Publications | Perceiving Systems - Max Planck Institute for Intelligent Systems

61 results (View BibTeX file of all listed publications)

2013

Branch&Rank for Efficient Object Detection

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

International Journal of Computer Vision, Springer, December 2013 (article)

Abstract

Ranking hypothesis sets is a powerful concept for efficient object detection. In this work, we propose a branch&rank scheme that detects objects with often less than 100 ranking operations. This efficiency enables the use of strong and also costly classifiers like non-linear SVMs with RBF-TeX kernels. We thereby relieve an inherent limitation of branch&bound methods as bounds are often not tight enough to be effective in practice. Our approach features three key components: a ranking function that operates on sets of hypotheses and a grouping of these into different tasks. Detection efficiency results from adaptively sub-dividing the object search space into decreasingly smaller sets. This is inherited from branch&bound, while the ranking function supersedes a tight bound which is often unavailable (except for rather limited function classes). The grouping makes the system effective: it separates image classification from object recognition, yet combines them in a single formulation, phrased as a structured SVM problem. A novel aspect of branch&rank is that a better ranking function is expected to decrease the number of classifier calls during detection. We use the VOC’07 dataset to demonstrate the algorithmic properties of branch&rank.

pdf link (url) [BibTex]

2013

Lehmann, A., Gehler, P., VanGool, L. Branch&Rank for Efficient Object Detection International Journal of Computer Vision, Springer, December 2013 (article)

pdf link (url) [BibTex]

Strong Appearance and Expressive Spatial Models for Human Pose Estimation

Pishchulin, L., Andriluka, M., Gehler, P., Schiele, B.

In International Conference on Computer Vision (ICCV), pages: 3487 - 3494 , IEEE, December 2013 (inproceedings)

Abstract

Typical approaches to articulated pose estimation combine spatial modelling of the human body with appearance modelling of body parts. This paper aims to push the state-of-the-art in articulated pose estimation in two ways. First we explore various types of appearance representations aiming to substantially improve the body part hypotheses. And second, we draw on and combine several recently proposed powerful ideas such as more flexible spatial models as well as image-conditioned spatial models. In a series of experiments we draw several important conclusions: (1) we show that the proposed appearance representations are complementary; (2) we demonstrate that even a basic tree-structure spatial human body model achieves state-of-the-art performance when augmented with the proper appearance representation; and (3) we show that the combination of the best performing appearance model with a flexible image-conditioned spatial model achieves the best result, significantly improving over the state of the art, on the "Leeds Sports Poses'' and "Parse'' benchmarks.

pdf DOI Project Page [BibTex]

Pishchulin, L., Andriluka, M., Gehler, P., Schiele, B. Strong Appearance and Expressive Spatial Models for Human Pose Estimation In International Conference on Computer Vision (ICCV), pages: 3487 - 3494 , IEEE, December 2013 (inproceedings)

pdf DOI Project Page [BibTex]

Extracting Postural Synergies for Robotic Grasping

Romero, J., Feix, T., Ek, C., Kjellstrom, H., Kragic, D.

Robotics, IEEE Transactions on, 29(6):1342-1352, December 2013 (article)

[BibTex]

Romero, J., Feix, T., Ek, C., Kjellstrom, H., Kragic, D. Extracting Postural Synergies for Robotic Grasping Robotics, IEEE Transactions on, 29(6):1342-1352, December 2013 (article)

[BibTex]

Understanding High-Level Semantics by Modeling Traffic Patterns

Zhang, H., Geiger, A., Urtasun, R.

In International Conference on Computer Vision, pages: 3056-3063, Sydney, Australia, December 2013 (inproceedings)

Abstract

In this paper, we are interested in understanding the semantics of outdoor scenes in the context of autonomous driving. Towards this goal, we propose a generative model of 3D urban scenes which is able to reason not only about the geometry and objects present in the scene, but also about the high-level semantics in the form of traffic patterns. We found that a small number of patterns is sufficient to model the vast majority of traffic scenes and show how these patterns can be learned. As evidenced by our experiments, this high-level reasoning significantly improves the overall scene estimation as well as the vehicle-to-lane association when compared to state-of-the-art approaches. All data and code will be made available upon publication.

pdf [BibTex]

Zhang, H., Geiger, A., Urtasun, R. Understanding High-Level Semantics by Modeling Traffic Patterns In International Conference on Computer Vision, pages: 3056-3063, Sydney, Australia, December 2013 (inproceedings)

pdf [BibTex]

A Non-parametric Bayesian Network Prior of Human Pose

Lehrmann, A. M., Gehler, P., Nowozin, S.

In Proceedings IEEE Conf. on Computer Vision (ICCV), pages: 1281-1288, December 2013 (inproceedings)

Abstract

Having a sensible prior of human pose is a vital ingredient for many computer vision applications, including tracking and pose estimation. While the application of global non-parametric approaches and parametric models has led to some success, finding the right balance in terms of flexibility and tractability, as well as estimating model parameters from data has turned out to be challenging. In this work, we introduce a sparse Bayesian network model of human pose that is non-parametric with respect to the estimation of both its graph structure and its local distributions. We describe an efficient sampling scheme for our model and show its tractability for the computation of exact log-likelihoods. We empirically validate our approach on the Human 3.6M dataset and demonstrate superior performance to global models and parametric networks. We further illustrate our model's ability to represent and compose poses not present in the training set (compositionality) and describe a speed-accuracy trade-off that allows realtime scoring of poses.

Project page pdf DOI Project Page [BibTex]

Lehrmann, A. M., Gehler, P., Nowozin, S. A Non-parametric Bayesian Network Prior of Human Pose In Proceedings IEEE Conf. on Computer Vision (ICCV), pages: 1281-1288, December 2013 (inproceedings)

Project page pdf DOI Project Page [BibTex]

Towards understanding action recognition

Jhuang, H., Gall, J., Zuffi, S., Schmid, C., Black, M. J.

In IEEE International Conference on Computer Vision (ICCV), pages: 3192-3199, IEEE, Sydney, Australia, December 2013 (inproceedings)

Abstract

Although action recognition in videos is widely studied, current methods often fail on real-world datasets. Many recent approaches improve accuracy and robustness to cope with challenging video sequences, but it is often unclear what affects the results most. This paper attempts to provide insights based on a systematic performance evaluation using thoroughly-annotated data of human actions. We annotate human Joints for the HMDB dataset (J-HMDB). This annotation can be used to derive ground truth optical flow and segmentation. We evaluate current methods using this dataset and systematically replace the output of various algorithms with ground truth. This enables us to discover what is important – for example, should we work on improving flow algorithms, estimating human bounding boxes, or enabling pose estimation? In summary, we find that highlevel pose features greatly outperform low/mid level features; in particular, pose over time is critical, but current pose estimation algorithms are not yet reliable enough to provide this information. We also find that the accuracy of a top-performing action recognition framework can be greatly increased by refining the underlying low/mid level features; this suggests it is important to improve optical flow and human detection algorithms. Our analysis and JHMDB dataset should facilitate a deeper understanding of action recognition algorithms.

Website Errata Poster Paper Slides DOI Project Page Project Page Project Page [BibTex]

Jhuang, H., Gall, J., Zuffi, S., Schmid, C., Black, M. J. Towards understanding action recognition In IEEE International Conference on Computer Vision (ICCV), pages: 3192-3199, IEEE, Sydney, Australia, December 2013 (inproceedings)

Website Errata Poster Paper Slides DOI Project Page Project Page Project Page [BibTex]

Mixing Decoded Cursor Velocity and Position from an Offline Kalman Filter Improves Cursor Control in People with Tetraplegia

Homer, M., Harrison, M., Black, M. J., Perge, J., Cash, S., Friehs, G., Hochberg, L.

In 6th International IEEE EMBS Conference on Neural Engineering, pages: 715-718, San Diego, November 2013 (inproceedings)

Abstract

Kalman filtering is a common method to decode neural signals from the motor cortex. In clinical research investigating the use of intracortical brain computer interfaces (iBCIs), the technique enabled people with tetraplegia to control assistive devices such as a computer or robotic arm directly from their neural activity. For reaching movements, the Kalman filter typically estimates the instantaneous endpoint velocity of the control device. Here, we analyzed attempted arm/hand movements by people with tetraplegia to control a cursor on a computer screen to reach several circular targets. A standard velocity Kalman filter is enhanced to additionally decode for the cursor’s position. We then mix decoded velocity and position to generate cursor movement commands. We analyzed data, offline, from two participants across six sessions. Root mean squared error between the actual and estimated cursor trajectory improved by 12.2 ±10.5% (pairwise t-test, p<0.05) as compared to a standard velocity Kalman filter. The findings suggest that simultaneously decoding for intended velocity and position and using them both to generate movement commands can improve the performance of iBCIs.

pdf Project Page [BibTex]

Homer, M., Harrison, M., Black, M. J., Perge, J., Cash, S., Friehs, G., Hochberg, L. Mixing Decoded Cursor Velocity and Position from an Offline Kalman Filter Improves Cursor Control in People with Tetraplegia In 6th International IEEE EMBS Conference on Neural Engineering, pages: 715-718, San Diego, November 2013 (inproceedings)

pdf Project Page [BibTex]

Markov Random Field Modeling, Inference & Learning in Computer Vision & Image Understanding: A Survey

Wang, C., Komodakis, N., Paragios, N.

Computer Vision and Image Understanding (CVIU), 117(11):1610-1627, November 2013 (article)

Abstract

In this paper, we present a comprehensive survey of Markov Random Fields (MRFs) in computer vision and image understanding, with respect to the modeling, the inference and the learning. While MRFs were introduced into the computer vision field about two decades ago, they started to become a ubiquitous tool for solving visual perception problems around the turn of the millennium following the emergence of efficient inference methods. During the past decade, a variety of MRF models as well as inference and learning methods have been developed for addressing numerous low, mid and high-level vision problems. While most of the literature concerns pairwise MRFs, in recent years we have also witnessed significant progress in higher-order MRFs, which substantially enhances the expressiveness of graph-based models and expands the domain of solvable problems. This survey provides a compact and informative summary of the major literature in this research topic.

Publishers site pdf [BibTex]

Wang, C., Komodakis, N., Paragios, N. Markov Random Field Modeling, Inference & Learning in Computer Vision & Image Understanding: A Survey Computer Vision and Image Understanding (CVIU), 117(11):1610-1627, November 2013 (article)

Publishers site pdf [BibTex]

Puppet Flow

Zuffi, S., Black, M. J.

(7), Max Planck Institute for Intelligent Systems, October 2013 (techreport)

Abstract

We introduce Puppet Flow (PF), a layered model describing the optical flow of a person in a video sequence. We consider video frames composed by two layers: a foreground layer corresponding to a person, and background. We model the background as an affine flow field. The foreground layer, being a moving person, requires reasoning about the articulated nature of the human body. We thus represent the foreground layer with the Deformable Structures model (DS), a parametrized 2D part-based human body representation. We call the motion field defined through articulated motion and deformation of the DS model, a Puppet Flow. By exploiting the DS representation, Puppet Flow is a parametrized optical flow field, where parameters are the person's pose, gender and body shape.

pdf Project Page Project Page [BibTex]

Zuffi, S., Black, M. J. Puppet Flow (7), Max Planck Institute for Intelligent Systems, October 2013 (techreport)

pdf Project Page Project Page [BibTex]

Vision meets Robotics: The KITTI Dataset

Geiger, A., Lenz, P., Stiller, C., Urtasun, R.

International Journal of Robotics Research, 32(11):1231 - 1237 , Sage Publishing, September 2013 (article)

Abstract

We present a novel dataset captured from a VW station wagon for use in mobile robotics and autonomous driving research. In total, we recorded 6 hours of traffic scenarios at 10-100 Hz using a variety of sensor modalities such as high-resolution color and grayscale stereo cameras, a Velodyne 3D laser scanner and a high-precision GPS/IMU inertial navigation system. The scenarios are diverse, capturing real-world traffic situations and range from freeways over rural areas to inner-city scenes with many static and dynamic objects. Our data is calibrated, synchronized and timestamped, and we provide the rectified and raw image sequences. Our dataset also contains object labels in the form of 3D tracklets and we provide online benchmarks for stereo, optical flow, object detection and other tasks. This paper describes our recording platform, the data format and the utilities that we provide.

pdf DOI [BibTex]

Geiger, A., Lenz, P., Stiller, C., Urtasun, R. Vision meets Robotics: The KITTI Dataset International Journal of Robotics Research, 32(11):1231 - 1237 , Sage Publishing, September 2013 (article)

pdf DOI [BibTex]

Human Pose Calculation from Optical Flow Data

Black, M., Loper, M., Romero, J., Zuffi, S.

European Patent Application EP 2843621 , August 2013 (patent)

Google Patents [BibTex]

Black, M., Loper, M., Romero, J., Zuffi, S. Human Pose Calculation from Optical Flow Data European Patent Application EP 2843621 , August 2013 (patent)

Google Patents [BibTex]

Statistics on Manifolds with Applications to Modeling Shape Deformations

Freifeld, O.

Brown University, August 2013 (phdthesis)

Abstract

Statistical models of non-rigid deformable shape have wide application in many fields, including computer vision, computer graphics, and biometry. We show that shape deformations are well represented through nonlinear manifolds that are also matrix Lie groups. These pattern-theoretic representations lead to several advantages over other alternatives, including a principled measure of shape dissimilarity and a natural way to compose deformations. Moreover, they enable building models using statistics on manifolds. Consequently, such models are superior to those based on Euclidean representations. We demonstrate this by modeling 2D and 3D human body shape. Shape deformations are only one example of manifold-valued data. More generally, in many computer-vision and machine-learning problems, nonlinear manifold representations arise naturally and provide a powerful alternative to Euclidean representations. Statistics is traditionally concerned with data in a Euclidean space, relying on the linear structure and the distances associated with such a space; this renders it inappropriate for nonlinear spaces. Statistics can, however, be generalized to nonlinear manifolds. Moreover, by respecting the underlying geometry, the statistical models result in not only more effective analysis but also consistent synthesis. We go beyond previous work on statistics on manifolds by showing how, even on these curved spaces, problems related to modeling a class from scarce data can be dealt with by leveraging information from related classes residing in different regions of the space. We show the usefulness of our approach with 3D shape deformations. To summarize our main contributions: 1) We define a new 2D articulated model -- more expressive than traditional ones -- of deformable human shape that factors body-shape, pose, and camera variations. Its high realism is obtained from training data generated from a detailed 3D model. 2) We define a new manifold-based representation of 3D shape deformations that yields statistical deformable-template models that are better than the current state-of-the- art. 3) We generalize a transfer learning idea from Euclidean spaces to Riemannian manifolds. This work demonstrates the value of modeling manifold-valued data and their statistics explicitly on the manifold. Specifically, the methods here provide new tools for shape analysis.

pdf Project Page [BibTex]

Freifeld, O. Statistics on Manifolds with Applications to Modeling Shape Deformations Brown University, August 2013 (phdthesis)

pdf Project Page [BibTex]

Poselet conditioned pictorial structures

Pishchulin, L., Andriluka, M., Gehler, P., Schiele, B.

In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages: 588 - 595, IEEE, Portland, OR, June 2013 (inproceedings)

pdf DOI Project Page [BibTex]

Pishchulin, L., Andriluka, M., Gehler, P., Schiele, B. Poselet conditioned pictorial structures In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages: 588 - 595, IEEE, Portland, OR, June 2013 (inproceedings)

pdf DOI Project Page [BibTex]

Occlusion Patterns for Object Class Detection

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

In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Portland, OR, June 2013 (inproceedings)

Abstract

Despite the success of recent object class recognition systems, the long-standing problem of partial occlusion re- mains a major challenge, and a principled solution is yet to be found. In this paper we leave the beaten path of meth- ods that treat occlusion as just another source of noise – instead, we include the occluder itself into the modelling, by mining distinctive, reoccurring occlusion patterns from annotated training data. These patterns are then used as training data for dedicated detectors of varying sophistica- tion. In particular, we evaluate and compare models that range from standard object class detectors to hierarchical, part-based representations of occluder/occludee pairs. In an extensive evaluation we derive insights that can aid fur- ther developments in tackling the occlusion challenge.

pdf Project Page [BibTex]

Pepik, B., Stark, M., Gehler, P., Schiele, B. Occlusion Patterns for Object Class Detection In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Portland, OR, June 2013 (inproceedings)

pdf Project Page [BibTex]

Lost! Leveraging the Crowd for Probabilistic Visual Self-Localization

(CVPR13 Best Paper Runner-Up)

Brubaker, M. A., Geiger, A., Urtasun, R.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2013), pages: 3057-3064, IEEE, Portland, OR, June 2013 (inproceedings)

Abstract

In this paper we propose an affordable solution to self- localization, which utilizes visual odometry and road maps as the only inputs. To this end, we present a probabilis- tic model as well as an efficient approximate inference al- gorithm, which is able to utilize distributed computation to meet the real-time requirements of autonomous systems. Because of the probabilistic nature of the model we are able to cope with uncertainty due to noisy visual odometry and inherent ambiguities in the map ( e.g ., in a Manhattan world). By exploiting freely available, community devel- oped maps and visual odometry measurements, we are able to localize a vehicle up to 3m after only a few seconds of driving on maps which contain more than 2,150km of driv- able roads.

pdf supplementary project page [BibTex]

Brubaker, M. A., Geiger, A., Urtasun, R. Lost! Leveraging the Crowd for Probabilistic Visual Self-Localization In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2013), pages: 3057-3064, IEEE, Portland, OR, June 2013 (inproceedings)

pdf supplementary project page [BibTex]

Human Pose Estimation using Body Parts Dependent Joint Regressors

Dantone, M., Gall, J., Leistner, C., van Gool, L.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages: 3041-3048, IEEE, Portland, OR, USA, June 2013 (inproceedings)

Abstract

In this work, we address the problem of estimating 2d human pose from still images. Recent methods that rely on discriminatively trained deformable parts organized in a tree model have shown to be very successful in solving this task. Within such a pictorial structure framework, we address the problem of obtaining good part templates by proposing novel, non-linear joint regressors. In particular, we employ two-layered random forests as joint regressors. The first layer acts as a discriminative, independent body part classifier. The second layer takes the estimated class distributions of the first one into account and is thereby able to predict joint locations by modeling the interdependence and co-occurrence of the parts. This results in a pose estimation framework that takes dependencies between body parts already for joint localization into account and is thus able to circumvent typical ambiguities of tree structures, such as for legs and arms. In the experiments, we demonstrate that our body parts dependent joint regressors achieve a higher joint localization accuracy than tree-based state-of-the-art methods.

pdf DOI Project Page [BibTex]

Dantone, M., Gall, J., Leistner, C., van Gool, L. Human Pose Estimation using Body Parts Dependent Joint Regressors In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages: 3041-3048, IEEE, Portland, OR, USA, June 2013 (inproceedings)

pdf DOI Project Page [BibTex]

A fully-connected layered model of foreground and background flow

Sun, D., Wulff, J., Sudderth, E., Pfister, H., Black, M.

In IEEE Conf. on Computer Vision and Pattern Recognition, (CVPR 2013), pages: 2451-2458, Portland, OR, June 2013 (inproceedings)

Abstract

Layered models allow scene segmentation and motion estimation to be formulated together and to inform one another. Traditional layered motion methods, however, employ fairly weak models of scene structure, relying on locally connected Ising/Potts models which have limited ability to capture long-range correlations in natural scenes. To address this, we formulate a fully-connected layered model that enables global reasoning about the complicated segmentations of real objects. Optimization with fully-connected graphical models is challenging, and our inference algorithm leverages recent work on efficient mean field updates for fully-connected conditional random fields. These methods can be implemented efficiently using high-dimensional Gaussian filtering. We combine these ideas with a layered flow model, and find that the long-range connections greatly improve segmentation into figure-ground layers when compared with locally connected MRF models. Experiments on several benchmark datasets show that the method can recover fine structures and large occlusion regions, with good flow accuracy and much lower computational cost than previous locally-connected layered models.

pdf Supplemental Material Project Page Project Page [BibTex]

Sun, D., Wulff, J., Sudderth, E., Pfister, H., Black, M. A fully-connected layered model of foreground and background flow In IEEE Conf. on Computer Vision and Pattern Recognition, (CVPR 2013), pages: 2451-2458, Portland, OR, June 2013 (inproceedings)

pdf Supplemental Material Project Page Project Page [BibTex]

Unscented Kalman Filtering on Riemannian Manifolds

Soren Hauberg, Francois Lauze, Kim S. Pedersen

Journal of Mathematical Imaging and Vision, 46(1):103-120, Springer Netherlands, May 2013 (article)

Publishers site PDF [BibTex]

Soren Hauberg, Francois Lauze, Kim S. Pedersen Unscented Kalman Filtering on Riemannian Manifolds Journal of Mathematical Imaging and Vision, 46(1):103-120, Springer Netherlands, May 2013 (article)

Publishers site PDF [BibTex]

Quasi-Newton Methods: A New Direction

Hennig, P., Kiefel, M.

Journal of Machine Learning Research, 14(1):843-865, March 2013 (article)

Abstract

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

website+code pdf link (url) [BibTex]

Hennig, P., Kiefel, M. Quasi-Newton Methods: A New Direction Journal of Machine Learning Research, 14(1):843-865, March 2013 (article)

website+code pdf link (url) [BibTex]

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

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

(CS-10-03), Brown University, Department of Computer Science, January 2013 (techreport)

pdf [BibTex]

Sun, D., Roth, S., Black, M. J. A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles Behind Them (CS-10-03), Brown University, Department of Computer Science, January 2013 (techreport)

pdf [BibTex]

Estimating Human Pose with Flowing Puppets

Zuffi, S., Romero, J., Schmid, C., Black, M. J.

In IEEE International Conference on Computer Vision (ICCV), pages: 3312-3319, 2013 (inproceedings)

Abstract

We address the problem of upper-body human pose estimation in uncontrolled monocular video sequences, without manual initialization. Most current methods focus on isolated video frames and often fail to correctly localize arms and hands. Inferring pose over a video sequence is advantageous because poses of people in adjacent frames exhibit properties of smooth variation due to the nature of human and camera motion. To exploit this, previous methods have used prior knowledge about distinctive actions or generic temporal priors combined with static image likelihoods to track people in motion. Here we take a different approach based on a simple observation: Information about how a person moves from frame to frame is present in the optical flow field. We develop an approach for tracking articulated motions that "links" articulated shape models of people in adjacent frames trough the dense optical flow. Key to this approach is a 2D shape model of the body that we use to compute how the body moves over time. The resulting "flowing puppets" provide a way of integrating image evidence across frames to improve pose inference. We apply our method on a challenging dataset of TV video sequences and show state-of-the-art performance.

pdf code data DOI Project Page Project Page Project Page [BibTex]

Zuffi, S., Romero, J., Schmid, C., Black, M. J. Estimating Human Pose with Flowing Puppets In IEEE International Conference on Computer Vision (ICCV), pages: 3312-3319, 2013 (inproceedings)

pdf code data DOI Project Page Project Page Project Page [BibTex]

A Comparison of Directional Distances for Hand Pose Estimation

Tzionas, D., Gall, J.

In German Conference on Pattern Recognition (GCPR), 8142, pages: 131-141, Lecture Notes in Computer Science, (Editors: Weickert, Joachim and Hein, Matthias and Schiele, Bernt), Springer, 2013 (inproceedings)

Abstract

Benchmarking methods for 3d hand tracking is still an open problem due to the difficulty of acquiring ground truth data. We introduce a new dataset and benchmarking protocol that is insensitive to the accumulative error of other protocols. To this end, we create testing frame pairs of increasing difficulty and measure the pose estimation error separately for each of them. This approach gives new insights and allows to accurately study the performance of each feature or method without employing a full tracking pipeline. Following this protocol, we evaluate various directional distances in the context of silhouette-based 3d hand tracking, expressed as special cases of a generalized Chamfer distance form. An appropriate parameter setup is proposed for each of them, and a comparative study reveals the best performing method in this context.

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

Tzionas, D., Gall, J. A Comparison of Directional Distances for Hand Pose Estimation In German Conference on Pattern Recognition (GCPR), 8142, pages: 131-141, Lecture Notes in Computer Science, (Editors: Weickert, Joachim and Hein, Matthias and Schiele, Bernt), Springer, 2013 (inproceedings)

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

A Generic Deformation Model for Dense Non-Rigid Surface Registration: a Higher-Order MRF-based Approach

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

In IEEE International Conference on Computer Vision (ICCV), pages: 3360~3367, 2013 (inproceedings)

pdf [BibTex]

Zeng, Y., Wang, C., Gu, X., Samaras, D., Paragios, N. A Generic Deformation Model for Dense Non-Rigid Surface Registration: a Higher-Order MRF-based Approach In IEEE International Conference on Computer Vision (ICCV), pages: 3360~3367, 2013 (inproceedings)

pdf [BibTex]

Random Forests for Real Time 3D Face Analysis

Fanelli, G., Dantone, M., Gall, J., Fossati, A., van Gool, L.

International Journal of Computer Vision, 101(3):437-458, Springer, 2013 (article)

Abstract

We present a random forest-based framework for real time head pose estimation from depth images and extend it to localize a set of facial features in 3D. Our algorithm takes a voting approach, where each patch extracted from the depth image can directly cast a vote for the head pose or each of the facial features. Our system proves capable of handling large rotations, partial occlusions, and the noisy depth data acquired using commercial sensors. Moreover, the algorithm works on each frame independently and achieves real time performance without resorting to parallel computations on a GPU. We present extensive experiments on publicly available, challenging datasets and present a new annotated head pose database recorded using a Microsoft Kinect.

data and code publisher's site pdf DOI Project Page [BibTex]

Fanelli, G., Dantone, M., Gall, J., Fossati, A., van Gool, L. Random Forests for Real Time 3D Face Analysis International Journal of Computer Vision, 101(3):437-458, Springer, 2013 (article)

data and code publisher's site pdf DOI Project Page [BibTex]

Markerless Motion Capture of Multiple Characters Using Multi-view Image Segmentation

Liu, Y., Gall, J., Stoll, C., Dai, Q., Seidel, H., Theobalt, C.

Transactions on Pattern Analysis and Machine Intelligence, 35(11):2720-2735, 2013 (article)

Abstract

Capturing the skeleton motion and detailed time-varying surface geometry of multiple, closely interacting peoples is a very challenging task, even in a multicamera setup, due to frequent occlusions and ambiguities in feature-to-person assignments. To address this task, we propose a framework that exploits multiview image segmentation. To this end, a probabilistic shape and appearance model is employed to segment the input images and to assign each pixel uniquely to one person. Given the articulated template models of each person and the labeled pixels, a combined optimization scheme, which splits the skeleton pose optimization problem into a local one and a lower dimensional global one, is applied one by one to each individual, followed with surface estimation to capture detailed nonrigid deformations. We show on various sequences that our approach can capture the 3D motion of humans accurately even if they move rapidly, if they wear wide apparel, and if they are engaged in challenging multiperson motions, including dancing, wrestling, and hugging.

data and video pdf DOI Project Page [BibTex]

Liu, Y., Gall, J., Stoll, C., Dai, Q., Seidel, H., Theobalt, C. Markerless Motion Capture of Multiple Characters Using Multi-view Image Segmentation Transactions on Pattern Analysis and Machine Intelligence, 35(11):2720-2735, 2013 (article)

data and video pdf DOI Project Page [BibTex]

Viewpoint and pose in body-form adaptation

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

Perception, 42(2):176-186, 2013 (article)

Abstract

Faces and bodies are complex structures, perception of which can play important roles in person identification and inference of emotional state. Face representations have been explored using behavioural adaptation: in particular, studies have shown that face aftereffects show relatively broad tuning for viewpoint, consistent with origin in a high-level structural descriptor far removed from the retinal image. Our goals were to determine first, if body aftereffects also showed a degree of viewpoint invariance, and second if they also showed pose invariance, given that changes in pose create even more dramatic changes in the 2-D retinal image. We used a 3-D model of the human body to generate headless body images, whose parameters could be varied to generate different body forms, viewpoints, and poses. In the first experiment, subjects adapted to varying viewpoints of either slim or heavy bodies in a neutral stance, followed by test stimuli that were all front-facing. In the second experiment, we used the same front-facing bodies in neutral stance as test stimuli, but compared adaptation from bodies in the same neutral stance to adaptation with the same bodies in different poses. We found that body aftereffects were obtained over substantial viewpoint changes, with no significant decline in aftereffect magnitude with increasing viewpoint difference between adapting and test images. Aftereffects also showed transfer across one change in pose but not across another. We conclude that body representations may have more viewpoint invariance than faces, and demonstrate at least some transfer across pose, consistent with a high-level structural description. Keywords: aftereffect, shape, face, representation

pdf from publisher abstract pdf link (url) Project Page [BibTex]

Sekunova, A., Black, M., Parkinson, L., Barton, J. J. S. Viewpoint and pose in body-form adaptation Perception, 42(2):176-186, 2013 (article)

pdf from publisher abstract pdf link (url) Project Page [BibTex]

Class-Specific Hough Forests for Object Detection

Gall, J., Lempitsky, V.

In Decision Forests for Computer Vision and Medical Image Analysis, pages: 143-157, 11, (Editors: Criminisi, A. and Shotton, J.), Springer, 2013 (incollection)

code Project Page [BibTex]

Gall, J., Lempitsky, V. Class-Specific Hough Forests for Object Detection In Decision Forests for Computer Vision and Medical Image Analysis, pages: 143-157, 11, (Editors: Criminisi, A. and Shotton, J.), Springer, 2013 (incollection)

code Project Page [BibTex]

Non-parametric hand pose estimation with object context

Romero, J., Kjellström, H., Ek, C. H., Kragic, D.

Image and Vision Computing , 31(8):555 - 564, 2013 (article)

Abstract

In the spirit of recent work on contextual recognition and estimation, we present a method for estimating the pose of human hands, employing information about the shape of the object in the hand. Despite the fact that most applications of human hand tracking involve grasping and manipulation of objects, the majority of methods in the literature assume a free hand, isolated from the surrounding environment. Occlusion of the hand from grasped objects does in fact often pose a severe challenge to the estimation of hand pose. In the presented method, object occlusion is not only compensated for, it contributes to the pose estimation in a contextual fashion; this without an explicit model of object shape. Our hand tracking method is non-parametric, performing a nearest neighbor search in a large database (.. entries) of hand poses with and without grasped objects. The system that operates in real time, is robust to self occlusions, object occlusions and segmentation errors, and provides full hand pose reconstruction from monocular video. Temporal consistency in hand pose is taken into account, without explicitly tracking the hand in the high-dim pose space. Experiments show the non-parametric method to outperform other state of the art regression methods, while operating at a significantly lower computational cost than comparable model-based hand tracking methods.

Publisher site pdf link (url) [BibTex]

Romero, J., Kjellström, H., Ek, C. H., Kragic, D. Non-parametric hand pose estimation with object context Image and Vision Computing , 31(8):555 - 564, 2013 (article)

Publisher site pdf link (url) [BibTex]

2012

Virtual Human Bodies with Clothing and Hair: From Images to Animation

Guan, P.

Brown University, Department of Computer Science, December 2012 (phdthesis)

pdf [BibTex]

2012

Guan, P. Virtual Human Bodies with Clothing and Hair: From Images to Animation Brown University, Department of Computer Science, December 2012 (phdthesis)

pdf [BibTex]

Coregistration: Supplemental Material

Hirshberg, D., Loper, M., Rachlin, E., Black, M. J.

(No. 4), Max Planck Institute for Intelligent Systems, October 2012 (techreport)

pdf [BibTex]

Hirshberg, D., Loper, M., Rachlin, E., Black, M. J. Coregistration: Supplemental Material (No. 4), Max Planck Institute for Intelligent Systems, October 2012 (techreport)

pdf [BibTex]

Lie Bodies: A Manifold Representation of 3D Human Shape

Freifeld, O., Black, M. J.

In European Conf. on Computer Vision (ECCV), pages: 1-14, Part I, LNCS 7572, (Editors: A. Fitzgibbon et al. (Eds.)), Springer-Verlag, October 2012 (inproceedings)

Abstract

Three-dimensional object shape is commonly represented in terms of deformations of a triangular mesh from an exemplar shape. Existing models, however, are based on a Euclidean representation of shape deformations. In contrast, we argue that shape has a manifold structure: For example, summing the shape deformations for two people does not necessarily yield a deformation corresponding to a valid human shape, nor does the Euclidean difference of these two deformations provide a meaningful measure of shape dissimilarity. Consequently, we define a novel manifold for shape representation, with emphasis on body shapes, using a new Lie group of deformations. This has several advantages. First we define triangle deformations exactly, removing non-physical deformations and redundant degrees of freedom common to previous methods. Second, the Riemannian structure of Lie Bodies enables a more meaningful definition of body shape similarity by measuring distance between bodies on the manifold of body shape deformations. Third, the group structure allows the valid composition of deformations. This is important for models that factor body shape deformations into multiple causes or represent shape as a linear combination of basis shapes. Finally, body shape variation is modeled using statistics on manifolds. Instead of modeling Euclidean shape variation with Principal Component Analysis we capture shape variation on the manifold using Principal Geodesic Analysis. Our experiments show consistent visual and quantitative advantages of Lie Bodies over traditional Euclidean models of shape deformation and our representation can be easily incorporated into existing methods.

pdf supplemental material youtube poster eigenshape video code Project Page Project Page Project Page [BibTex]

Freifeld, O., Black, M. J. Lie Bodies: A Manifold Representation of 3D Human Shape In European Conf. on Computer Vision (ECCV), pages: 1-14, Part I, LNCS 7572, (Editors: A. Fitzgibbon et al. (Eds.)), Springer-Verlag, October 2012 (inproceedings)

pdf supplemental material youtube poster eigenshape video code Project Page Project Page Project Page [BibTex]

Coregistration: Simultaneous alignment and modeling of articulated 3D shape

Hirshberg, D., Loper, M., Rachlin, E., Black, M.

In European Conf. on Computer Vision (ECCV), pages: 242-255, LNCS 7577, Part IV, (Editors: A. Fitzgibbon et al. (Eds.)), Springer-Verlag, October 2012 (inproceedings)

Abstract

Three-dimensional (3D) shape models are powerful because they enable the inference of object shape from incomplete, noisy, or ambiguous 2D or 3D data. For example, realistic parameterized 3D human body models have been used to infer the shape and pose of people from images. To train such models, a corpus of 3D body scans is typically brought into registration by aligning a common 3D human-shaped template to each scan. This is an ill-posed problem that typically involves solving an optimization problem with regularization terms that penalize implausible deformations of the template. When aligning a corpus, however, we can do better than generic regularization. If we have a model of how the template can deform then alignments can be regularized by this model. Constructing a model of deformations, however, requires having a corpus that is already registered. We address this chicken-and-egg problem by approaching modeling and registration together. By minimizing a single objective function, we reliably obtain high quality registration of noisy, incomplete, laser scans, while simultaneously learning a highly realistic articulated body model. The model greatly improves robustness to noise and missing data. Since the model explains a corpus of body scans, it captures how body shape varies across people and poses.

pdf publisher site poster supplemental material (400MB) Project Page Project Page [BibTex]

Hirshberg, D., Loper, M., Rachlin, E., Black, M. Coregistration: Simultaneous alignment and modeling of articulated 3D shape In European Conf. on Computer Vision (ECCV), pages: 242-255, LNCS 7577, Part IV, (Editors: A. Fitzgibbon et al. (Eds.)), Springer-Verlag, October 2012 (inproceedings)

pdf publisher site poster supplemental material (400MB) Project Page Project Page [BibTex]

Lie Bodies: A Manifold Representation of 3D Human Shape. Supplemental Material

Freifeld, O., Black, M. J.

(No. 5), Max Planck Institute for Intelligent Systems, October 2012 (techreport)

pdf Project Page [BibTex]

Freifeld, O., Black, M. J. Lie Bodies: A Manifold Representation of 3D Human Shape. Supplemental Material (No. 5), Max Planck Institute for Intelligent Systems, October 2012 (techreport)

pdf Project Page [BibTex]

Coupled Action Recognition and Pose Estimation from Multiple Views

Yao, A., Gall, J., van Gool, L.

International Journal of Computer Vision, 100(1):16-37, October 2012 (article)

publisher's site code pdf Project Page Project Page Project Page [BibTex]

Yao, A., Gall, J., van Gool, L. Coupled Action Recognition and Pose Estimation from Multiple Views International Journal of Computer Vision, 100(1):16-37, October 2012 (article)

publisher's site code pdf Project Page Project Page Project Page [BibTex]

MPI-Sintel Optical Flow Benchmark: Supplemental Material

Butler, D. J., Wulff, J., Stanley, G. B., Black, M. J.

(No. 6), Max Planck Institute for Intelligent Systems, October 2012 (techreport)

pdf Project Page [BibTex]

Butler, D. J., Wulff, J., Stanley, G. B., Black, M. J. MPI-Sintel Optical Flow Benchmark: Supplemental Material (No. 6), Max Planck Institute for Intelligent Systems, October 2012 (techreport)

pdf Project Page [BibTex]

Lessons and insights from creating a synthetic optical flow benchmark

Wulff, J., Butler, D. J., Stanley, G. B., Black, M. J.

In ECCV Workshop on Unsolved Problems in Optical Flow and Stereo Estimation, pages: 168-177, Part II, LNCS 7584, (Editors: A. Fusiello et al. (Eds.)), Springer-Verlag, October 2012 (inproceedings)

pdf dataset poster youtube Project Page [BibTex]

Wulff, J., Butler, D. J., Stanley, G. B., Black, M. J. Lessons and insights from creating a synthetic optical flow benchmark In ECCV Workshop on Unsolved Problems in Optical Flow and Stereo Estimation, pages: 168-177, Part II, LNCS 7584, (Editors: A. Fusiello et al. (Eds.)), Springer-Verlag, October 2012 (inproceedings)

pdf dataset poster youtube Project Page [BibTex]

3D2PM – 3D Deformable Part Models

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

In Proceedings of the European Conference on Computer Vision (ECCV), pages: 356-370, Lecture Notes in Computer Science, (Editors: Fitzgibbon, Andrew W. and Lazebnik, Svetlana and Perona, Pietro and Sato, Yoichi and Schmid, Cordelia), Springer, Firenze, October 2012 (inproceedings)

pdf video poster Project Page [BibTex]

Pepik, B., Gehler, P., Stark, M., Schiele, B. 3D2PM – 3D Deformable Part Models In Proceedings of the European Conference on Computer Vision (ECCV), pages: 356-370, Lecture Notes in Computer Science, (Editors: Fitzgibbon, Andrew W. and Lazebnik, Svetlana and Perona, Pietro and Sato, Yoichi and Schmid, Cordelia), Springer, Firenze, October 2012 (inproceedings)

pdf video poster Project Page [BibTex]

A naturalistic open source movie for optical flow evaluation

Butler, D. J., Wulff, J., Stanley, G. B., Black, M. J.

In European Conf. on Computer Vision (ECCV), pages: 611-625, Part IV, LNCS 7577, (Editors: A. Fitzgibbon et al. (Eds.)), Springer-Verlag, October 2012 (inproceedings)

Abstract

Ground truth optical flow is difficult to measure in real scenes with natural motion. As a result, optical flow data sets are restricted in terms of size, complexity, and diversity, making optical flow algorithms difficult to train and test on realistic data. We introduce a new optical flow data set derived from the open source 3D animated short film Sintel. This data set has important features not present in the popular Middlebury flow evaluation: long sequences, large motions, specular reflections, motion blur, defocus blur, and atmospheric effects. Because the graphics data that generated the movie is open source, we are able to render scenes under conditions of varying complexity to evaluate where existing flow algorithms fail. We evaluate several recent optical flow algorithms and find that current highly-ranked methods on the Middlebury evaluation have difficulty with this more complex data set suggesting further research on optical flow estimation is needed. To validate the use of synthetic data, we compare the image- and flow-statistics of Sintel to those of real films and videos and show that they are similar. The data set, metrics, and evaluation website are publicly available.

pdf dataset youtube talk supplemental material Project Page Project Page [BibTex]

Butler, D. J., Wulff, J., Stanley, G. B., Black, M. J. A naturalistic open source movie for optical flow evaluation In European Conf. on Computer Vision (ECCV), pages: 611-625, Part IV, LNCS 7577, (Editors: A. Fitzgibbon et al. (Eds.)), Springer-Verlag, October 2012 (inproceedings)

pdf dataset youtube talk supplemental material Project Page Project Page [BibTex]

A framework for relating neural activity to freely moving behavior

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

In 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’12), pages: 2736 -2739 , IEEE, San Diego, August 2012 (inproceedings)

pdf Project Page [BibTex]

Foster, J. D., Nuyujukian, P., Freifeld, O., Ryu, S., Black, M. J., Shenoy, K. V. A framework for relating neural activity to freely moving behavior In 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’12), pages: 2736 -2739 , IEEE, San Diego, August 2012 (inproceedings)

pdf Project Page [BibTex]

Pottics – The Potts Topic Model for Semantic Image Segmentation

Dann, C., Gehler, P., Roth, S., Nowozin, S.

In Proceedings of 34th DAGM Symposium, pages: 397-407, Lecture Notes in Computer Science, (Editors: Pinz, Axel and Pock, Thomas and Bischof, Horst and Leberl, Franz), Springer, August 2012 (inproceedings)

code pdf poster [BibTex]

Dann, C., Gehler, P., Roth, S., Nowozin, S. Pottics – The Potts Topic Model for Semantic Image Segmentation In Proceedings of 34th DAGM Symposium, pages: 397-407, Lecture Notes in Computer Science, (Editors: Pinz, Axel and Pock, Thomas and Bischof, Horst and Leberl, Franz), Springer, August 2012 (inproceedings)

code pdf poster [BibTex]

Quasi-Newton Methods: A New Direction

Hennig, P., Kiefel, M.

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

Abstract

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

website+code pdf link (url) [BibTex]

Hennig, P., Kiefel, M. Quasi-Newton Methods: A New Direction In Proceedings of the 29th International Conference on Machine Learning, pages: 25-32, ICML ’12, (Editors: John Langford and Joelle Pineau), Omnipress, New York, NY, USA, July 2012 (inproceedings)

website+code pdf link (url) [BibTex]

DRAPE: DRessing Any PErson

Guan, P., Reiss, L., Hirshberg, D., Weiss, A., Black, M. J.

ACM Trans. on Graphics (Proc. SIGGRAPH), 31(4):35:1-35:10, July 2012 (article)

Abstract

We describe a complete system for animating realistic clothing on synthetic bodies of any shape and pose without manual intervention. The key component of the method is a model of clothing called DRAPE (DRessing Any PErson) that is learned from a physics-based simulation of clothing on bodies of different shapes and poses. The DRAPE model has the desirable property of "factoring" clothing deformations due to body shape from those due to pose variation. This factorization provides an approximation to the physical clothing deformation and greatly simplifies clothing synthesis. Given a parameterized model of the human body with known shape and pose parameters, we describe an algorithm that dresses the body with a garment that is customized to fit and possesses realistic wrinkles. DRAPE can be used to dress static bodies or animated sequences with a learned model of the cloth dynamics. Since the method is fully automated, it is appropriate for dressing large numbers of virtual characters of varying shape. The method is significantly more efficient than physical simulation.

YouTube pdf talk Project Page Project Page [BibTex]

Guan, P., Reiss, L., Hirshberg, D., Weiss, A., Black, M. J. DRAPE: DRessing Any PErson ACM Trans. on Graphics (Proc. SIGGRAPH), 31(4):35:1-35:10, July 2012 (article)

YouTube pdf talk Project Page Project Page [BibTex]

From Pixels to Layers: Joint Motion Estimation and Segmentation

Sun, D.

Brown University, Department of Computer Science, July 2012 (phdthesis)

pdf [BibTex]

Sun, D. From Pixels to Layers: Joint Motion Estimation and Segmentation Brown University, Department of Computer Science, July 2012 (phdthesis)

pdf [BibTex]

From pictorial structures to deformable structures

Zuffi, S., Freifeld, O., Black, M. J.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages: 3546-3553, IEEE, June 2012 (inproceedings)

Abstract

Pictorial Structures (PS) define a probabilistic model of 2D articulated objects in images. Typical PS models assume an object can be represented by a set of rigid parts connected with pairwise constraints that define the prior probability of part configurations. These models are widely used to represent non-rigid articulated objects such as humans and animals despite the fact that such objects have parts that deform non-rigidly. Here we define a new Deformable Structures (DS) model that is a natural extension of previous PS models and that captures the non-rigid shape deformation of the parts. Each part in a DS model is represented by a low-dimensional shape deformation space and pairwise potentials between parts capture how the shape varies with pose and the shape of neighboring parts. A key advantage of such a model is that it more accurately models object boundaries. This enables image likelihood models that are more discriminative than previous PS likelihoods. This likelihood is learned using training imagery annotated using a DS “puppet.” We focus on a human DS model learned from 2D projections of a realistic 3D human body model and use it to infer human poses in images using a form of non-parametric belief propagation.

pdf sup mat code poster Project Page Project Page Project Page Project Page [BibTex]

Zuffi, S., Freifeld, O., Black, M. J. From pictorial structures to deformable structures In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages: 3546-3553, IEEE, June 2012 (inproceedings)

pdf sup mat code poster Project Page Project Page Project Page Project Page [BibTex]

Teaching 3D Geometry to Deformable Part Models

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

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 3362 -3369, IEEE, Providence, RI, USA, June 2012, oral presentation (inproceedings)

pdf DOI Project Page [BibTex]

Pepik, B., Stark, M., Gehler, P., Schiele, B. Teaching 3D Geometry to Deformable Part Models In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 3362 -3369, IEEE, Providence, RI, USA, June 2012, oral presentation (inproceedings)

pdf DOI Project Page [BibTex]

Visual Orientation and Directional Selectivity Through Thalamic Synchrony

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

Journal of Neuroscience, 32(26):9073-9088, June 2012 (article)

Abstract

Thalamic neurons respond to visual scenes by generating synchronous spike trains on the timescale of 10–20 ms that are very effective at driving cortical targets. Here we demonstrate that this synchronous activity contains unexpectedly rich information about fundamental properties of visual stimuli. We report that the occurrence of synchronous firing of cat thalamic cells with highly overlapping receptive fields is strongly sensitive to the orientation and the direction of motion of the visual stimulus. We show that this stimulus selectivity is robust, remaining relatively unchanged under different contrasts and temporal frequencies (stimulus velocities). A computational analysis based on an integrate-and-fire model of the direct thalamic input to a layer 4 cortical cell reveals a strong correlation between the degree of thalamic synchrony and the nonlinear relationship between cortical membrane potential and the resultant firing rate. Together, these findings suggest a novel population code in the synchronous firing of neurons in the early visual pathway that could serve as the substrate for establishing cortical representations of the visual scene.

preprint publisher's site Project Page [BibTex]

Stanley, G., Jin, J., Wang, Y., Desbordes, G., Wang, Q., Black, M., Alonso, J. Visual Orientation and Directional Selectivity Through Thalamic Synchrony Journal of Neuroscience, 32(26):9073-9088, June 2012 (article)

preprint publisher's site Project Page [BibTex]

Real-time Facial Feature Detection using Conditional Regression Forests

Dantone, M., Gall, J., Fanelli, G., van Gool, L.

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 2578-2585, IEEE, Providence, RI, USA, 2012 (inproceedings)

code pdf Project Page [BibTex]

Dantone, M., Gall, J., Fanelli, G., van Gool, L. Real-time Facial Feature Detection using Conditional Regression Forests In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 2578-2585, IEEE, Providence, RI, USA, 2012 (inproceedings)

code pdf Project Page [BibTex]

Latent Hough Transform for Object Detection

Razavi, N., Gall, J., Kohli, P., van Gool, L.

In European Conference on Computer Vision (ECCV), 7574, pages: 312-325, LNCS, Springer, 2012 (inproceedings)

pdf Project Page [BibTex]

Razavi, N., Gall, J., Kohli, P., van Gool, L. Latent Hough Transform for Object Detection In European Conference on Computer Vision (ECCV), 7574, pages: 312-325, LNCS, Springer, 2012 (inproceedings)

pdf Project Page [BibTex]

Destination Flow for Crowd Simulation

Pellegrini, S., Gall, J., Sigal, L., van Gool, L.

In Workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Streams, 7585, pages: 162-171, LNCS, Springer, 2012 (inproceedings)

pdf Project Page [BibTex]

Pellegrini, S., Gall, J., Sigal, L., van Gool, L. Destination Flow for Crowd Simulation In Workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Streams, 7585, pages: 162-171, LNCS, Springer, 2012 (inproceedings)

pdf Project Page [BibTex]

From Deformations to Parts: Motion-based Segmentation of 3D Objects

Ghosh, S., Sudderth, E., Loper, M., Black, M.

In Advances in Neural Information Processing Systems 25 (NIPS), pages: 2006-2014, (Editors: P. Bartlett and F.C.N. Pereira and C.J.C. Burges and L. Bottou and K.Q. Weinberger), MIT Press, 2012 (inproceedings)

Abstract

We develop a method for discovering the parts of an articulated object from aligned meshes of the object in various three-dimensional poses. We adapt the distance dependent Chinese restaurant process (ddCRP) to allow nonparametric discovery of a potentially unbounded number of parts, while simultaneously guaranteeing a spatially connected segmentation. To allow analysis of datasets in which object instances have varying 3D shapes, we model part variability across poses via affine transformations. By placing a matrix normal-inverse-Wishart prior on these affine transformations, we develop a ddCRP Gibbs sampler which tractably marginalizes over transformation uncertainty. Analyzing a dataset of humans captured in dozens of poses, we infer parts which provide quantitatively better deformation predictions than conventional clustering methods.

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

Ghosh, S., Sudderth, E., Loper, M., Black, M. From Deformations to Parts: Motion-based Segmentation of 3D Objects In Advances in Neural Information Processing Systems 25 (NIPS), pages: 2006-2014, (Editors: P. Bartlett and F.C.N. Pereira and C.J.C. Burges and L. Bottou and K.Q. Weinberger), MIT Press, 2012 (inproceedings)

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