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2017


OctNet: Learning Deep 3D Representations at High Resolutions
OctNet: Learning Deep 3D Representations at High Resolutions

Riegler, G., Ulusoy, O., Geiger, A.

In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, IEEE, Piscataway, NJ, USA, July 2017 (inproceedings)

Abstract
We present OctNet, a representation for deep learning with sparse 3D data. In contrast to existing models, our representation enables 3D convolutional networks which are both deep and high resolution. Towards this goal, we exploit the sparsity in the input data to hierarchically partition the space using a set of unbalanced octrees where each leaf node stores a pooled feature representation. This allows to focus memory allocation and computation to the relevant dense regions and enables deeper networks without compromising resolution. We demonstrate the utility of our OctNet representation by analyzing the impact of resolution on several 3D tasks including 3D object classification, orientation estimation and point cloud labeling.

pdf suppmat Project Page Video Project Page [BibTex]

2017

pdf suppmat Project Page Video Project Page [BibTex]


Reflectance Adaptive Filtering Improves Intrinsic Image Estimation
Reflectance Adaptive Filtering Improves Intrinsic Image Estimation

Nestmeyer, T., Gehler, P. V.

In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 1771-1780, IEEE, Piscataway, NJ, USA, July 2017 (inproceedings)

pre-print DOI Project Page Project Page [BibTex]

pre-print DOI Project Page Project Page [BibTex]


Detailed, accurate, human shape estimation from clothed {3D} scan sequences
Detailed, accurate, human shape estimation from clothed 3D scan sequences

Zhang, C., Pujades, S., Black, M., Pons-Moll, G.

In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society, Washington, DC, USA, July 2017, Spotlight (inproceedings)

Abstract
We address the problem of estimating human body shape from 3D scans over time. Reliable estimation of 3D body shape is necessary for many applications including virtual try-on, health monitoring, and avatar creation for virtual reality. Scanning bodies in minimal clothing, however, presents a practical barrier to these applications. We address this problem by estimating body shape under clothing from a sequence of 3D scans. Previous methods that have exploited statistical models of body shape produce overly smooth shapes lacking personalized details. In this paper we contribute a new approach to recover not only an approximate shape of the person, but also their detailed shape. Our approach allows the estimated shape to deviate from a parametric model to fit the 3D scans. We demonstrate the method using high quality 4D data as well as sequences of visual hulls extracted from multi-view images. We also make available a new high quality 4D dataset that enables quantitative evaluation. Our method outperforms the previous state of the art, both qualitatively and quantitatively.

arxiv_preprint video dataset pdf supplemental DOI Project Page [BibTex]

arxiv_preprint video dataset pdf supplemental DOI Project Page [BibTex]


{3D} Menagerie: Modeling the {3D} Shape and Pose of Animals
3D Menagerie: Modeling the 3D Shape and Pose of Animals

Zuffi, S., Kanazawa, A., Jacobs, D., Black, M. J.

In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, pages: 5524-5532, IEEE, Piscataway, NJ, USA, July 2017 (inproceedings)

Abstract
There has been significant work on learning realistic, articulated, 3D models of the human body. In contrast, there are few such models of animals, despite many applications. The main challenge is that animals are much less cooperative than humans. The best human body models are learned from thousands of 3D scans of people in specific poses, which is infeasible with live animals. Consequently, we learn our model from a small set of 3D scans of toy figurines in arbitrary poses. We employ a novel part-based shape model to compute an initial registration to the scans. We then normalize their pose, learn a statistical shape model, and refine the registrations and the model together. In this way, we accurately align animal scans from different quadruped families with very different shapes and poses. With the registration to a common template we learn a shape space representing animals including lions, cats, dogs, horses, cows and hippos. Animal shapes can be sampled from the model, posed, animated, and fit to data. We demonstrate generalization by fitting it to images of real animals including species not seen in training.

pdf video Project Page [BibTex]

pdf video Project Page [BibTex]


Optical Flow Estimation using a Spatial Pyramid Network
Optical Flow Estimation using a Spatial Pyramid Network

Ranjan, A., Black, M.

In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, IEEE, Piscataway, NJ, USA, July 2017 (inproceedings)

Abstract
We learn to compute optical flow by combining a classical spatial-pyramid formulation with deep learning. This estimates large motions in a coarse-to-fine approach by warping one image of a pair at each pyramid level by the current flow estimate and computing an update to the flow. Instead of the standard minimization of an objective function at each pyramid level, we train one deep network per level to compute the flow update. Unlike the recent FlowNet approach, the networks do not need to deal with large motions; these are dealt with by the pyramid. This has several advantages. First, our Spatial Pyramid Network (SPyNet) is much simpler and 96% smaller than FlowNet in terms of model parameters. This makes it more efficient and appropriate for embedded applications. Second, since the flow at each pyramid level is small (< 1 pixel), a convolutional approach applied to pairs of warped images is appropriate. Third, unlike FlowNet, the learned convolution filters appear similar to classical spatio-temporal filters, giving insight into the method and how to improve it. Our results are more accurate than FlowNet on most standard benchmarks, suggesting a new direction of combining classical flow methods with deep learning.

pdf SupMat project/code [BibTex]

pdf SupMat project/code [BibTex]


Multiple People Tracking by Lifted Multicut and Person Re-identification
Multiple People Tracking by Lifted Multicut and Person Re-identification

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

In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 3701-3710, IEEE Computer Society, Washington, DC, USA, July 2017 (inproceedings)

DOI Project Page [BibTex]

DOI Project Page [BibTex]


Video Propagation Networks
Video Propagation Networks

Jampani, V., Gadde, R., Gehler, P. V.

In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, IEEE, Piscataway, NJ, USA, July 2017 (inproceedings)

pdf supplementary arXiv project page code Project Page [BibTex]

pdf supplementary arXiv project page code Project Page [BibTex]


Generating Descriptions with Grounded and Co-Referenced People
Generating Descriptions with Grounded and Co-Referenced People

Rohrbach, A., Rohrbach, M., Tang, S., Oh, S. J., Schiele, B.

In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 4196-4206, IEEE, Piscataway, NJ, USA, July 2017 (inproceedings)

PDF DOI Project Page [BibTex]

PDF DOI Project Page [BibTex]


Semantic Multi-view Stereo: Jointly Estimating Objects and Voxels
Semantic Multi-view Stereo: Jointly Estimating Objects and Voxels

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

In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, IEEE, Piscataway, NJ, USA, July 2017 (inproceedings)

Abstract
Dense 3D reconstruction from RGB images is a highly ill-posed problem due to occlusions, textureless or reflective surfaces, as well as other challenges. We propose object-level shape priors to address these ambiguities. Towards this goal, we formulate a probabilistic model that integrates multi-view image evidence with 3D shape information from multiple objects. Inference in this model yields a dense 3D reconstruction of the scene as well as the existence and precise 3D pose of the objects in it. Our approach is able to recover fine details not captured in the input shapes while defaulting to the input models in occluded regions where image evidence is weak. Due to its probabilistic nature, the approach is able to cope with the approximate geometry of the 3D models as well as input shapes that are not present in the scene. We evaluate the approach quantitatively on several challenging indoor and outdoor datasets.

YouTube pdf suppmat Project Page [BibTex]

YouTube pdf suppmat Project Page [BibTex]


Deep representation learning for human motion prediction and classification
Deep representation learning for human motion prediction and classification

Bütepage, J., Black, M., Kragic, D., Kjellström, H.

In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, IEEE, Piscataway, NJ, USA, July 2017 (inproceedings)

Abstract
Generative models of 3D human motion are often restricted to a small number of activities and can therefore not generalize well to novel movements or applications. In this work we propose a deep learning framework for human motion capture data that learns a generic representation from a large corpus of motion capture data and generalizes well to new, unseen, motions. Using an encoding-decoding network that learns to predict future 3D poses from the most recent past, we extract a feature representation of human motion. Most work on deep learning for sequence prediction focuses on video and speech. Since skeletal data has a different structure, we present and evaluate different network architectures that make different assumptions about time dependencies and limb correlations. To quantify the learned features, we use the output of different layers for action classification and visualize the receptive fields of the network units. Our method outperforms the recent state of the art in skeletal motion prediction even though these use action specific training data. Our results show that deep feedforward networks, trained from a generic mocap database, can successfully be used for feature extraction from human motion data and that this representation can be used as a foundation for classification and prediction.

arXiv Project Page [BibTex]

arXiv Project Page [BibTex]


Unite the People: Closing the Loop Between 3D and 2D Human Representations
Unite the People: Closing the Loop Between 3D and 2D Human Representations

Lassner, C., Romero, J., Kiefel, M., Bogo, F., Black, M. J., Gehler, P. V.

In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, IEEE, Piscataway, NJ, USA, July 2017 (inproceedings)

Abstract
3D models provide a common ground for different representations of human bodies. In turn, robust 2D estimation has proven to be a powerful tool to obtain 3D fits “in-the-wild”. However, depending on the level of detail, it can be hard to impossible to acquire labeled data for training 2D estimators on large scale. We propose a hybrid approach to this problem: with an extended version of the recently introduced SMPLify method, we obtain high quality 3D body model fits for multiple human pose datasets. Human annotators solely sort good and bad fits. This procedure leads to an initial dataset, UP-3D, with rich annotations. With a comprehensive set of experiments, we show how this data can be used to train discriminative models that produce results with an unprecedented level of detail: our models predict 31 segments and 91 landmark locations on the body. Using the 91 landmark pose estimator, we present state-of-the art results for 3D human pose and shape estimation using an order of magnitude less training data and without assumptions about gender or pose in the fitting procedure. We show that UP-3D can be enhanced with these improved fits to grow in quantity and quality, which makes the system deployable on large scale. The data, code and models are available for research purposes.

arXiv project/code/data Project Page [BibTex]

arXiv project/code/data Project Page [BibTex]


Human Shape Estimation using Statistical Body Models
Human Shape Estimation using Statistical Body Models

Loper, M. M.

University of Tübingen, May 2017 (thesis)

Abstract
Human body estimation methods transform real-world observations into predictions about human body state. These estimation methods benefit a variety of health, entertainment, clothing, and ergonomics applications. State may include pose, overall body shape, and appearance. Body state estimation is underconstrained by observations; ambiguity presents itself both in the form of missing data within observations, and also in the form of unknown correspondences between observations. We address this challenge with the use of a statistical body model: a data-driven virtual human. This helps resolve ambiguity in two ways. First, it fills in missing data, meaning that incomplete observations still result in complete shape estimates. Second, the model provides a statistically-motivated penalty for unlikely states, which enables more plausible body shape estimates. Body state inference requires more than a body model; we therefore build obser- vation models whose output is compared with real observations. In this thesis, body state is estimated from three types of observations: 3D motion capture markers, depth and color images, and high-resolution 3D scans. In each case, a forward process is proposed which simulates observations. By comparing observations to the results of the forward process, state can be adjusted to minimize the difference between simulated and observed data. We use gradient-based methods because they are critical to the precise estimation of state with a large number of parameters. The contributions of this work include three parts. First, we propose a method for the estimation of body shape, nonrigid deformation, and pose from 3D markers. Second, we present a concise approach to differentiating through the rendering process, with application to body shape estimation. And finally, we present a statistical body model trained from human body scans, with state-of-the-art fidelity, good runtime performance, and compatibility with existing animation packages.

Official Version [BibTex]


Learning Inference Models for Computer Vision
Learning Inference Models for Computer Vision

Jampani, V.

MPI for Intelligent Systems and University of Tübingen, 2017 (phdthesis)

Abstract
Computer vision can be understood as the ability to perform 'inference' on image data. Breakthroughs in computer vision technology are often marked by advances in inference techniques, as even the model design is often dictated by the complexity of inference in them. This thesis proposes learning based inference schemes and demonstrates applications in computer vision. We propose techniques for inference in both generative and discriminative computer vision models. Despite their intuitive appeal, the use of generative models in vision is hampered by the difficulty of posterior inference, which is often too complex or too slow to be practical. We propose techniques for improving inference in two widely used techniques: Markov Chain Monte Carlo (MCMC) sampling and message-passing inference. Our inference strategy is to learn separate discriminative models that assist Bayesian inference in a generative model. Experiments on a range of generative vision models show that the proposed techniques accelerate the inference process and/or converge to better solutions. A main complication in the design of discriminative models is the inclusion of prior knowledge in a principled way. For better inference in discriminative models, we propose techniques that modify the original model itself, as inference is simple evaluation of the model. We concentrate on convolutional neural network (CNN) models and propose a generalization of standard spatial convolutions, which are the basic building blocks of CNN architectures, to bilateral convolutions. First, we generalize the existing use of bilateral filters and then propose new neural network architectures with learnable bilateral filters, which we call `Bilateral Neural Networks'. We show how the bilateral filtering modules can be used for modifying existing CNN architectures for better image segmentation and propose a neural network approach for temporal information propagation in videos. Experiments demonstrate the potential of the proposed bilateral networks on a wide range of vision tasks and datasets. In summary, we propose learning based techniques for better inference in several computer vision models ranging from inverse graphics to freely parameterized neural networks. In generative vision models, our inference techniques alleviate some of the crucial hurdles in Bayesian posterior inference, paving new ways for the use of model based machine learning in vision. In discriminative CNN models, the proposed filter generalizations aid in the design of new neural network architectures that can handle sparse high-dimensional data as well as provide a way for incorporating prior knowledge into CNNs.

pdf [BibTex]

pdf [BibTex]


Towards Accurate Marker-less Human Shape and Pose Estimation over Time
Towards Accurate Marker-less Human Shape and Pose Estimation over Time

Huang, Y., Bogo, F., Lassner, C., Kanazawa, A., Gehler, P. V., Romero, J., Akhter, I., Black, M. J.

In International Conference on 3D Vision (3DV), pages: 421-430, 2017 (inproceedings)

Abstract
Existing markerless motion capture methods often assume known backgrounds, static cameras, and sequence specific motion priors, limiting their application scenarios. Here we present a fully automatic method that, given multiview videos, estimates 3D human pose and body shape. We take the recently proposed SMPLify method [12] as the base method and extend it in several ways. First we fit a 3D human body model to 2D features detected in multi-view images. Second, we use a CNN method to segment the person in each image and fit the 3D body model to the contours, further improving accuracy. Third we utilize a generic and robust DCT temporal prior to handle the left and right side swapping issue sometimes introduced by the 2D pose estimator. Validation on standard benchmarks shows our results are comparable to the state of the art and also provide a realistic 3D shape avatar. We also demonstrate accurate results on HumanEva and on challenging monocular sequences of dancing from YouTube.

Code pdf DOI Project Page [BibTex]


Capturing Hand-Object Interaction and Reconstruction of Manipulated Objects
Capturing Hand-Object Interaction and Reconstruction of Manipulated Objects

Tzionas, D.

University of Bonn, 2017 (phdthesis)

Abstract
Hand motion capture with an RGB-D sensor gained recently a lot of research attention, however, even most recent approaches focus on the case of a single isolated hand. We focus instead on hands that interact with other hands or with a rigid or articulated object. Our framework successfully captures motion in such scenarios by combining a generative model with discriminatively trained salient points, collision detection and physics simulation to achieve a low tracking error with physically plausible poses. All components are unified in a single objective function that can be optimized with standard optimization techniques. We initially assume a-priori knowledge of the object's shape and skeleton. In case of unknown object shape there are existing 3d reconstruction methods that capitalize on distinctive geometric or texture features. These methods though fail for textureless and highly symmetric objects like household articles, mechanical parts or toys. We show that extracting 3d hand motion for in-hand scanning effectively facilitates the reconstruction of such objects and we fuse the rich additional information of hands into a 3d reconstruction pipeline. Finally, although shape reconstruction is enough for rigid objects, there is a lack of tools that build rigged models of articulated objects that deform realistically using RGB-D data. We propose a method that creates a fully rigged model consisting of a watertight mesh, embedded skeleton and skinning weights by employing a combination of deformable mesh tracking, motion segmentation based on spectral clustering and skeletonization based on mean curvature flow.

Thesis link (url) Project Page [BibTex]

2006


no image
Finding directional movement representations in motor cortical neural populations using nonlinear manifold learning

WorKim, S., Simeral, J., Jenkins, O., Donoghue, J., Black, M.

World Congress on Medical Physics and Biomedical Engineering 2006, Seoul, Korea, August 2006 (conference)

[BibTex]

2006

[BibTex]


A non-parametric {Bayesian} approach to spike sorting
A non-parametric Bayesian approach to spike sorting

Wood, F., Goldwater, S., Black, M. J.

In International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pages: 1165-1169, New York, NY, August 2006 (inproceedings)

pdf [BibTex]

pdf [BibTex]


Predicting {3D} people from {2D} pictures
Predicting 3D people from 2D pictures

(Best Paper)

Sigal, L., Black, M. J.

In Proc. IV Conf. on Articulated Motion and DeformableObjects (AMDO), LNCS 4069, pages: 185-195, July 2006 (inproceedings)

Abstract
We propose a hierarchical process for inferring the 3D pose of a person from monocular images. First we infer a learned view-based 2D body model from a single image using non-parametric belief propagation. This approach integrates information from bottom-up body-part proposal processes and deals with self-occlusion to compute distributions over limb poses. Then, we exploit a learned Mixture of Experts model to infer a distribution of 3D poses conditioned on 2D poses. This approach is more general than recent work on inferring 3D pose directly from silhouettes since the 2D body model provides a richer representation that includes the 2D joint angles and the poses of limbs that may be unobserved in the silhouette. We demonstrate the method in a laboratory setting where we evaluate the accuracy of the 3D poses against ground truth data. We also estimate 3D body pose in a monocular image sequence. The resulting 3D estimates are sufficiently accurate to serve as proposals for the Bayesian inference of 3D human motion over time

pdf pdf from publisher Video [BibTex]

pdf pdf from publisher Video [BibTex]


Specular flow and the recovery of surface structure
Specular flow and the recovery of surface structure

Roth, S., Black, M.

In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, CVPR, 2, pages: 1869-1876, New York, NY, June 2006 (inproceedings)

Abstract
In scenes containing specular objects, the image motion observed by a moving camera may be an intermixed combination of optical flow resulting from diffuse reflectance (diffuse flow) and specular reflection (specular flow). Here, with few assumptions, we formalize the notion of specular flow, show how it relates to the 3D structure of the world, and develop an algorithm for estimating scene structure from 2D image motion. Unlike previous work on isolated specular highlights we use two image frames and estimate the semi-dense flow arising from the specular reflections of textured scenes. We parametrically model the image motion of a quadratic surface patch viewed from a moving camera. The flow is modeled as a probabilistic mixture of diffuse and specular components and the 3D shape is recovered using an Expectation-Maximization algorithm. Rather than treating specular reflections as noise to be removed or ignored, we show that the specular flow provides additional constraints on scene geometry that improve estimation of 3D structure when compared with reconstruction from diffuse flow alone. We demonstrate this for a set of synthetic and real sequences of mixed specular-diffuse objects.

pdf [BibTex]

pdf [BibTex]


An adaptive appearance model approach for model-based articulated object tracking
An adaptive appearance model approach for model-based articulated object tracking

Balan, A., Black, M. J.

In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, CVPR, 1, pages: 758-765, New York, NY, June 2006 (inproceedings)

Abstract
The detection and tracking of three-dimensional human body models has progressed rapidly but successful approaches typically rely on accurate foreground silhouettes obtained using background segmentation. There are many practical applications where such information is imprecise. Here we develop a new image likelihood function based on the visual appearance of the subject being tracked. We propose a robust, adaptive, appearance model based on the Wandering-Stable-Lost framework extended to the case of articulated body parts. The method models appearance using a mixture model that includes an adaptive template, frame-to-frame matching and an outlier process. We employ an annealed particle filtering algorithm for inference and take advantage of the 3D body model to predict self occlusion and improve pose estimation accuracy. Quantitative tracking results are presented for a walking sequence with a 180 degree turn, captured with four synchronized and calibrated cameras and containing significant appearance changes and self-occlusion in each view.

pdf [BibTex]

pdf [BibTex]


Measure locally, reason globally: Occlusion-sensitive articulated pose estimation
Measure locally, reason globally: Occlusion-sensitive articulated pose estimation

Sigal, L., Black, M. J.

In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, CVPR, 2, pages: 2041-2048, New York, NY, June 2006 (inproceedings)

pdf [BibTex]

pdf [BibTex]


Statistical analysis of the non-stationarity of neural population codes
Statistical analysis of the non-stationarity of neural population codes

Kim, S., Wood, F., Fellows, M., Donoghue, J. P., Black, M. J.

In BioRob 2006, The first IEEE / RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, pages: 295-299, Pisa, Italy, Febuary 2006 (inproceedings)

pdf [BibTex]

pdf [BibTex]


no image
How to choose the covariance for Gaussian process regression independently of the basis

Franz, M., Gehler, P.

In Proceedings of the Workshop Gaussian Processes in Practice, 2006 (inproceedings)

pdf [BibTex]

pdf [BibTex]


The rate adapting poisson model for information retrieval and object recognition
The rate adapting poisson model for information retrieval and object recognition

Gehler, P. V., Holub, A. D., Welling, M.

In Proceedings of the 23rd international conference on Machine learning, pages: 337-344, ICML ’06, ACM, New York, NY, USA, 2006 (inproceedings)

project page pdf DOI [BibTex]

project page pdf DOI [BibTex]


Implicit Wiener Series, Part II: Regularised estimation
Implicit Wiener Series, Part II: Regularised estimation

Gehler, P., Franz, M.

(148), Max Planck Institute, 2006 (techreport)

pdf [BibTex]


Tracking complex objects using graphical object models
Tracking complex objects using graphical object models

Sigal, L., Zhu, Y., Comaniciu, D., Black, M. J.

In International Workshop on Complex Motion, LNCS 3417, pages: 223-234, Springer-Verlag, 2006 (inproceedings)

pdf pdf from publisher [BibTex]

pdf pdf from publisher [BibTex]


{HumanEva}: Synchronized video and motion capture dataset for evaluation of articulated human motion
HumanEva: Synchronized video and motion capture dataset for evaluation of articulated human motion

Sigal, L., Black, M. J.

(CS-06-08), Brown University, Department of Computer Science, 2006 (techreport)

pdf abstract [BibTex]

pdf abstract [BibTex]


Hierarchical Approach for Articulated {3D} Pose-Estimation and Tracking (extended abstract)
Hierarchical Approach for Articulated 3D Pose-Estimation and Tracking (extended abstract)

Sigal, L., Black, M. J.

In Learning, Representation and Context for Human Sensing in Video Workshop (in conjunction with CVPR), 2006 (inproceedings)

pdf poster [BibTex]

pdf poster [BibTex]


Nonlinear physically-based models for decoding motor-cortical population activity
Nonlinear physically-based models for decoding motor-cortical population activity

Shakhnarovich, G., Kim, S., Black, M. J.

In Advances in Neural Information Processing Systems 19, NIPS-2006, pages: 1257-1264, MIT Press, 2006 (inproceedings)

pdf [BibTex]

pdf [BibTex]


no image
A comparison of decoding models for imagined motion from human motor cortex

Kim, S., Simeral, J., Donoghue, J. P., Hocherberg, L. R., Friehs, G., Mukand, J. A., Chen, D., Black, M. J.

Program No. 256.11. 2006 Abstract Viewer and Itinerary Planner, Society for Neuroscience, Atlanta, GA, 2006, Online (conference)

[BibTex]

[BibTex]


Denoising archival films using a learned {Bayesian} model
Denoising archival films using a learned Bayesian model

Moldovan, T. M., Roth, S., Black, M. J.

In Int. Conf. on Image Processing, ICIP, pages: 2641-2644, Atlanta, 2006 (inproceedings)

pdf [BibTex]

pdf [BibTex]


Efficient belief propagation with learned higher-order {Markov} random fields
Efficient belief propagation with learned higher-order Markov random fields

Lan, X., Roth, S., Huttenlocher, D., Black, M. J.

In European Conference on Computer Vision, ECCV, II, pages: 269-282, Graz, Austria, 2006 (inproceedings)

pdf pdf from publisher [BibTex]

pdf pdf from publisher [BibTex]


no image
Modeling neural control of physically realistic movement

Shaknarovich, G., Kim, S., Donoghue, J. P., Hocherberg, L. R., Friehs, G., Mukand, J. A., Chen, D., Black, M. J.

Program No. 256.12. 2006 Abstract Viewer and Itinerary Planner, Society for Neuroscience, Atlanta, GA, 2006, Online (conference)

[BibTex]

[BibTex]

2004


no image
Automatic spike sorting for neural decoding

Wood, F. D., Fellows, M., Donoghue, J. P., Black, M. J.

In Proc. IEEE Engineering in Medicine and Biology Society, pages: 4009-4012, September 2004 (inproceedings)

pdf [BibTex]

2004

pdf [BibTex]


Closed-loop neural control of cursor motion using a {Kalman} filter
Closed-loop neural control of cursor motion using a Kalman filter

Wu, W., Shaikhouni, A., Donoghue, J. P., Black, M. J.

In Proc. IEEE Engineering in Medicine and Biology Society, pages: 4126-4129, September 2004 (inproceedings)

pdf [BibTex]

pdf [BibTex]


The dense estimation of motion and appearance in layers
The dense estimation of motion and appearance in layers

Yalcin, H., Black, M. J., Fablet, R.

In IEEE Workshop on Image and Video Registration, June 2004 (inproceedings)

pdf [BibTex]

pdf [BibTex]


{3D} human limb detection using space carving and multi-view eigen models
3D human limb detection using space carving and multi-view eigen models

Bhatia, S., Sigal, L., Isard, M., Black, M. J.

In IEEE Workshop on Articulated and Nonrigid Motion, June 2004 (inproceedings)

pdf [BibTex]

pdf [BibTex]


Tracking loose-limbed people
Tracking loose-limbed people

Sigal, L., Bhatia, S., Roth, S., Black, M. J., Isard, M.

In IEEE Conf. on Computer Vision and Pattern Recognition, 1, pages: 421-428, June 2004 (inproceedings)

pdf [BibTex]

pdf [BibTex]


Gibbs likelihoods for {Bayesian} tracking
Gibbs likelihoods for Bayesian tracking

Roth, S., Sigal, L., Black, M. J.

In IEEE Conf. on Computer Vision and Pattern Recognition, 1, pages: 886-893, June 2004 (inproceedings)

pdf [BibTex]

pdf [BibTex]


no image
A direct brain-machine interface for 2D cursor control using a Kalman filter

Shaikhouni, A., Wu, W., Moris, D. S., Donoghue, J. P., Black, M. J.

Society for Neuroscience, 2004, Online (conference)

abstract [BibTex]

abstract [BibTex]

1994


Estimating multiple independent motions in segmented images using parametric models with local deformations
Estimating multiple independent motions in segmented images using parametric models with local deformations

Black, M. J., Jepson, A.

In Workshop on Non-rigid and Articulate Motion, pages: 220-227, Austin, Texas, November 1994 (inproceedings)

pdf abstract [BibTex]

1994

pdf abstract [BibTex]


Time to contact from active tracking of motion boundaries
Time to contact from active tracking of motion boundaries

Ju, X., Black, M. J.

In Intelligent Robots and Computer Vision XIII: 3D Vision, Product Inspection, and Active Vision, pages: 26-37, Proc. SPIE 2354, Boston, Massachusetts, November 1994 (inproceedings)

pdf abstract [BibTex]

pdf abstract [BibTex]


The outlier process: Unifying line processes and robust statistics
The outlier process: Unifying line processes and robust statistics

Black, M., Rangarajan, A.

In IEEE Conf. on Computer Vision and Pattern Recognition, CVPR’94, pages: 15-22, Seattle, WA, June 1994 (inproceedings)

pdf abstract [BibTex]

pdf abstract [BibTex]


Recursive non-linear estimation of discontinuous flow fields
Recursive non-linear estimation of discontinuous flow fields

Black, M.

In Proc. Third European Conf. on Computer Vision, ECCV’94,, pages: 138-145, LNCS 800, Springer Verlag, Sweden, May 1994 (inproceedings)

pdf abstract [BibTex]

pdf abstract [BibTex]

1993


Mixture models for optical flow computation
Mixture models for optical flow computation

Jepson, A., Black, M.

In IEEE Conf. on Computer Vision and Pattern Recognition, CVPR-93, pages: 760-761, New York, NY, June 1993 (inproceedings)

Abstract
The computation of optical flow relies on merging information available over an image patch to form an estimate of 2-D image velocity at a point. This merging process raises many issues. These include the treatment of outliers in component velocity measurements and the modeling of multiple motions within a patch which arise from occlusion boundaries or transparency. A new approach for dealing with these issues is presented. It is based on the use of a probabilistic mixture model to explicitly represent multiple motions within a patch. A simple extension of the EM-algorithm is used to compute a maximum likelihood estimate for the various motion parameters. Preliminary experiments indicate that this approach is computationally efficient, and that it can provide robust estimates of the optical flow values in the presence of outliers and multiple motions.

pdf tech report [BibTex]

1993

pdf tech report [BibTex]


A framework for the robust estimation of optical flow
A framework for the robust estimation of optical flow

(Helmholtz Prize)

Black, M. J., Anandan, P.

In Fourth International Conf. on Computer Vision, ICCV-93, pages: 231-236, Berlin, Germany, May 1993 (inproceedings)

Abstract
Most approaches for estimating optical flow assume that, within a finite image region, only a single motion is present. This single motion assumption is violated in common situations involving transparency, depth discontinuities, independently moving objects, shadows, and specular reflections. To robustly estimate optical flow, the single motion assumption must be relaxed. This work describes a framework based on robust estimation that addresses violations of the brightness constancy and spatial smoothness assumptions caused by multiple motions. We show how the robust estimation framework can be applied to standard formulations of the optical flow problem thus reducing their sensitivity to violations of their underlying assumptions. The approach has been applied to three standard techniques for recovering optical flow: area-based regression, correlation, and regularization with motion discontinuities. This work focuses on the recovery of multiple parametric motion models within a region as well as the recovery of piecewise-smooth flow fields and provides examples with natural and synthetic image sequences.

pdf video abstract code [BibTex]

pdf video abstract code [BibTex]


Action, representation, and purpose: Re-evaluating the foundations of computational vision
Action, representation, and purpose: Re-evaluating the foundations of computational vision

Black, M. J., Aloimonos, Y., Brown, C. M., Horswill, I., Malik, J., G. Sandini, , Tarr, M. J.

In International Joint Conference on Artificial Intelligence, IJCAI-93, pages: 1661-1666, Chambery, France, 1993 (inproceedings)

pdf [BibTex]

pdf [BibTex]

1990


A model for the detection of motion over time
A model for the detection of motion over time

Black, M. J., Anandan, P.

In Proc. Int. Conf. on Computer Vision, ICCV-90, pages: 33-37, Osaka, Japan, December 1990 (inproceedings)

Abstract
We propose a model for the recovery of visual motion fields from image sequences. Our model exploits three constraints on the motion of a patch in the environment: i) Data Conservation: the intensity structure corresponding to an environmental surface patch changes gradually over time; ii) Spatial Coherence: since surfaces have spatial extent neighboring points have similar motions; iii) Temporal Coherence: the direction and velocity of motion for a surface patch changes gradually. The formulation of the constraints takes into account the possibility of multiple motions at a particular location. We also present a highly parallel computational model for realizing these constraints in which computation occurs locally, knowledge about the motion increases over time, and occlusion and disocclusion boundaries are estimated. An implementation of the model using a stochastic temporal updating scheme is described. Experiments with both synthetic and real imagery are presented.

pdf [BibTex]

1990

pdf [BibTex]


Constraints for the early detection of discontinuity from motion
Constraints for the early detection of discontinuity from motion

Black, M. J., Anandan, P.

In Proc. National Conf. on Artificial Intelligence, AAAI-90, pages: 1060-1066, Boston, MA, 1990 (inproceedings)

Abstract
Surface discontinuities are detected in a sequence of images by exploiting physical constraints at early stages in the processing of visual motion. To achieve accurate early discontinuity detection we exploit five physical constraints on the presence of discontinuities: i) the shape of the sum of squared differences (SSD) error surface in the presence of surface discontinuities; ii) the change in the shape of the SSD surface due to relative surface motion; iii) distribution of optic flow in a neighborhood of a discontinuity; iv) spatial consistency of discontinuities; V) temporal consistency of discontinuities. The constraints are described, and experimental results on sequences of real and synthetic images are presented. The work has applications in the recovery of environmental structure from motion and in the generation of dense optic flow fields.

pdf [BibTex]

pdf [BibTex]