-611, 2007 (article)

Abstract
This review describes the rationale, early stage development, and initial human application of neural interface systems (NISs) for humans with paralysis. NISs are emerging medical devices designed to allowpersonswith paralysis to operate assistive technologies or to reanimatemuscles based upon a command signal that is obtained directly fromthe brain. Such systems require the development of sensors to detect brain signals, decoders to transformneural activity signals into a useful command, and an interface for the user.We review initial pilot trial results of an NIS that is based on an intracortical microelectrode sensor that derives control signals from the motor cortex.We review recent findings showing, first, that neurons engaged by movement intentions persist in motor cortex years after injury or disease to the motor system, and second, that signals derived from motor cortex can be used by persons with paralysis to operate a range of devices. We suggest that, with further development, this form of NIS holds promise as a useful new neurotechnology for those with limited motor function or communication.We also discuss the additional potential for neural sensors to be used in the diagnosis and management of various neurological conditions and as a new way to learn about human brain function.

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pdf preprint pdf from publisher DOI [BibTex]

pdf preprint pdf from publisher DOI [BibTex]

2003


Learning the statistics of people in images and video
Learning the statistics of people in images and video

Sidenbladh, H., Black, M. J.

International Journal of Computer Vision, 54(1-3):183-209, August 2003 (article)

Abstract
This paper address the problems of modeling the appearance of humans and distinguishing human appearance from the appearance of general scenes. We seek a model of appearance and motion that is generic in that it accounts for the ways in which people's appearance varies and, at the same time, is specific enough to be useful for tracking people in natural scenes. Given a 3D model of the person projected into an image we model the likelihood of observing various image cues conditioned on the predicted locations and orientations of the limbs. These cues are taken to be steered filter responses corresponding to edges, ridges, and motion-compensated temporal differences. Motivated by work on the statistics of natural scenes, the statistics of these filter responses for human limbs are learned from training images containing hand-labeled limb regions. Similarly, the statistics of the filter responses in general scenes are learned to define a “background” distribution. The likelihood of observing a scene given a predicted pose of a person is computed, for each limb, using the likelihood ratio between the learned foreground (person) and background distributions. Adopting a Bayesian formulation allows cues to be combined in a principled way. Furthermore, the use of learned distributions obviates the need for hand-tuned image noise models and thresholds. The paper provides a detailed analysis of the statistics of how people appear in scenes and provides a connection between work on natural image statistics and the Bayesian tracking of people.

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pdf pdf from publisher code DOI [BibTex]

2003


pdf pdf from publisher code DOI [BibTex]


A framework for robust subspace learning
A framework for robust subspace learning

De la Torre, F., Black, M. J.

International Journal of Computer Vision, 54(1-3):117-142, August 2003 (article)

Abstract
Many computer vision, signal processing and statistical problems can be posed as problems of learning low dimensional linear or multi-linear models. These models have been widely used for the representation of shape, appearance, motion, etc., in computer vision applications. Methods for learning linear models can be seen as a special case of subspace fitting. One draw-back of previous learning methods is that they are based on least squares estimation techniques and hence fail to account for “outliers” which are common in realistic training sets. We review previous approaches for making linear learning methods robust to outliers and present a new method that uses an intra-sample outlier process to account for pixel outliers. We develop the theory of Robust Subspace Learning (RSL) for linear models within a continuous optimization framework based on robust M-estimation. The framework applies to a variety of linear learning problems in computer vision including eigen-analysis and structure from motion. Several synthetic and natural examples are used to develop and illustrate the theory and applications of robust subspace learning in computer vision.

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pdf code pdf from publisher Project Page [BibTex]

pdf code pdf from publisher Project Page [BibTex]