Publications | Perceiving Systems - Max Planck Institute for Intelligent Systems

124 results (View BibTeX file of all listed publications)

2016

Skinned multi-person linear model

Black, M.J., Loper, M., Mahmood, N., Pons-Moll, G., Romero, J.

December 2016, Application PCT/EP2016/064610 (misc)

Abstract

The invention comprises a learned model of human body shape and pose dependent shape variation that is more accurate than previous models and is compatible with existing graphics pipelines. Our Skinned Multi-Person Linear model (SMPL) is a skinned vertex based model that accurately represents a wide variety of body shapes in natural human poses. The parameters of the model are learned from data including the rest pose template, blend weights, pose-dependent blend shapes, identity- dependent blend shapes, and a regressor from vertices to joint locations. Unlike previous models, the pose-dependent blend shapes are a linear function of the elements of the pose rotation matrices. This simple formulation enables training the entire model from a relatively large number of aligned 3D meshes of different people in different poses. The invention quantitatively evaluates variants of SMPL using linear or dual- quaternion blend skinning and show that both are more accurate than a Blend SCAPE model trained on the same data. In a further embodiment, the invention realistically models dynamic soft-tissue deformations. Because it is based on blend skinning, SMPL is compatible with existing rendering engines and we make it available for research purposes.

Google Patents [BibTex]

2016

Black, M.J., Loper, M., Mahmood, N., Pons-Moll, G., Romero, J. Skinned multi-person linear model December 2016, Application PCT/EP2016/064610 (misc)

Google Patents [BibTex]

Creating body shapes from verbal descriptions by linking similarity spaces

Hill, M. Q., Streuber, S., Hahn, C. A., Black, M. J., O’Toole, A. J.

Psychological Science, 27(11):1486-1497, November 2016, (article)

Abstract

Brief verbal descriptions of bodies (e.g. curvy, long-legged) can elicit vivid mental images. The ease with which we create these mental images belies the complexity of three-dimensional body shapes. We explored the relationship between body shapes and body descriptions and show that a small number of words can be used to generate categorically accurate representations of three-dimensional bodies. The dimensions of body shape variation that emerged in a language-based similarity space were related to major dimensions of variation computed directly from three-dimensional laser scans of 2094 bodies. This allowed us to generate three-dimensional models of people in the shape space using only their coordinates on analogous dimensions in the language-based description space. Human descriptions of photographed bodies and their corresponding models matched closely. The natural mapping between the spaces illustrates the role of language as a concise code for body shape, capturing perceptually salient global and local body features.

pdf [BibTex]

Hill, M. Q., Streuber, S., Hahn, C. A., Black, M. J., O’Toole, A. J. Creating body shapes from verbal descriptions by linking similarity spaces Psychological Science, 27(11):1486-1497, November 2016, (article)

pdf [BibTex]

Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image

Bogo, F., Kanazawa, A., Lassner, C., Gehler, P., Romero, J., Black, M. J.

In Computer Vision – ECCV 2016, pages: 561-578, Lecture Notes in Computer Science, Springer International Publishing, October 2016 (inproceedings)

Abstract

We describe the first method to automatically estimate the 3D pose of the human body as well as its 3D shape from a single unconstrained image. We estimate a full 3D mesh and show that 2D joints alone carry a surprising amount of information about body shape. The problem is challenging because of the complexity of the human body, articulation, occlusion, clothing, lighting, and the inherent ambiguity in inferring 3D from 2D. To solve this, we first use a recently published CNN-based method, DeepCut, to predict (bottom-up) the 2D body joint locations. We then fit (top-down) a recently published statistical body shape model, called SMPL, to the 2D joints. We do so by minimizing an objective function that penalizes the error between the projected 3D model joints and detected 2D joints. Because SMPL captures correlations in human shape across the population, we are able to robustly fit it to very little data. We further leverage the 3D model to prevent solutions that cause interpenetration. We evaluate our method, SMPLify, on the Leeds Sports, HumanEva, and Human3.6M datasets, showing superior pose accuracy with respect to the state of the art.

pdf Video Sup Mat video Code Project Project Page [BibTex]

Bogo, F., Kanazawa, A., Lassner, C., Gehler, P., Romero, J., Black, M. J. Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image In Computer Vision – ECCV 2016, pages: 561-578, Lecture Notes in Computer Science, Springer International Publishing, October 2016 (inproceedings)

pdf Video Sup Mat video Code Project Project Page [BibTex]

Superpixel Convolutional Networks using Bilateral Inceptions

Gadde, R., Jampani, V., Kiefel, M., Kappler, D., Gehler, P.

In European Conference on Computer Vision (ECCV), Lecture Notes in Computer Science, Springer, October 2016 (inproceedings)

Abstract

In this paper we propose a CNN architecture for semantic image segmentation. We introduce a new “bilateral inception” module that can be inserted in existing CNN architectures and performs bilateral filtering, at multiple feature-scales, between superpixels in an image. The feature spaces for bilateral filtering and other parameters of the module are learned end-to-end using standard backpropagation techniques. The bilateral inception module addresses two issues that arise with general CNN segmentation architectures. First, this module propagates information between (super) pixels while respecting image edges, thus using the structured information of the problem for improved results. Second, the layer recovers a full resolution segmentation result from the lower resolution solution of a CNN. In the experiments, we modify several existing CNN architectures by inserting our inception modules between the last CNN (1 × 1 convolution) layers. Empirical results on three different datasets show reliable improvements not only in comparison to the baseline networks, but also in comparison to several dense-pixel prediction techniques such as CRFs, while being competitive in time.

pdf supplementary poster Project Page Project Page [BibTex]

Gadde, R., Jampani, V., Kiefel, M., Kappler, D., Gehler, P. Superpixel Convolutional Networks using Bilateral Inceptions In European Conference on Computer Vision (ECCV), Lecture Notes in Computer Science, Springer, October 2016 (inproceedings)

pdf supplementary poster Project Page Project Page [BibTex]

Barrista - Caffe Well-Served

Lassner, C., Kappler, D., Kiefel, M., Gehler, P.

In ACM Multimedia Open Source Software Competition, October 2016 (inproceedings)

Abstract

The caffe framework is one of the leading deep learning toolboxes in the machine learning and computer vision community. While it offers efficiency and configurability, it falls short of a full interface to Python. With increasingly involved procedures for training deep networks and reaching depths of hundreds of layers, creating configuration files and keeping them consistent becomes an error prone process. We introduce the barrista framework, offering full, pythonic control over caffe. It separates responsibilities and offers code to solve frequently occurring tasks for pre-processing, training and model inspection. It is compatible to all caffe versions since mid 2015 and can import and export .prototxt files. Examples are included, e.g., a deep residual network implemented in only 172 lines (for arbitrary depths), comparing to 2320 lines in the official implementation for the equivalent model.

pdf link (url) DOI Project Page [BibTex]

Lassner, C., Kappler, D., Kiefel, M., Gehler, P. Barrista - Caffe Well-Served In ACM Multimedia Open Source Software Competition, October 2016 (inproceedings)

pdf link (url) DOI Project Page [BibTex]

Non-parametric Models for Structured Data and Applications to Human Bodies and Natural Scenes

Lehrmann, A.

ETH Zurich, July 2016 (phdthesis)

Abstract

The purpose of this thesis is the study of non-parametric models for structured data and their fields of application in computer vision. We aim at the development of context-sensitive architectures which are both expressive and efficient. Our focus is on directed graphical models, in particular Bayesian networks, where we combine the flexibility of non-parametric local distributions with the efficiency of a global topology with bounded treewidth. A bound on the treewidth is obtained by either constraining the maximum indegree of the underlying graph structure or by introducing determinism. The non-parametric distributions in the nodes of the graph are given by decision trees or kernel density estimators. The information flow implied by specific network topologies, especially the resultant (conditional) independencies, allows for a natural integration and control of contextual information. We distinguish between three different types of context: static, dynamic, and semantic. In four different approaches we propose models which exhibit varying combinations of these contextual properties and allow modeling of structured data in space, time, and hierarchies derived thereof. The generative character of the presented models enables a direct synthesis of plausible hypotheses. Extensive experiments validate the developed models in two application scenarios which are of particular interest in computer vision: human bodies and natural scenes. In the practical sections of this work we discuss both areas from different angles and show applications of our models to human pose, motion, and segmentation as well as object categorization and localization. Here, we benefit from the availability of modern datasets of unprecedented size and diversity. Comparisons to traditional approaches and state-of-the-art research on the basis of well-established evaluation criteria allows the objective assessment of our contributions.

pdf [BibTex]

Lehrmann, A. Non-parametric Models for Structured Data and Applications to Human Bodies and Natural Scenes ETH Zurich, July 2016 (phdthesis)

pdf [BibTex]

Dynamic baseline stereo vision-based cooperative target tracking

Ahmad, A., Ruff, E., Bülthoff, H.

19th International Conference on Information Fusion, pages: 1728-1734, July 2016 (conference)

Abstract

In this article we present a new method for multi-robot cooperative target tracking based on dynamic baseline stereo vision. The core novelty of our approach includes a computationally light-weight scheme to compute the 3D stereo measurements that exactly satisfy the epipolar constraints and a covariance intersection (CI)-based method to fuse the 3D measurements obtained by each individual robot. Using CI we are able to systematically integrate the robot localization uncertainties as well as the uncertainties in the measurements generated by the monocular camera images from each individual robot into the resulting stereo measurements. Through an extensive set of simulation and real robot results we show the robustness and accuracy of our approach with respect to ground truth. The source code related to this article is publicly accessible on our website and the datasets are available on request.

DOI [BibTex]

Ahmad, A., Ruff, E., Bülthoff, H. Dynamic baseline stereo vision-based cooperative target tracking 19th International Conference on Information Fusion, pages: 1728-1734, July 2016 (conference)

DOI [BibTex]

Body Talk: Crowdshaping Realistic 3D Avatars with Words

Streuber, S., Quiros-Ramirez, M. A., Hill, M. Q., Hahn, C. A., Zuffi, S., O’Toole, A., Black, M. J.

ACM Trans. Graph. (Proc. SIGGRAPH), 35(4):54:1-54:14, July 2016 (article)

Abstract

Realistic, metrically accurate, 3D human avatars are useful for games, shopping, virtual reality, and health applications. Such avatars are not in wide use because solutions for creating them from high-end scanners, low-cost range cameras, and tailoring measurements all have limitations. Here we propose a simple solution and show that it is surprisingly accurate. We use crowdsourcing to generate attribute ratings of 3D body shapes corresponding to standard linguistic descriptions of 3D shape. We then learn a linear function relating these ratings to 3D human shape parameters. Given an image of a new body, we again turn to the crowd for ratings of the body shape. The collection of linguistic ratings of a photograph provides remarkably strong constraints on the metric 3D shape. We call the process crowdshaping and show that our Body Talk system produces shapes that are perceptually indistinguishable from bodies created from high-resolution scans and that the metric accuracy is sufficient for many tasks. This makes body “scanning” practical without a scanner, opening up new applications including database search, visualization, and extracting avatars from books.

pdf web tool video talk (ppt) [BibTex]

Streuber, S., Quiros-Ramirez, M. A., Hill, M. Q., Hahn, C. A., Zuffi, S., O’Toole, A., Black, M. J. Body Talk: Crowdshaping Realistic 3D Avatars with Words ACM Trans. Graph. (Proc. SIGGRAPH), 35(4):54:1-54:14, July 2016 (article)

pdf web tool video talk (ppt) [BibTex]

DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation

Pishchulin, L., Insafutdinov, E., Tang, S., Andres, B., Andriluka, M., Gehler, P., Schiele, B.

In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 4929-4937, IEEE, June 2016 (inproceedings)

Abstract

This paper considers the task of articulated human pose estimation of multiple people in real-world images. We propose an approach that jointly solves the tasks of detection and pose estimation: it infers the number of persons in a scene, identifies occluded body parts, and disambiguates body parts between people in close proximity of each other. This joint formulation is in contrast to previous strategies, that address the problem by first detecting people and subsequently estimating their body pose. We propose a partitioning and labeling formulation of a set of body-part hypotheses generated with CNN-based part detectors. Our formulation, an instance of an integer linear program, implicitly performs non-maximum suppression on the set of part candidates and groups them to form configurations of body parts respecting geometric and appearance constraints. Experiments on four different datasets demonstrate state-of-the-art results for both single person and multi person pose estimation.

code pdf supplementary DOI Project Page [BibTex]

Pishchulin, L., Insafutdinov, E., Tang, S., Andres, B., Andriluka, M., Gehler, P., Schiele, B. DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 4929-4937, IEEE, June 2016 (inproceedings)

code pdf supplementary DOI Project Page [BibTex]

Video segmentation via object flow

Tsai, Y., Yang, M., Black, M. J.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2016 (inproceedings)

Abstract

Video object segmentation is challenging due to fast moving objects, deforming shapes, and cluttered backgrounds. Optical flow can be used to propagate an object segmentation over time but, unfortunately, flow is often inaccurate, particularly around object boundaries. Such boundaries are precisely where we want our segmentation to be accurate. To obtain accurate segmentation across time, we propose an efficient algorithm that considers video segmentation and optical flow estimation simultaneously. For video segmentation, we formulate a principled, multiscale, spatio-temporal objective function that uses optical flow to propagate information between frames. For optical flow estimation, particularly at object boundaries, we compute the flow independently in the segmented regions and recompose the results. We call the process object flow and demonstrate the effectiveness of jointly optimizing optical flow and video segmentation using an iterative scheme. Experiments on the SegTrack v2 and Youtube-Objects datasets show that the proposed algorithm performs favorably against the other state-of-the-art methods.

pdf [BibTex]

Tsai, Y., Yang, M., Black, M. J. Video segmentation via object flow In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2016 (inproceedings)

pdf [BibTex]

Patches, Planes and Probabilities: A Non-local Prior for Volumetric 3D Reconstruction

Ulusoy, A. O., Black, M. J., Geiger, A.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2016 (inproceedings)

Abstract

In this paper, we propose a non-local structured prior for volumetric multi-view 3D reconstruction. Towards this goal, we present a novel Markov random field model based on ray potentials in which assumptions about large 3D surface patches such as planarity or Manhattan world constraints can be efficiently encoded as probabilistic priors. We further derive an inference algorithm that reasons jointly about voxels, pixels and image segments, and estimates marginal distributions of appearance, occupancy, depth, normals and planarity. Key to tractable inference is a novel hybrid representation that spans both voxel and pixel space and that integrates non-local information from 2D image segmentations in a principled way. We compare our non-local prior to commonly employed local smoothness assumptions and a variety of state-of-the-art volumetric reconstruction baselines on challenging outdoor scenes with textureless and reflective surfaces. Our experiments indicate that regularizing over larger distances has the potential to resolve ambiguities where local regularizers fail.

YouTube pdf poster suppmat Project Page [BibTex]

Ulusoy, A. O., Black, M. J., Geiger, A. Patches, Planes and Probabilities: A Non-local Prior for Volumetric 3D Reconstruction In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2016 (inproceedings)

YouTube pdf poster suppmat Project Page [BibTex]

Capturing Hands in Action using Discriminative Salient Points and Physics Simulation

Tzionas, D., Ballan, L., Srikantha, A., Aponte, P., Pollefeys, M., Gall, J.

International Journal of Computer Vision (IJCV), 118(2):172-193, June 2016 (article)

Abstract

Hand motion capture is a popular research field, recently gaining more attention due to the ubiquity of RGB-D sensors. However, even most recent approaches focus on the case of a single isolated hand. In this work, we focus on hands that interact with other hands or objects and present a framework that successfully captures motion in such interaction scenarios for both rigid and articulated objects. Our framework combines a generative model with discriminatively trained salient points to achieve a low tracking error and with collision detection and physics simulation to achieve physically plausible estimates even in case of occlusions and missing visual data. Since all components are unified in a single objective function which is almost everywhere differentiable, it can be optimized with standard optimization techniques. Our approach works for monocular RGB-D sequences as well as setups with multiple synchronized RGB cameras. For a qualitative and quantitative evaluation, we captured 29 sequences with a large variety of interactions and up to 150 degrees of freedom.

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

Tzionas, D., Ballan, L., Srikantha, A., Aponte, P., Pollefeys, M., Gall, J. Capturing Hands in Action using Discriminative Salient Points and Physics Simulation International Journal of Computer Vision (IJCV), 118(2):172-193, June 2016 (article)

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

Optical Flow with Semantic Segmentation and Localized Layers

Sevilla-Lara, L., Sun, D., Jampani, V., Black, M. J.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages: 3889-3898, June 2016 (inproceedings)

Abstract

Existing optical flow methods make generic, spatially homogeneous, assumptions about the spatial structure of the flow. In reality, optical flow varies across an image depending on object class. Simply put, different objects move differently. Here we exploit recent advances in static semantic scene segmentation to segment the image into objects of different types. We define different models of image motion in these regions depending on the type of object. For example, we model the motion on roads with homographies, vegetation with spatially smooth flow, and independently moving objects like cars and planes with affine motion plus deviations. We then pose the flow estimation problem using a novel formulation of localized layers, which addresses limitations of traditional layered models for dealing with complex scene motion. Our semantic flow method achieves the lowest error of any published monocular method in the KITTI-2015 flow benchmark and produces qualitatively better flow and segmentation than recent top methods on a wide range of natural videos.

video Kitti Precomputed Data (1.6GB) pdf YouTube Sequences Code Project Page Project Page [BibTex]

Sevilla-Lara, L., Sun, D., Jampani, V., Black, M. J. Optical Flow with Semantic Segmentation and Localized Layers In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages: 3889-3898, June 2016 (inproceedings)

video Kitti Precomputed Data (1.6GB) pdf YouTube Sequences Code Project Page Project Page [BibTex]

Learning Sparse High Dimensional Filters: Image Filtering, Dense CRFs and Bilateral Neural Networks

Jampani, V., Kiefel, M., Gehler, P. V.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages: 4452-4461, June 2016 (inproceedings)

Abstract

Bilateral filters have wide spread use due to their edge-preserving properties. The common use case is to manually choose a parametric filter type, usually a Gaussian filter. In this paper, we will generalize the parametrization and in particular derive a gradient descent algorithm so the filter parameters can be learned from data. This derivation allows to learn high dimensional linear filters that operate in sparsely populated feature spaces. We build on the permutohedral lattice construction for efficient filtering. The ability to learn more general forms of high-dimensional filters can be used in several diverse applications. First, we demonstrate the use in applications where single filter applications are desired for runtime reasons. Further, we show how this algorithm can be used to learn the pairwise potentials in densely connected conditional random fields and apply these to different image segmentation tasks. Finally, we introduce layers of bilateral filters in CNNs and propose bilateral neural networks for the use of high-dimensional sparse data. This view provides new ways to encode model structure into network architectures. A diverse set of experiments empirically validates the usage of general forms of filters.

project page code CVF open-access pdf supplementary poster Project Page Project Page [BibTex]

Jampani, V., Kiefel, M., Gehler, P. V. Learning Sparse High Dimensional Filters: Image Filtering, Dense CRFs and Bilateral Neural Networks In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages: 4452-4461, June 2016 (inproceedings)

project page code CVF open-access pdf supplementary poster Project Page Project Page [BibTex]

Occlusion boundary detection via deep exploration of context

Fu, H., Wang, C., Tao, D., Black, M. J.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2016 (inproceedings)

Abstract

Occlusion boundaries contain rich perceptual information about the underlying scene structure. They also provide important cues in many visual perception tasks such as scene understanding, object recognition, and segmentation. In this paper, we improve occlusion boundary detection via enhanced exploration of contextual information (e.g., local structural boundary patterns, observations from surrounding regions, and temporal context), and in doing so develop a novel approach based on convolutional neural networks (CNNs) and conditional random fields (CRFs). Experimental results demonstrate that our detector significantly outperforms the state-of-the-art (e.g., improving the F-measure from 0.62 to 0.71 on the commonly used CMU benchmark). Last but not least, we empirically assess the roles of several important components of the proposed detector, so as to validate the rationale behind this approach.

pdf [BibTex]

Fu, H., Wang, C., Tao, D., Black, M. J. Occlusion boundary detection via deep exploration of context In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2016 (inproceedings)

pdf [BibTex]

Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer

Xie, J., Kiefel, M., Sun, M., Geiger, A.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2016 (inproceedings)

Abstract

Semantic annotations are vital for training models for object recognition, semantic segmentation or scene understanding. Unfortunately, pixelwise annotation of images at very large scale is labor-intensive and only little labeled data is available, particularly at instance level and for street scenes. In this paper, we propose to tackle this problem by lifting the semantic instance labeling task from 2D into 3D. Given reconstructions from stereo or laser data, we annotate static 3D scene elements with rough bounding primitives and develop a probabilistic model which transfers this information into the image domain. We leverage our method to obtain 2D labels for a novel suburban video dataset which we have collected, resulting in 400k semantic and instance image annotations. A comparison of our method to state-of-the-art label transfer baselines reveals that 3D information enables more efficient annotation while at the same time resulting in improved accuracy and time-coherent labels.

pdf suppmat Project Page Project Page [BibTex]

Xie, J., Kiefel, M., Sun, M., Geiger, A. Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2016 (inproceedings)

pdf suppmat Project Page Project Page [BibTex]

Appealing female avatars from 3D body scans: Perceptual effects of stylization

Fleming, R., Mohler, B., Romero, J., Black, M. J., Breidt, M.

In 11th Int. Conf. on Computer Graphics Theory and Applications (GRAPP), Febuary 2016 (inproceedings)

Abstract

Advances in 3D scanning technology allow us to create realistic virtual avatars from full body 3D scan data. However, negative reactions to some realistic computer generated humans suggest that this approach might not always provide the most appealing results. Using styles derived from existing popular character designs, we present a novel automatic stylization technique for body shape and colour information based on a statistical 3D model of human bodies. We investigate whether such stylized body shapes result in increased perceived appeal with two different experiments: One focuses on body shape alone, the other investigates the additional role of surface colour and lighting. Our results consistently show that the most appealing avatar is a partially stylized one. Importantly, avatars with high stylization or no stylization at all were rated to have the least appeal. The inclusion of colour information and improvements to render quality had no significant effect on the overall perceived appeal of the avatars, and we observe that the body shape primarily drives the change in appeal ratings. For body scans with colour information, we found that a partially stylized avatar was most effective, increasing average appeal ratings by approximately 34%.

pdf Project Page [BibTex]

Fleming, R., Mohler, B., Romero, J., Black, M. J., Breidt, M. Appealing female avatars from 3D body scans: Perceptual effects of stylization In 11th Int. Conf. on Computer Graphics Theory and Applications (GRAPP), Febuary 2016 (inproceedings)

pdf Project Page [BibTex]

Human Pose Estimation from Video and IMUs

Marcard, T. V., Pons-Moll, G., Rosenhahn, B.

Transactions on Pattern Analysis and Machine Intelligence PAMI, 38(8):1533-1547, January 2016 (article)

data pdf dataset_documentation [BibTex]

Marcard, T. V., Pons-Moll, G., Rosenhahn, B. Human Pose Estimation from Video and IMUs Transactions on Pattern Analysis and Machine Intelligence PAMI, 38(8):1533-1547, January 2016 (article)

data pdf dataset_documentation [BibTex]

Deep Discrete Flow

Güney, F., Geiger, A.

Asian Conference on Computer Vision (ACCV), 2016 (conference) Accepted

pdf suppmat Project Page [BibTex]

Güney, F., Geiger, A. Deep Discrete Flow Asian Conference on Computer Vision (ACCV), 2016 (conference) Accepted

pdf suppmat Project Page [BibTex]

Moving-horizon Nonlinear Least Squares-based Multirobot Cooperative Perception

Ahmad, A., Bülthoff, H.

Robotics and Autonomous Systems, 83, pages: 275-286, 2016 (article)

Abstract

In this article we present an online estimator for multirobot cooperative localization and target tracking based on nonlinear least squares minimization. Our method not only makes the rigorous optimization-based approach applicable online but also allows the estimator to be stable and convergent. We do so by employing a moving horizon technique to nonlinear least squares minimization and a novel design of the arrival cost function that ensures stability and convergence of the estimator. Through an extensive set of real robot experiments, we demonstrate the robustness of our method as well as the optimality of the arrival cost function. The experiments include comparisons of our method with i) an extended Kalman filter-based online-estimator and ii) an offline-estimator based on full-trajectory nonlinear least squares.

DOI Project Page [BibTex]

Ahmad, A., Bülthoff, H. Moving-horizon Nonlinear Least Squares-based Multirobot Cooperative Perception Robotics and Autonomous Systems, 83, pages: 275-286, 2016 (article)

DOI Project Page [BibTex]

Perceiving Systems (2011-2015)

Scientific Advisory Board Report, 2016 (misc)

pdf [BibTex]

Perceiving Systems (2011-2015) Scientific Advisory Board Report, 2016 (misc)

pdf [BibTex]

Shape estimation of subcutaneous adipose tissue using an articulated statistical shape model

Yeo, S. Y., Romero, J., Loper, M., Machann, J., Black, M.

Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 0(0):1-8, 2016 (article)

publisher website preprint pdf link (url) DOI Project Page [BibTex]

Yeo, S. Y., Romero, J., Loper, M., Machann, J., Black, M. Shape estimation of subcutaneous adipose tissue using an articulated statistical shape model Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 0(0):1-8, 2016 (article)

publisher website preprint pdf link (url) DOI Project Page [BibTex]

Multi-Person Tracking by Multicuts and Deep Matching

(Winner of the Multi-Object Tracking Challenge ECCV 2016)

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

ECCV Workshop on Benchmarking Mutliple Object Tracking, 2016 (conference)

PDF [BibTex]

Tang, S., Andres, B., Andriluka, M., Schiele, B. Multi-Person Tracking by Multicuts and Deep Matching ECCV Workshop on Benchmarking Mutliple Object Tracking, 2016 (conference)

PDF [BibTex]

Reconstructing Articulated Rigged Models from RGB-D Videos

Tzionas, D., Gall, J.

In European Conference on Computer Vision Workshops 2016 (ECCVW’16) - Workshop on Recovering 6D Object Pose (R6D’16), pages: 620-633, Springer International Publishing, 2016 (inproceedings)

Abstract

Although commercial and open-source software exist to reconstruct a static object from a sequence recorded with an RGB-D sensor, there is a lack of tools that build rigged models of articulated objects that deform realistically and can be used for tracking or animation. In this work, we fill this gap and propose a method that creates a fully rigged model of an articulated object from depth data of a single sensor. To this end, we combine deformable mesh tracking, motion segmentation based on spectral clustering and skeletonization based on mean curvature flow. The fully rigged model then consists of a watertight mesh, embedded skeleton, and skinning weights.

pdf suppl Project's Website YouTube link (url) DOI [BibTex]

Tzionas, D., Gall, J. Reconstructing Articulated Rigged Models from RGB-D Videos In European Conference on Computer Vision Workshops 2016 (ECCVW’16) - Workshop on Recovering 6D Object Pose (R6D’16), pages: 620-633, Springer International Publishing, 2016 (inproceedings)

pdf suppl Project's Website YouTube link (url) DOI [BibTex]

A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects

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

In arXiv:1607.06317, 2016 (inproceedings)

PDF [BibTex]

Keuper, M., Tang, S., Yu, Z., Andres, B., Brox, T., Schiele, B. A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects In arXiv:1607.06317, 2016 (inproceedings)

PDF [BibTex]

The GRASP Taxonomy of Human Grasp Types

Feix, T., Romero, J., Schmiedmayer, H., Dollar, A., Kragic, D.

Human-Machine Systems, IEEE Transactions on, 46(1):66-77, 2016 (article)

publisher website pdf DOI Project Page [BibTex]

Feix, T., Romero, J., Schmiedmayer, H., Dollar, A., Kragic, D. The GRASP Taxonomy of Human Grasp Types Human-Machine Systems, IEEE Transactions on, 46(1):66-77, 2016 (article)

publisher website pdf DOI Project Page [BibTex]

Map-Based Probabilistic Visual Self-Localization

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

IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 2016 (article)

Abstract

Accurate and efficient self-localization is a critical problem for autonomous systems. This paper describes an affordable solution to vehicle self-localization which uses odometry computed from two video cameras and road maps as the sole inputs. The core of the method is a probabilistic model for which an efficient approximate inference algorithm is derived. The inference algorithm is able to utilize distributed computation in order to meet the real-time requirements of autonomous systems in some instances. Because of the probabilistic nature of the model the method is capable of coping with various sources of uncertainty including noise in the visual odometry and inherent ambiguities in the map (e.g., in a Manhattan world). By exploiting freely available, community developed maps and visual odometry measurements, the proposed method is able to localize a vehicle to 4m on average after 52 seconds of driving on maps which contain more than 2,150km of drivable roads.

pdf Project Page [BibTex]

Brubaker, M. A., Geiger, A., Urtasun, R. Map-Based Probabilistic Visual Self-Localization IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 2016 (article)

pdf Project Page [BibTex]

2010

Visibility Maps for Improving Seam Carving

Mansfield, A., Gehler, P., Van Gool, L., Rother, C.

In Media Retargeting Workshop, European Conference on Computer Vision (ECCV), september 2010 (inproceedings)

webpage pdf slides supplementary code [BibTex]

2010

Mansfield, A., Gehler, P., Van Gool, L., Rother, C. Visibility Maps for Improving Seam Carving In Media Retargeting Workshop, European Conference on Computer Vision (ECCV), september 2010 (inproceedings)

webpage pdf slides supplementary code [BibTex]

A 2D human body model dressed in eigen clothing

Guan, P., Freifeld, O., Black, M. J.

In European Conf. on Computer Vision, (ECCV), pages: 285-298, Springer-Verlag, September 2010 (inproceedings)

Abstract

Detection, tracking, segmentation and pose estimation of people in monocular images are widely studied. Two-dimensional models of the human body are extensively used, however, they are typically fairly crude, representing the body either as a rough outline or in terms of articulated geometric primitives. We describe a new 2D model of the human body contour that combines an underlying naked body with a low-dimensional clothing model. The naked body is represented as a Contour Person that can take on a wide variety of poses and body shapes. Clothing is represented as a deformation from the underlying body contour. This deformation is learned from training examples using principal component analysis to produce eigen clothing. We find that the statistics of clothing deformations are skewed and we model the a priori probability of these deformations using a Beta distribution. The resulting generative model captures realistic human forms in monocular images and is used to infer 2D body shape and pose under clothing. We also use the coefficients of the eigen clothing to recognize different categories of clothing on dressed people. The method is evaluated quantitatively on synthetic and real images and achieves better accuracy than previous methods for estimating body shape under clothing.

pdf data poster Project Page [BibTex]

Guan, P., Freifeld, O., Black, M. J. A 2D human body model dressed in eigen clothing In European Conf. on Computer Vision, (ECCV), pages: 285-298, Springer-Verlag, September 2010 (inproceedings)

pdf data poster Project Page [BibTex]

Analyzing and Evaluating Markerless Motion Tracking Using Inertial Sensors

Baak, A., Helten, T., Müller, M., Pons-Moll, G., Rosenhahn, B., Seidel, H.

In European Conference on Computer Vision (ECCV Workshops), September 2010 (inproceedings)

pdf [BibTex]

Baak, A., Helten, T., Müller, M., Pons-Moll, G., Rosenhahn, B., Seidel, H. Analyzing and Evaluating Markerless Motion Tracking Using Inertial Sensors In European Conference on Computer Vision (ECCV Workshops), September 2010 (inproceedings)

pdf [BibTex]

Trainable, Vision-Based Automated Home Cage Behavioral Phenotyping

Jhuang, H., Garrote, E., Edelman, N., Poggio, T., Steele, A., Serre, T.

In Measuring Behavior, August 2010 (inproceedings)

pdf [BibTex]

Jhuang, H., Garrote, E., Edelman, N., Poggio, T., Steele, A., Serre, T. Trainable, Vision-Based Automated Home Cage Behavioral Phenotyping In Measuring Behavior, August 2010 (inproceedings)

pdf [BibTex]

Decoding complete reach and grasp actions from local primary motor cortex populations

(Featured in Nature’s Research Highlights (Nature, Vol 466, 29 July 2010))

Vargas-Irwin, C. E., Shakhnarovich, G., Yadollahpour, P., Mislow, J., Black, M. J., Donoghue, J. P.

J. of Neuroscience, 39(29):9659-9669, July 2010 (article)

pdf pdf from publisher Movie 1 Movie 2 Project Page [BibTex]

Vargas-Irwin, C. E., Shakhnarovich, G., Yadollahpour, P., Mislow, J., Black, M. J., Donoghue, J. P. Decoding complete reach and grasp actions from local primary motor cortex populations J. of Neuroscience, 39(29):9659-9669, July 2010 (article)

pdf pdf from publisher Movie 1 Movie 2 Project Page [BibTex]

Multisensor-Fusion for 3D Full-Body Human Motion Capture

Pons-Moll, G., Baak, A., Helten, T., Müller, M., Seidel, H., Rosenhahn, B.

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2010 (inproceedings)

project page pdf [BibTex]

Pons-Moll, G., Baak, A., Helten, T., Müller, M., Seidel, H., Rosenhahn, B. Multisensor-Fusion for 3D Full-Body Human Motion Capture In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2010 (inproceedings)

project page pdf [BibTex]

Coded exposure imaging for projective motion deblurring

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

In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 2408-2415, June 2010 (inproceedings)

Abstract

We propose a method for deblurring of spatially variant object motion. A principal challenge of this problem is how to estimate the point spread function (PSF) of the spatially variant blur. Based on the projective motion blur model of, we present a blur estimation technique that jointly utilizes a coded exposure camera and simple user interactions to recover the PSF. With this spatially variant PSF, objects that exhibit projective motion can be effectively de-blurred. We validate this method with several challenging image examples.

Publisher site [BibTex]

Tai, Y., Kong, N., Lin, S., Shin, S. Y. Coded exposure imaging for projective motion deblurring In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 2408-2415, June 2010 (inproceedings)

Publisher site [BibTex]

Tracking people interacting with objects

Kjellstrom, H., Kragic, D., Black, M. J.

In IEEE Conf. on Computer Vision and Pattern Recognition, CVPR, pages: 747-754, June 2010 (inproceedings)

pdf Video [BibTex]

Kjellstrom, H., Kragic, D., Black, M. J. Tracking people interacting with objects In IEEE Conf. on Computer Vision and Pattern Recognition, CVPR, pages: 747-754, June 2010 (inproceedings)

pdf Video [BibTex]

Contour people: A parameterized model of 2D articulated human shape

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

In IEEE Conf. on Computer Vision and Pattern Recognition, (CVPR), pages: 639-646, IEEE, June 2010 (inproceedings)

pdf slides video of CVPR talk Project Page [BibTex]

Freifeld, O., Weiss, A., Zuffi, S., Black, M. J. Contour people: A parameterized model of 2D articulated human shape In IEEE Conf. on Computer Vision and Pattern Recognition, (CVPR), pages: 639-646, IEEE, June 2010 (inproceedings)

pdf slides video of CVPR talk Project Page [BibTex]

Secrets of optical flow estimation and their principles

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

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages: 2432-2439, IEEE, June 2010 (inproceedings)

pdf Matlab code code copryright notice [BibTex]

Sun, D., Roth, S., Black, M. J. Secrets of optical flow estimation and their principles In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages: 2432-2439, IEEE, June 2010 (inproceedings)

pdf Matlab code code copryright notice [BibTex]

Guest editorial: State of the art in image- and video-based human pose and motion estimation

Sigal, L., Black, M. J.

International Journal of Computer Vision, 87(1):1-3, March 2010 (article)

pdf from publisher [BibTex]

Sigal, L., Black, M. J. Guest editorial: State of the art in image- and video-based human pose and motion estimation International Journal of Computer Vision, 87(1):1-3, March 2010 (article)

pdf from publisher [BibTex]

HumanEva: Synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion

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

International Journal of Computer Vision, 87(1):4-27, Springer Netherlands, March 2010 (article)

Abstract

While research on articulated human motion and pose estimation has progressed rapidly in the last few years, there has been no systematic quantitative evaluation of competing methods to establish the current state of the art. We present data obtained using a hardware system that is able to capture synchronized video and ground-truth 3D motion. The resulting HumanEva datasets contain multiple subjects performing a set of predefined actions with a number of repetitions. On the order of 40,000 frames of synchronized motion capture and multi-view video (resulting in over one quarter million image frames in total) were collected at 60 Hz with an additional 37,000 time instants of pure motion capture data. A standard set of error measures is defined for evaluating both 2D and 3D pose estimation and tracking algorithms. We also describe a baseline algorithm for 3D articulated tracking that uses a relatively standard Bayesian framework with optimization in the form of Sequential Importance Resampling and Annealed Particle Filtering. In the context of this baseline algorithm we explore a variety of likelihood functions, prior models of human motion and the effects of algorithm parameters. Our experiments suggest that image observation models and motion priors play important roles in performance, and that in a multi-view laboratory environment, where initialization is available, Bayesian filtering tends to perform well. The datasets and the software are made available to the research community. This infrastructure will support the development of new articulated motion and pose estimation algorithms, will provide a baseline for the evaluation and comparison of new methods, and will help establish the current state of the art in human pose estimation and tracking.

pdf pdf from publisher [BibTex]

Sigal, L., Balan, A., Black, M. J. HumanEva: Synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion International Journal of Computer Vision, 87(1):4-27, Springer Netherlands, March 2010 (article)

pdf pdf from publisher [BibTex]

Modellbasierte Echtzeit-Bewegungsschätzung in der Fluoreszenzendoskopie

Stehle, T., Wulff, J., Behrens, A., Gross, S., Aach, T.

In Bildverarbeitung für die Medizin, 574, pages: 435-439, CEUR Workshop Proceedings, 2010 (inproceedings)

pdf [BibTex]

Stehle, T., Wulff, J., Behrens, A., Gross, S., Aach, T. Modellbasierte Echtzeit-Bewegungsschätzung in der Fluoreszenzendoskopie In Bildverarbeitung für die Medizin, 574, pages: 435-439, CEUR Workshop Proceedings, 2010 (inproceedings)

pdf [BibTex]

Robust one-shot 3D scanning using loopy belief propagation

Ulusoy, A., Calakli, F., Taubin, G.

In Computer Vision and Pattern Recognition Workshops (CVPRW), 2010 IEEE Computer Society Conference on, pages: 15-22, IEEE, 2010 (inproceedings)

Abstract

A structured-light technique can greatly simplify the problem of shape recovery from images. There are currently two main research challenges in design of such techniques. One is handling complicated scenes involving texture, occlusions, shadows, sharp discontinuities, and in some cases even dynamic change; and the other is speeding up the acquisition process by requiring small number of images and computationally less demanding algorithms. This paper presents a “one-shot” variant of such techniques to tackle the aforementioned challenges. It works by projecting a static grid pattern onto the scene and identifying the correspondence between grid stripes and the camera image. The correspondence problem is formulated using a novel graphical model and solved efficiently using loopy belief propagation. Unlike prior approaches, the proposed approach uses non-deterministic geometric constraints, thereby can handle spurious connections of stripe images. The effectiveness of the proposed approach is verified on a variety of complicated real scenes.

pdf link (url) DOI [BibTex]

Ulusoy, A., Calakli, F., Taubin, G. Robust one-shot 3D scanning using loopy belief propagation In Computer Vision and Pattern Recognition Workshops (CVPRW), 2010 IEEE Computer Society Conference on, pages: 15-22, IEEE, 2010 (inproceedings)

pdf link (url) DOI [BibTex]

Scene Carving: Scene Consistent Image Retargeting

Mansfield, A., Gehler, P., Van Gool, L., Rother, C.

In European Conference on Computer Vision (ECCV), 2010 (inproceedings)

webpage+code pdf supplementary poster [BibTex]

Mansfield, A., Gehler, P., Van Gool, L., Rother, C. Scene Carving: Scene Consistent Image Retargeting In European Conference on Computer Vision (ECCV), 2010 (inproceedings)

webpage+code pdf supplementary poster [BibTex]

Epione: An Innovative Pain Management System Using Facial Expression Analysis, Biofeedback and Augmented Reality-Based Distraction

Georgoulis, S., Eleftheriadis, S., Tzionas, D., Vrenas, K., Petrantonakis, P., Hadjileontiadis, L. J.

In Proceedings of the 2010 International Conference on Intelligent Networking and Collaborative Systems, pages: 259-266, INCOS ’10, IEEE Computer Society, Washington, DC, USA, 2010 (inproceedings)

Abstract

An innovative pain management system, namely Epione, is presented here. Epione deals with three main types of pain, i.e., acute pain, chronic pain, and phantom limb pain. In particular, by using facial expression analysis, Epione forms a dynamic pain meter, which then triggers biofeedback and augmented reality-based destruction scenarios, in an effort to maximize patient's pain relief. This unique combination sets Epione not only a novel pain management approach, but also a means that provides an understanding and integration of the needs of the whole community involved i.e., patients and physicians, in a joint attempt to facilitate easing of their suffering, provide efficient monitoring and contribute to a better quality of life.

Paper Project Page DOI [BibTex]

Georgoulis, S., Eleftheriadis, S., Tzionas, D., Vrenas, K., Petrantonakis, P., Hadjileontiadis, L. J. Epione: An Innovative Pain Management System Using Facial Expression Analysis, Biofeedback and Augmented Reality-Based Distraction In Proceedings of the 2010 International Conference on Intelligent Networking and Collaborative Systems, pages: 259-266, INCOS ’10, IEEE Computer Society, Washington, DC, USA, 2010 (inproceedings)

Paper Project Page DOI [BibTex]

Phantom Limb Pain Management Using Facial Expression Analysis, Biofeedback and Augmented Reality Interfacing

Tzionas, D., Vrenas, K., Eleftheriadis, S., Georgoulis, S., Petrantonakis, P. C., Hadjileontiadis, L. J.

In Proceedings of the 3rd International Conferenceon Software Development for EnhancingAccessibility and Fighting Info-Exclusion, pages: 23-30, DSAI ’10, UTAD - Universidade de Trás-os-Montes e Alto Douro, 2010 (inproceedings)

Abstract

Post-amputation sensation often translates to the feeling of severe pain in the missing limb, referred to as phantom limb pain (PLP). A clear and rational treatment regimen is difficult to establish, as long as the underlying pathophysiology is not fully known. In this work, an innovative PLP management system is presented, as a module of an holistic computer-mediated pain management environment, namely Epione. The proposed Epione-PLP scheme is structured upon advanced facial expression analysis, used to form a dynamic pain meter, which, in turn, is used to trigger biofeedback and augmented reality-based PLP distraction scenarios. The latter incorporate a model of the missing limb for its visualization, in an effort to provide to the amputee the feeling of its existence and control, and, thus, maximize his/her PLP relief. The novel Epione-PLP management approach integrates edge-technology within the context of personalized health and it could be used to facilitate easing of PLP patients' suffering, provide efficient progress monitoring and contribute to the increase in their quality of life.

Paper Project Page link (url) [BibTex]

Tzionas, D., Vrenas, K., Eleftheriadis, S., Georgoulis, S., Petrantonakis, P. C., Hadjileontiadis, L. J. Phantom Limb Pain Management Using Facial Expression Analysis, Biofeedback and Augmented Reality Interfacing In Proceedings of the 3rd International Conferenceon Software Development for EnhancingAccessibility and Fighting Info-Exclusion, pages: 23-30, DSAI ’10, UTAD - Universidade de Trás-os-Montes e Alto Douro, 2010 (inproceedings)

Paper Project Page link (url) [BibTex]

Automated Home-Cage Behavioral Phenotyping of Mice

Jhuang, H., Garrote, E., Mutch, J., Poggio, T., Steele, A., Serre, T.

Nature Communications, Nature Communications, 2010 (article)

software, demo pdf [BibTex]

Jhuang, H., Garrote, E., Mutch, J., Poggio, T., Steele, A., Serre, T. Automated Home-Cage Behavioral Phenotyping of Mice Nature Communications, Nature Communications, 2010 (article)

software, demo pdf [BibTex]

An automated action initiation system reveals behavioral deficits in MyosinVa deficient mice

Pandian, S., Edelman, N., Jhuang, H., Serre, T., Poggio, T., Constantine-Paton, M.

Society for Neuroscience, 2010 (conference)

pdf [BibTex]

Pandian, S., Edelman, N., Jhuang, H., Serre, T., Poggio, T., Constantine-Paton, M. An automated action initiation system reveals behavioral deficits in MyosinVa deficient mice Society for Neuroscience, 2010 (conference)

pdf [BibTex]

Dense Marker-less Three Dimensional Motion Capture

Soren Hauberg, Bente Rona Jensen, Morten Engell-Norregaard, Kenny Erleben, Kim S. Pedersen

In Virtual Vistas; Eleventh International Symposium on the 3D Analysis of Human Movement, 2010 (inproceedings)

Conference site [BibTex]

Soren Hauberg, Bente Rona Jensen, Morten Engell-Norregaard, Kenny Erleben, Kim S. Pedersen Dense Marker-less Three Dimensional Motion Capture In Virtual Vistas; Eleventh International Symposium on the 3D Analysis of Human Movement, 2010 (inproceedings)

Conference site [BibTex]

ImageFlow: Streaming Image Search

Jampani, V., Ramos, G., Drucker, S.

MSR-TR-2010-148, Microsoft Research, Redmond, 2010 (techreport)

Abstract

Traditional grid and list representations of image search results are the dominant interaction paradigms that users face on a daily basis, yet it is unclear that such paradigms are well-suited for experiences where the user‟s task is to browse images for leisure, to discover new information or to seek particular images to represent ideas. We introduce ImageFlow, a novel image search user interface that ex-plores a different alternative to the traditional presentation of image search results. ImageFlow presents image results on a canvas where we map semantic features (e.g., rele-vance, related queries) to the canvas‟ spatial dimensions (e.g., x, y, z) in a way that allows for several levels of en-gagement – from passively viewing a stream of images, to seamlessly navigating through the semantic space and ac-tively collecting images for sharing and reuse. We have implemented our system as a fully functioning prototype, and we report on promising, preliminary usage results.

url pdf link (url) [BibTex]

Jampani, V., Ramos, G., Drucker, S. ImageFlow: Streaming Image Search MSR-TR-2010-148, Microsoft Research, Redmond, 2010 (techreport)

url pdf link (url) [BibTex]

On Parameter Learning in CRF-based Approaches to Object Class Image Segmentation

Nowozin, S., Gehler, P., Lampert, C.

In European Conference on Computer Vision (ECCV), 2010 (inproceedings)

pdf supplementary [BibTex]

Nowozin, S., Gehler, P., Lampert, C. On Parameter Learning in CRF-based Approaches to Object Class Image Segmentation In European Conference on Computer Vision (ECCV), 2010 (inproceedings)

pdf supplementary [BibTex]

Stick It! Articulated Tracking using Spatial Rigid Object Priors

Soren Hauberg, Kim S. Pedersen

In Computer Vision – ACCV 2010, 6494, pages: 758-769, Lecture Notes in Computer Science, (Editors: Kimmel, Ron and Klette, Reinhard and Sugimoto, Akihiro), Springer Berlin Heidelberg, 2010 (inproceedings)

Publishers site Paper site Code PDF [BibTex]

Soren Hauberg, Kim S. Pedersen Stick It! Articulated Tracking using Spatial Rigid Object Priors In Computer Vision – ACCV 2010, 6494, pages: 758-769, Lecture Notes in Computer Science, (Editors: Kimmel, Ron and Klette, Reinhard and Sugimoto, Akihiro), Springer Berlin Heidelberg, 2010 (inproceedings)

Publishers site Paper site Code PDF [BibTex]

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