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2019


Thumb xl teaser results
Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation

Ranjan, A., Jampani, V., Balles, L., Kim, K., Sun, D., Wulff, J., Black, M. J.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2019 (inproceedings)

Abstract
We address the unsupervised learning of several interconnected problems in low-level vision: single view depth prediction, camera motion estimation, optical flow, and segmentation of a video into the static scene and moving regions. Our key insight is that these four fundamental vision problems are coupled through geometric constraints. Consequently, learning to solve them together simplifies the problem because the solutions can reinforce each other. We go beyond previous work by exploiting geometry more explicitly and segmenting the scene into static and moving regions. To that end, we introduce Competitive Collaboration, a framework that facilitates the coordinated training of multiple specialized neural networks to solve complex problems. Competitive Collaboration works much like expectation-maximization, but with neural networks that act as both competitors to explain pixels that correspond to static or moving regions, and as collaborators through a moderator that assigns pixels to be either static or independently moving. Our novel method integrates all these problems in a common framework and simultaneously reasons about the segmentation of the scene into moving objects and the static background, the camera motion, depth of the static scene structure, and the optical flow of moving objects. Our model is trained without any supervision and achieves state-of-the-art performance among joint unsupervised methods on all sub-problems.

Paper link (url) Project Page Project Page [BibTex]


Thumb xl cvpr2019 demo v2.001
Local Temporal Bilinear Pooling for Fine-grained Action Parsing

Zhang, Y., Tang, S., Muandet, K., Jarvers, C., Neumann, H.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2019 (inproceedings)

Abstract
Fine-grained temporal action parsing is important in many applications, such as daily activity understanding, human motion analysis, surgical robotics and others requiring subtle and precise operations in a long-term period. In this paper we propose a novel bilinear pooling operation, which is used in intermediate layers of a temporal convolutional encoder-decoder net. In contrast to other work, our proposed bilinear pooling is learnable and hence can capture more complex local statistics than the conventional counterpart. In addition, we introduce exact lower-dimension representations of our bilinear forms, so that the dimensionality is reduced with neither information loss nor extra computation. We perform intensive experiments to quantitatively analyze our model and show the superior performances to other state-of-the-art work on various datasets.

Code video demo pdf link (url) [BibTex]

Code video demo pdf link (url) [BibTex]


Thumb xl ringnet
Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision

Sanyal, S., Bolkart, T., Feng, H., Black, M. J.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2019 (inproceedings)

Abstract
The estimation of 3D face shape from a single image must be robust to variations in lighting, head pose, expression, facial hair, makeup, and occlusions. Robustness requires a large training set of in-the-wild images, which by construction, lack ground truth 3D shape. To train a network without any 2D-to-3D supervision, we present RingNet, which learns to compute 3D face shape from a single image. Our key observation is that an individual’s face shape is constant across images, regardless of expression, pose, lighting, etc. RingNet leverages multiple images of a person and automatically detected 2D face features. It uses a novel loss that encourages the face shape to be similar when the identity is the same and different for different people. We achieve invariance to expression by representing the face using the FLAME model. Once trained, our method takes a single image and outputs the parameters of FLAME, which can be readily animated. Additionally we create a new database of faces “not quite in-the-wild” (NoW) with 3D head scans and high-resolution images of the subjects in a wide variety of conditions. We evaluate publicly available methods and find that RingNet is more accurate than methods that use 3D supervision. The dataset, model, and results are available for research purposes.

code pdf preprint link (url) [BibTex]

code pdf preprint link (url) [BibTex]


Thumb xl obman new
Learning joint reconstruction of hands and manipulated objects

Hasson, Y., Varol, G., Tzionas, D., Kalevatykh, I., Black, M. J., Laptev, I., Schmid, C.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2019 (inproceedings)

Abstract
Estimating hand-object manipulations is essential for interpreting and imitating human actions. Previous work has made significant progress towards reconstruction of hand poses and object shapes in isolation. Yet, reconstructing hands and objects during manipulation is a more challenging task due to significant occlusions of both the hand and object. While presenting challenges, manipulations may also simplify the problem since the physics of contact restricts the space of valid hand-object configurations. For example, during manipulation, the hand and object should be in contact but not interpenetrate. In this work, we regularize the joint reconstruction of hands and objects with manipulation constraints. We present an end-to-end learnable model that exploits a novel contact loss that favors physically plausible hand-object constellations. Our approach improves grasp quality metrics over baselines, using RGB images as input. To train and evaluate the model, we also propose a new large-scale synthetic dataset, ObMan, with hand-object manipulations. We demonstrate the transferability of ObMan-trained models to real data.

pdf suppl poster link (url) Project Page Project Page [BibTex]

pdf suppl poster link (url) Project Page Project Page [BibTex]


Thumb xl smplex
Expressive Body Capture: 3D Hands, Face, and Body from a Single Image

Pavlakos, G., Choutas, V., Ghorbani, N., Bolkart, T., Osman, A. A. A., Tzionas, D., Black, M. J.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2019 (inproceedings)

Abstract
To facilitate the analysis of human actions, interactions and emotions, we compute a 3D model of human body pose, hand pose, and facial expression from a single monocular image. To achieve this, we use thousands of 3D scans to train a new, unified, 3D model of the human body, SMPL-X, that extends SMPL with fully articulated hands and an expressive face. Learning to regress the parameters of SMPL-X directly from images is challenging without paired images and 3D ground truth. Consequently, we follow the approach of SMPLify, which estimates 2D features and then optimizes model parameters to fit the features. We improve on SMPLify in several significant ways: (1) we detect 2D features corresponding to the face, hands, and feet and fit the full SMPL-X model to these; (2) we train a new neural network pose prior using a large MoCap dataset; (3) we define a new interpenetration penalty that is both fast and accurate; (4) we automatically detect gender and the appropriate body models (male, female, or neutral); (5) our PyTorch implementation achieves a speedup of more than 8x over Chumpy. We use the new method, SMPLify-X, to fit SMPL-X to both controlled images and images in the wild. We evaluate 3D accuracy on a new curated dataset comprising 100 images with pseudo ground-truth. This is a step towards automatic expressive human capture from monocular RGB data. The models, code, and data are available for research purposes at https://smpl-x.is.tue.mpg.de.

video code pdf suppl poster link (url) Project Page [BibTex]


Thumb xl voca
Capture, Learning, and Synthesis of 3D Speaking Styles

Cudeiro, D., Bolkart, T., Laidlaw, C., Ranjan, A., Black, M. J.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2019 (inproceedings)

Abstract
Audio-driven 3D facial animation has been widely explored, but achieving realistic, human-like performance is still unsolved. This is due to the lack of available 3D datasets, models, and standard evaluation metrics. To address this, we introduce a unique 4D face dataset with about 29 minutes of 4D scans captured at 60 fps and synchronized audio from 12 speakers. We then train a neural network on our dataset that factors identity from facial motion. The learned model, VOCA (Voice Operated Character Animation) takes any speech signal as input—even speech in languages other than English—and realistically animates a wide range of adult faces. Conditioning on subject labels during training allows the model to learn a variety of realistic speaking styles. VOCA also provides animator controls to alter speaking style, identity-dependent facial shape, and pose (i.e. head, jaw, and eyeball rotations) during animation. To our knowledge, VOCA is the only realistic 3D facial animation model that is readily applicable to unseen subjects without retargeting. This makes VOCA suitable for tasks like in-game video, virtual reality avatars, or any scenario in which the speaker, speech, or language is not known in advance. We make the dataset and model available for research purposes at http://voca.is.tue.mpg.de.

code Project Page video paper [BibTex]

code Project Page video paper [BibTex]


Thumb xl screen shot 2019 07 15 at 19.10.21
Learning to Train with Synthetic Humans

Hoffmann, D., Tzionas, D., Black, M., Tang, S.

In 2019 (inproceedings)

Abstract
Neural networks need big annotated datasets for training. However, manual annotation can be too expensive or even unfeasible for certain tasks, like multi-person 2D pose estimation with severe occlusions. A remedy for this is synthetic data with perfect ground truth. Here we explore two variations of synthetic data for this challenging problem; a dataset with purely synthetic humans, as well as a real dataset augmented with synthetic humans. We then study which approach better generalizes to real data, as well as the influence of virtual humans in the training loss. We observe that not all synthetic samples are equally informative for training, while the informative samples are different for each training stage. To exploit this observation, we employ an adversarial student-teacher framework; the teacher improves the student by providing the hardest samples for its current state as a challenge. Experiments show that this student-teacher framework outperforms all our baselines.

[BibTex]


Thumb xl model
Resisting Adversarial Attacks using Gaussian Mixture Variational Autoencoders

Ghosh, P., Losalka, A., Black, M. J.

In Proc. AAAI, 2019 (inproceedings)

Abstract
Susceptibility of deep neural networks to adversarial attacks poses a major theoretical and practical challenge. All efforts to harden classifiers against such attacks have seen limited success till now. Two distinct categories of samples against which deep neural networks are vulnerable, ``adversarial samples" and ``fooling samples", have been tackled separately so far due to the difficulty posed when considered together. In this work, we show how one can defend against them both under a unified framework. Our model has the form of a variational autoencoder with a Gaussian mixture prior on the latent variable, such that each mixture component corresponds to a single class. We show how selective classification can be performed using this model, thereby causing the adversarial objective to entail a conflict. The proposed method leads to the rejection of adversarial samples instead of misclassification, while maintaining high precision and recall on test data. It also inherently provides a way of learning a selective classifier in a semi-supervised scenario, which can similarly resist adversarial attacks. We further show how one can reclassify the detected adversarial samples by iterative optimization.

link (url) Project Page [BibTex]


Thumb xl rae
From Variational to Deterministic Autoencoders

Ghosh*, P., Sajjadi*, M. S. M., Vergari, A., Black, M. J., Schölkopf, B.

2019, *equal contribution (conference) Submitted

Abstract
Variational Autoencoders (VAEs) provide a theoretically-backed framework for deep generative models. However, they often produce “blurry” images, which is linked to their training objective. Sampling in the most popular implementation, the Gaussian VAE, can be interpreted as simply injecting noise to the input of a deterministic decoder. In practice, this simply enforces a smooth latent space structure. We challenge the adoption of the full VAE framework on this specific point in favor of a simpler, deterministic one. Specifically, we investigate how substituting stochasticity with other explicit and implicit regularization schemes can lead to a meaningful latent space without having to force it to conform to an arbitrarily chosen prior. To retrieve a generative mechanism for sampling new data points, we propose to employ an efficient ex-post density estimation step that can be readily adopted both for the proposed deterministic autoencoders as well as to improve sample quality of existing VAEs. We show in a rigorous empirical study that regularized deterministic autoencoding achieves state-of-the-art sample quality on the common MNIST, CIFAR-10 and CelebA datasets.

arXiv [BibTex]

2011


Thumb xl teaser iccv2011
Outdoor Human Motion Capture using Inverse Kinematics and von Mises-Fisher Sampling

Pons-Moll, G., Baak, A., Gall, J., Leal-Taixe, L., Mueller, M., Seidel, H., Rosenhahn, B.

In IEEE International Conference on Computer Vision (ICCV), pages: 1243-1250, November 2011 (inproceedings)

project page pdf supplemental [BibTex]

2011

project page pdf supplemental [BibTex]


Thumb xl iccv2011homepageimage notext small
Home 3D body scans from noisy image and range data

Weiss, A., Hirshberg, D., Black, M.

In Int. Conf. on Computer Vision (ICCV), pages: 1951-1958, IEEE, Barcelona, November 2011 (inproceedings)

Abstract
The 3D shape of the human body is useful for applications in fitness, games and apparel. Accurate body scanners, however, are expensive, limiting the availability of 3D body models. We present a method for human shape reconstruction from noisy monocular image and range data using a single inexpensive commodity sensor. The approach combines low-resolution image silhouettes with coarse range data to estimate a parametric model of the body. Accurate 3D shape estimates are obtained by combining multiple monocular views of a person moving in front of the sensor. To cope with varying body pose, we use a SCAPE body model which factors 3D body shape and pose variations. This enables the estimation of a single consistent shape while allowing pose to vary. Additionally, we describe a novel method to minimize the distance between the projected 3D body contour and the image silhouette that uses analytic derivatives of the objective function. We propose a simple method to estimate standard body measurements from the recovered SCAPE model and show that the accuracy of our method is competitive with commercial body scanning systems costing orders of magnitude more.

pdf YouTube poster Project Page Project Page [BibTex]

pdf YouTube poster Project Page Project Page [BibTex]


Thumb xl iccv2012
Means in spaces of tree-like shapes

Aasa Feragen, Soren Hauberg, Mads Nielsen, Francois Lauze

In Computer Vision (ICCV), 2011 IEEE International Conference on, pages: 736 -746, IEEE, november 2011 (inproceedings)

Publishers site PDF Suppl. material [BibTex]

Publishers site PDF Suppl. material [BibTex]


Thumb xl teaser iccvw
Everybody needs somebody: modeling social and grouping behavior on a linear programming multiple people tracker

Leal-Taixé, L., Rosenhahn, G. P. A. B.

In IEEE International Conference on Computer Vision Workshops (IICCVW), November 2011 (inproceedings)

project page pdf [BibTex]

project page pdf [BibTex]


Thumb xl lugano11small
Evaluating the Automated Alignment of 3D Human Body Scans

Hirshberg, D. A., Loper, M., Rachlin, E., Tsoli, A., Weiss, A., Corner, B., Black, M. J.

In 2nd International Conference on 3D Body Scanning Technologies, pages: 76-86, (Editors: D’Apuzzo, Nicola), Hometrica Consulting, Lugano, Switzerland, October 2011 (inproceedings)

Abstract
The statistical analysis of large corpora of human body scans requires that these scans be in alignment, either for a small set of key landmarks or densely for all the vertices in the scan. Existing techniques tend to rely on hand-placed landmarks or algorithms that extract landmarks from scans. The former is time consuming and subjective while the latter is error prone. Here we show that a model-based approach can align meshes automatically, producing alignment accuracy similar to that of previous methods that rely on many landmarks. Specifically, we align a low-resolution, artist-created template body mesh to many high-resolution laser scans. Our alignment procedure employs a robust iterative closest point method with a regularization that promotes smooth and locally rigid deformation of the template mesh. We evaluate our approach on 50 female body models from the CAESAR dataset that vary significantly in body shape. To make the method fully automatic, we define simple feature detectors for the head and ankles, which provide initial landmark locations. We find that, if body poses are fairly similar, as in CAESAR, the fully automated method provides dense alignments that enable statistical analysis and anthropometric measurement.

pdf slides DOI Project Page [BibTex]

pdf slides DOI Project Page [BibTex]


Thumb xl mt
Branch&Rank: Non-Linear Object Detection

(Best Impact Paper Prize)

Lehmann, A., Gehler, P., VanGool, L.

In Proceedings of the British Machine Vision Conference (BMVC), pages: 8.1-8.11, (Editors: Jesse Hoey and Stephen McKenna and Emanuele Trucco), BMVA Press, September 2011, http://dx.doi.org/10.5244/C.25.8 (inproceedings)

video of talk pdf slides supplementary [BibTex]

video of talk pdf slides supplementary [BibTex]


Thumb xl teaser dagm2011
Efficient and Robust Shape Matching for Model Based Human Motion Capture

Pons-Moll, G., Leal-Taixé, L., Truong, T., Rosenhahn, B.

In German Conference on Pattern Recognition (GCPR), pages: 416-425, September 2011 (inproceedings)

project page pdf [BibTex]

project page pdf [BibTex]


no image
BrainGate pilot clinical trials: Progress in translating neural engineering principles to clinical testing

Hochberg, L., Simeral, J., Black, M., Bacher, D., Barefoot, L., Berhanu, E., Borton, D., Cash, S., Feldman, J., Gallivan, E., Homer, M., Jarosiewicz, B., King, B., Liu, J., Malik, W., Masse, N., Perge, J., Rosler, D., Schmansky, N., Travers, B., Truccolo, W., Nurmikko, A., Donoghue, J.

33rd Annual International IEEE EMBS Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, August 2011 (conference)

[BibTex]

[BibTex]


Thumb xl screen shot 2012 02 23 at 09.35.10
Learning Output Kernels with Block Coordinate Descent

Dinuzzo, F., Ong, C. S., Gehler, P., Pillonetto, G.

In Proceedings of the 28th International Conference on Machine Learning (ICML-11), pages: 49-56, ICML ’11, (Editors: Getoor, Lise and Scheffer, Tobias), ACM, New York, NY, USA, June 2011 (inproceedings)

data+code pdf [BibTex]

data+code pdf [BibTex]


Thumb xl jampani11 spie
Role of expertise and contralateral symmetry in the diagnosis of pneumoconiosis: an experimental study

Jampani, V., Vaidya, V., Sivaswamy, J., Tourani, K. L.

In Proc. SPIE 7966, Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, 2011, Florida, March 2011 (inproceedings)

Abstract
Pneumoconiosis, a lung disease caused by the inhalation of dust, is mainly diagnosed using chest radiographs. The effects of using contralateral symmetric (CS) information present in chest radiographs in the diagnosis of pneumoconiosis are studied using an eye tracking experimental study. The role of expertise and the influence of CS information on the performance of readers with different expertise level are also of interest. Experimental subjects ranging from novices & medical students to staff radiologists were presented with 17 double and 16 single lung images, and were asked to give profusion ratings for each lung zone. Eye movements and the time for their diagnosis were also recorded. Kruskal-Wallis test (χ2(6) = 13.38, p = .038), showed that the observer error (average sum of absolute differences) in double lung images differed significantly across the different expertise categories when considering all the participants. Wilcoxon-signed rank test indicated that the observer error was significantly higher for single-lung images (Z = 3.13, p < .001) than for the double-lung images for all the participants. Mann-Whitney test (U = 28, p = .038) showed that the differential error between single and double lung images is significantly higher in doctors [staff & residents] than in non-doctors [others]. Thus, Expertise & CS information plays a significant role in the diagnosis of pneumoconiosis. CS information helps in diagnosing pneumoconiosis by reducing the general tendency of giving less profusion ratings. Training and experience appear to play important roles in learning to use the CS information present in the chest radiographs.

url link (url) [BibTex]

url link (url) [BibTex]


Thumb xl problem
Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance

Gehler, P., Rother, C., Kiefel, M., Zhang, L., Schölkopf, B.

In Advances in Neural Information Processing Systems 24, pages: 765-773, (Editors: Shawe-Taylor, John and Zemel, Richard S. and Bartlett, Peter L. and Pereira, Fernando C. N. and Weinberger, Kilian Q.), Curran Associates, Inc., Red Hook, NY, USA, 2011 (inproceedings)

Abstract
We address the challenging task of decoupling material properties from lighting properties given a single image. In the last two decades virtually all works have concentrated on exploiting edge information to address this problem. We take a different route by introducing a new prior on reflectance, that models reflectance values as being drawn from a sparse set of basis colors. This results in a Random Field model with global, latent variables (basis colors) and pixel-accurate output reflectance values. We show that without edge information high-quality results can be achieved, that are on par with methods exploiting this source of information. Finally, we are able to improve on state-of-the-art results by integrating edge information into our model. We believe that our new approach is an excellent starting point for future developments in this field.

website + code pdf poster Project Page Project Page [BibTex]

website + code pdf poster Project Page Project Page [BibTex]


Thumb xl fosterembs2011
Combining wireless neural recording and video capture for the analysis of natural gait

Foster, J., Freifeld, O., Nuyujukian, P., Ryu, S., Black, M. J., Shenoy, K.

In Proc. 5th Int. IEEE EMBS Conf. on Neural Engineering, pages: 613-616, IEEE, 2011 (inproceedings)

pdf Project Page [BibTex]

pdf Project Page [BibTex]


Thumb xl openbiosafetylab  a virtual world based biosafety training application for medical students
OpenBioSafetyLab: A virtual world based biosafety training application for medical students

Nakasone, A., Tang, S., Shigematsu, M., Heinecke, B., Fujimoto, S., Prendinger, H.

In International Conference on Information Technology: New Generations (ITNG), IEEE CPS, 2011 (inproceedings)

PDF [BibTex]

PDF [BibTex]


Thumb xl scia2011
Unscented Kalman Filtering for Articulated Human Tracking

Anders Boesen Lindbo Larsen, Soren Hauberg, Kim S. Pedersen

In Image Analysis, 6688, pages: 228-237, Lecture Notes in Computer Science, (Editors: Heyden, Anders and Kahl, Fredrik), Springer Berlin Heidelberg, 2011 (inproceedings)

Publishers site PDF [BibTex]

Publishers site PDF [BibTex]


no image
Adaptation for perception of the human body: Investigations of transfer across viewpoint and pose

Sekunova, A., Black, M. J., Parkinson, L., Barton, J. S.

Vision Sciences Society, 2011 (conference)

[BibTex]

[BibTex]


Thumb xl icip1
Level Set Segmentation with Robust Image Gradient Energy and Statistical Shape Prior

Si Yong Yeo, Xianghua Xie, Igor Sazonov, Perumal Nithiarasu

In IEEE International Conference on Image Processing, pages: 3397 - 3400, 2011 (inproceedings)

Abstract
We propose a new level set segmentation method with statistical shape prior using a variational approach. The image energy is derived from a robust image gradient feature. This gives the active contour a global representation of the geometric configuration, making it more robust to image noise, weak edges and initial configurations. Statistical shape information is incorporated using nonparametric shape density distribution, which allows the model to handle relatively large shape variations. Comparative examples using both synthetic and real images show the robustness and efficiency of the proposed method.

link (url) [BibTex]

link (url) [BibTex]


Thumb xl segmentation isbi11
Tagged Cardiac MR Image Segmentation Using Boundary & Regional-Support and Graph-based Deformable Priors

Xiang, B., Wang, C., Deux, J., Rahmouni, A., Paragios, N.

In IEEE International Symposium on Biomedical Imaging (ISBI), 2011 (inproceedings)

pdf [BibTex]

pdf [BibTex]


Thumb xl multi nrsfm
Multiview Structure from Motion in Trajectory Space

Zaheer, A., Akhter, I., Mohammad, H. B., Marzban, S., Khan, S.

In Computer Vision (ICCV), 2011 IEEE International Conference on, pages: 2447-2453, 2011 (inproceedings)

Abstract
Most nonrigid objects exhibit temporal regularities in their deformations. Recently it was proposed that these regularities can be parameterized by assuming that the non- rigid structure lies in a small dimensional trajectory space. In this paper, we propose a factorization approach for 3D reconstruction from multiple static cameras under the com- pact trajectory subspace representation. Proposed factor- ization is analogous to rank-3 factorization of rigid struc- ture from motion problem, in transformed space. The benefit of our approach is that the 3D trajectory basis can be directly learned from the image observations. This also allows us to impute missing observations and denoise tracking errors without explicit estimation of the 3D structure. In contrast to standard triangulation based methods which require points to be visible in at least two cameras, our ap- proach can reconstruct points, which remain occluded even in all the cameras for quite a long time. This makes our solution especially suitable for occlusion handling in motion capture systems. We demonstrate robustness of our method on challenging real and synthetic scenarios.

pdf project page [BibTex]

pdf project page [BibTex]


Thumb xl hmdb snapshot1
HMDB: A Large Video Database for Human Motion Recognition

Kuhne, H., Jhuang, H., Garrote, E., Poggio, T., Serre, T.

In IEEE International Conference on Computer Vision (ICCV), 2011 (inproceedings)

code, webpage, dataset pdf [BibTex]

code, webpage, dataset pdf [BibTex]


no image
Context dependent changes in grip selectivity in primate ventral premotor cortex

Franquemont, L., Vargas-Irwin, C., Black, M., Donoghue, J.

2011 Abstract Viewer and Itinerary Planner, Online, Society for Neuroscience, 2011, Online (conference)

[BibTex]

[BibTex]


no image
Towards a freely moving animal model: Combining markerless multi-camera video capture and wirelessly transmitted neural recording for the analysis of walking

Foster, J., Freifeld, O., Nuyujukian, P., Ryu, S., Black, M., Shenoy, K.

2011 Abstract Viewer and Itinerary Planner, Society for Neuroscience, 2011, Online (conference)

Project Page [BibTex]

Project Page [BibTex]


Thumb xl cmbve1
Variational Level Set Segmentation Using Shape Prior

Si Yong Yeo, Xianghua Xie, Igor Sazonov, Perumal Nithiarasu

In International Conference on Mathematical and Computational Biomedical Engineering, 2011 (inproceedings)

[BibTex]

[BibTex]


Thumb xl dagm2011imagesmall
Shape and pose-invariant correspondences using probabilistic geodesic surface embedding

Tsoli, A., Black, M. J.

In 33rd Annual Symposium of the German Association for Pattern Recognition (DAGM), 6835, pages: 256-265, Lecture Notes in Computer Science, (Editors: Mester, Rudolf and Felsberg, Michael), Springer, 2011 (inproceedings)

Abstract
Correspondence between non-rigid deformable 3D objects provides a foundation for object matching and retrieval, recognition, and 3D alignment. Establishing 3D correspondence is challenging when there are non-rigid deformations or articulations between instances of a class. We present a method for automatically finding such correspondences that deals with significant variations in pose, shape and resolution between pairs of objects.We represent objects as triangular meshes and consider normalized geodesic distances as representing their intrinsic characteristics. Geodesic distances are invariant to pose variations and nearly invariant to shape variations when properly normalized. The proposed method registers two objects by optimizing a joint probabilistic model over a subset of vertex pairs between the objects. The model enforces preservation of geodesic distances between corresponding vertex pairs and inference is performed using loopy belief propagation in a hierarchical scheme. Additionally our method prefers solutions in which local shape information is consistent at matching vertices. We quantitatively evaluate our method and show that is is more accurate than a state of the art method.

pdf talk Project Page [BibTex]

pdf talk Project Page [BibTex]


no image
Visual orientation and direction selectivity through thalamic synchrony

Kelly, S., Stanley, G., Jin, J., Wang, Y., Desbordes, G., Wang, Q., Black, M., Alonso, J.

2011 Abstract Viewer and Itinerary Planner, Society for Neuroscience, 2011, Online (conference)

[BibTex]

[BibTex]


no image
Use of the BrainGate neural inteface system for more than five years by a woman with tetraplegia

Hochberg, L., Bacher, D., Barefoot, L., Berhanu, E., Black, M., Cash, S., Feldman, J., Gallivan, E., Homer, M., Jarosiewicz, B., King, B., Liu, J., Malik, W., Masse, N., Berge, J., Rosler, D., Schmansky, N., Simeral, J., Travers, B., Truccolo, W., Donoghue, J.

2011 Abstract Viewer and Itinerary Planner, Society for Neuroscience, 2011, Onine (conference)

[BibTex]

[BibTex]


no image
Extracting 3D Structures from Biomedical Data

Xianghua Xie, Si Yong Yeo, Igor Sazonov, Perumal Nithiarasu

Proceedings of the 5th International Conference on Bioinformatics and Biomedical Engineering, 2011 (conference)

[BibTex]

[BibTex]


Thumb xl emmcvpr2012
Data-Driven Importance Distributions for Articulated Tracking

Soren Hauberg, Kim S. Pedersen

In Energy Minimization Methods in Computer Vision and Pattern Recognition, 6819, pages: 287-299, Lecture Notes in Computer Science, (Editors: Boykov, Yuri and Kahl, Fredrik and Lempitsky, Victor and Schmidt, Frank), Springer Berlin Heidelberg, 2011 (inproceedings)

Publishers site Code PDF Suppl. material [BibTex]

Publishers site Code PDF Suppl. material [BibTex]


Thumb xl kdcv2011 teaser
A Physically Natural Metric for Human Motion and the Associated Brownian Motion Model

Soren Hauberg, Kim Steenstrup Pedersen

In 1st IEEE Workshop on Kernels and Distances for Computer Vision (ICCV workshop), 2011 (inproceedings)

Workshop link [BibTex]

Workshop link [BibTex]


Thumb xl thumb system1
Virtual Visual Servoing for Real-Time Robot Pose Estimation

Gratal, X., Romero, J., Kragic, D.

In International Federation of Automatic Control World Congress, IFAC, 2011 (inproceedings)

Pdf [BibTex]

Pdf [BibTex]


Thumb xl illumination cvpr11
Illumination Estimation and Cast Shadow Detection through a Higher-order Graphical Model

Panagopoulos, A., Wang, C., Samaras, D., Paragios, N.

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011 (inproceedings)

pdf [BibTex]

pdf [BibTex]


Thumb xl femursegmentation miccai11
Pose-invariant 3D Proximal Femur Estimation through Bi-Planar Image Segmentation with Hierarchical Higher-Order Graph-based Priors

Wang, C., Boussaid, H., Simon, L., Lazennec, J., Paragios, N.

In International Conference, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2011 (inproceedings)

pdf [BibTex]

pdf [BibTex]


Thumb xl sufacetracking cvpr11
Intrinsic Dense 3D Surface Tracking

Zeng, Y., Wang, C., Wang, Y., Gu, X., Samaras, D., Paragios, N.

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011 (inproceedings)

pdf [BibTex]

pdf [BibTex]


Thumb xl icann2011
An Empirical Study on the Performance of Spectral Manifold Learning Techniques

Peter Mysling, Soren Hauberg, Kim S. Pedersen

In Artificial Neural Networks and Machine Learning – ICANN 2011, 6791, pages: 347-354, Lecture Notes in Computer Science, (Editors: Honkela, Timo and Duch, Włodzisław and Girolami, Mark and Kaski, Samuel), Springer Berlin Heidelberg, 2011 (inproceedings)

Publishers site PDF [BibTex]

Publishers site PDF [BibTex]


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Separation of visual object features and grasp strategy in primate ventral premotor cortex

Vargas-Irwin, C., Franquemont, L., Black, M., Donoghue, J.

Neural Control of Movement, 21st Annual Conference, 2011 (conference)

[BibTex]

[BibTex]


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Discrete Minimum Distortion Correspondence Problems for Non-rigid Shape Matching

Wang, C., Bronstein, M. M., Bronstein, A. M., Paragios, N.

In International Conference on Scale Space and Variational Methods in Computer Vision (SSVM), 2011 (inproceedings)

pdf [BibTex]

pdf [BibTex]


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Viewpoint Invariant 3D Landmark Model Inference from Monocular 2D Images Using Higher-Order Priors

Wang, C., Zeng, Y., Simon, L., Kakadiaris, I., Samaras, D., Paragios, N.

In IEEE International Conference on Computer Vision (ICCV), 2011 (inproceedings)

pdf [BibTex]

pdf [BibTex]


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Correspondence estimation from non-rigid motion information

Wulff, J., Lotz, T., Stehle, T., Aach, T., Chase, J. G.

In Proc. SPIE, Proc. SPIE, (Editors: B. M. Dawant, D. R. Haynor), SPIE, 2011 (inproceedings)

Abstract
The DIET (Digital Image Elasto Tomography) system is a novel approach to screen for breast cancer using only optical imaging information of the surface of a vibrating breast. 3D tracking of skin surface motion without the requirement of external markers is desirable. A novel approach to establish point correspondences using pure skin images is presented here. Instead of the intensity, motion is used as the primary feature, which can be extracted using optical flow algorithms. Taking sequences of multiple frames into account, this motion information alone is accurate and unambiguous enough to allow for a 3D reconstruction of the breast surface. Two approaches, direct and probabilistic, for this correspondence estimation are presented here, suitable for different levels of calibration information accuracy. Reconstructions show that the results obtained using these methods are comparable in accuracy to marker-based methods while considerably increasing resolution. The presented method has high potential in optical tissue deformation and motion sensing.

pdf link (url) DOI [BibTex]

pdf link (url) DOI [BibTex]


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Cooperative Localization Based on Visually Shared Objects

Lima, P., Santos, P., Oliveira, R., Ahmad, A., Santos, J.

In RoboCup 2010: Robot Soccer World Cup XIV, pages: 350-361, Lecture Notes in Computer Science ; 6556, Springer, Berlin, Germany, 2011 (inproceedings)

Abstract
In this paper we describe a cooperative localization algorithm based on a modification of the Monte Carlo Localization algorithm where, when a robot detects it is lost, particles are spread not uniformly in the state space, but rather according to the information on the location of an object whose distance and bearing is measured by the lost robot. The object location is provided by other robots of the same team using explicit (wireless) communication. Results of application of the method to a team of real robots are presented.

DOI [BibTex]

DOI [BibTex]

2005


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A quantitative evaluation of video-based 3D person tracking

Balan, A. O., Sigal, L., Black, M. J.

In The Second Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, VS-PETS, pages: 349-356, October 2005 (inproceedings)

pdf [BibTex]

2005

pdf [BibTex]


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Inferring attentional state and kinematics from motor cortical firing rates

Wood, F., Prabhat, , Donoghue, J. P., Black, M. J.

In Proc. IEEE Engineering in Medicine and Biology Society, pages: 1544-1547, September 2005 (inproceedings)

pdf [BibTex]

pdf [BibTex]


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Motor cortical decoding using an autoregressive moving average model

Fisher, J., Black, M. J.

In Proc. IEEE Engineering in Medicine and Biology Society, pages: 1469-1472, September 2005 (inproceedings)

pdf [BibTex]

pdf [BibTex]