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2005


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A quantitative evaluation of video-based 3D person tracking

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

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

pdf [BibTex]

2005

pdf [BibTex]


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Inferring attentional state and kinematics from motor cortical firing rates

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

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

pdf [BibTex]

pdf [BibTex]


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Motor cortical decoding using an autoregressive moving average model

Fisher, J., Black, M. J.

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

pdf [BibTex]

pdf [BibTex]


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Fields of Experts: A framework for learning image priors

Roth, S., Black, M. J.

In IEEE Conf. on Computer Vision and Pattern Recognition, 2, pages: 860-867, June 2005 (inproceedings)

pdf [BibTex]

pdf [BibTex]


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On the spatial statistics of optical flow

(Marr Prize, Honorable Mention)

Roth, S., Black, M. J.

In International Conf. on Computer Vision, International Conf. on Computer Vision, pages: 42-49, 2005 (inproceedings)

pdf [BibTex]

pdf [BibTex]


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Modeling neural population spiking activity with Gibbs distributions

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

In Advances in Neural Information Processing Systems 18, pages: 1537-1544, 2005 (inproceedings)

pdf [BibTex]

pdf [BibTex]


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Energy-based models of motor cortical population activity

Wood, F., Black, M.

Program No. 689.20. 2005 Abstract Viewer/Itinerary Planner, Society for Neuroscience, Washington, DC, 2005 (conference)

abstract [BibTex]

abstract [BibTex]

2002


Thumb xl bildschirmfoto 2013 01 15 um 09.54.19
Inferring hand motion from multi-cell recordings in motor cortex using a Kalman filter

Wu, W., Black, M. J., Gao, Y., Bienenstock, E., Serruya, M., Donoghue, J. P.

In SAB’02-Workshop on Motor Control in Humans and Robots: On the Interplay of Real Brains and Artificial Devices, pages: 66-73, Edinburgh, Scotland (UK), August 2002 (inproceedings)

pdf [BibTex]

2002

pdf [BibTex]


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Inferring hand motion from multi-cell recordings in motor cortex using a Kalman filter

Wu, W., Black M., Gao, Y., Bienenstock, E., Serruya, M., Donoghue, J.

Program No. 357.5. 2002 Abstract Viewer/Itinerary Planner, Society for Neuroscience, Washington, DC, 2002, Online (conference)

abstract [BibTex]

abstract [BibTex]


Thumb xl bildschirmfoto 2012 12 11 um 09.50.58
Automatic detection and tracking of human motion with a view-based representation

Fablet, R., Black, M. J.

In European Conf. on Computer Vision, ECCV 2002, 1, pages: 476-491, LNCS 2353, (Editors: A. Heyden and G. Sparr and M. Nielsen and P. Johansen), Springer-Verlag , 2002 (inproceedings)

Abstract
This paper proposes a solution for the automatic detection and tracking of human motion in image sequences. Due to the complexity of the human body and its motion, automatic detection of 3D human motion remains an open, and important, problem. Existing approaches for automatic detection and tracking focus on 2D cues and typically exploit object appearance (color distribution, shape) or knowledge of a static background. In contrast, we exploit 2D optical flow information which provides rich descriptive cues, while being independent of object and background appearance. To represent the optical flow patterns of people from arbitrary viewpoints, we develop a novel representation of human motion using low-dimensional spatio-temporal models that are learned using motion capture data of human subjects. In addition to human motion (the foreground) we probabilistically model the motion of generic scenes (the background); these statistical models are defined as Gibbsian fields specified from the first-order derivatives of motion observations. Detection and tracking are posed in a principled Bayesian framework which involves the computation of a posterior probability distribution over the model parameters (i.e., the location and the type of the human motion) given a sequence of optical flow observations. Particle filtering is used to represent and predict this non-Gaussian posterior distribution over time. The model parameters of samples from this distribution are related to the pose parameters of a 3D articulated model (e.g. the approximate joint angles and movement direction). Thus the approach proves suitable for initializing more complex probabilistic models of human motion. As shown by experiments on real image sequences, our method is able to detect and track people under different viewpoints with complex backgrounds.

pdf [BibTex]

pdf [BibTex]


Thumb xl bildschirmfoto 2012 12 11 um 10.06.33
A layered motion representation with occlusion and compact spatial support

Fleet, D. J., Jepson, A., Black, M. J.

In European Conf. on Computer Vision, ECCV 2002, 1, pages: 692-706, LNCS 2353, (Editors: A. Heyden and G. Sparr and M. Nielsen and P. Johansen), Springer-Verlag , 2002 (inproceedings)

Abstract
We describe a 2.5D layered representation for visual motion analysis. The representation provides a global interpretation of image motion in terms of several spatially localized foreground regions along with a background region. Each of these regions comprises a parametric shape model and a parametric motion model. The representation also contains depth ordering so visibility and occlusion are rightly included in the estimation of the model parameters. Finally, because the number of objects, their positions, shapes and sizes, and their relative depths are all unknown, initial models are drawn from a proposal distribution, and then compared using a penalized likelihood criterion. This allows us to automatically initialize new models, and to compare different depth orderings.

pdf [BibTex]

pdf [BibTex]


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Implicit probabilistic models of human motion for synthesis and tracking

Sidenbladh, H., Black, M. J., Sigal, L.

In European Conf. on Computer Vision, 1, pages: 784-800, 2002 (inproceedings)

Abstract
This paper addresses the problem of probabilistically modeling 3D human motion for synthesis and tracking. Given the high dimensional nature of human motion, learning an explicit probabilistic model from available training data is currently impractical. Instead we exploit methods from texture synthesis that treat images as representing an implicit empirical distribution. These methods replace the problem of representing the probability of a texture pattern with that of searching the training data for similar instances of that pattern. We extend this idea to temporal data representing 3D human motion with a large database of example motions. To make the method useful in practice, we must address the problem of efficient search in a large training set; efficiency is particularly important for tracking. Towards that end, we learn a low dimensional linear model of human motion that is used to structure the example motion database into a binary tree. An approximate probabilistic tree search method exploits the coefficients of this low-dimensional representation and runs in sub-linear time. This probabilistic tree search returns a particular sample human motion with probability approximating the true distribution of human motions in the database. This sampling method is suitable for use with particle filtering techniques and is applied to articulated 3D tracking of humans within a Bayesian framework. Successful tracking results are presented, along with examples of synthesizing human motion using the model.

pdf [BibTex]

pdf [BibTex]


Thumb xl bildschirmfoto 2012 12 11 um 10.29.56
Robust parameterized component analysis: Theory and applications to 2D facial modeling

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

In European Conf. on Computer Vision, ECCV 2002, 4, pages: 653-669, LNCS 2353, Springer-Verlag, 2002 (inproceedings)

pdf [BibTex]

pdf [BibTex]


Thumb xl bildschirmfoto 2013 01 15 um 10.03.10
Probabilistic inference of hand motion from neural activity in motor cortex

Gao, Y., Black, M. J., Bienenstock, E., Shoham, S., Donoghue, J.

In Advances in Neural Information Processing Systems 14, pages: 221-228, MIT Press, 2002 (inproceedings)

pdf [BibTex]

pdf [BibTex]

2000


Thumb xl bildschirmfoto 2012 12 11 um 12.12.25
Stochastic tracking of 3D human figures using 2D image motion

(Winner of the 2010 Koenderink Prize for Fundamental Contributions in Computer Vision)

Sidenbladh, H., Black, M. J., Fleet, D.

In European Conference on Computer Vision, ECCV, pages: 702-718, LNCS 1843, Springer Verlag, Dublin, Ireland, June 2000 (inproceedings)

Abstract
A probabilistic method for tracking 3D articulated human figures in monocular image sequences is presented. Within a Bayesian framework, we define a generative model of image appearance, a robust likelihood function based on image gray level differences, and a prior probability distribution over pose and joint angles that models how humans move. The posterior probability distribution over model parameters is represented using a discrete set of samples and is propagated over time using particle filtering. The approach extends previous work on parameterized optical flow estimation to exploit a complex 3D articulated motion model. It also extends previous work on human motion tracking by including a perspective camera model, by modeling limb self occlusion, and by recovering 3D motion from a monocular sequence. The explicit posterior probability distribution represents ambiguities due to image matching, model singularities, and perspective projection. The method relies only on a frame-to-frame assumption of brightness constancy and hence is able to track people under changing viewpoints, in grayscale image sequences, and with complex unknown backgrounds.

pdf code [BibTex]

2000

pdf code [BibTex]


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Functional analysis of human motion data

Ormoneit, D., Hastie, T., Black, M. J.

In In Proc. 5th World Congress of the Bernoulli Society for Probability and Mathematical Statistics and 63rd Annual Meeting of the Institute of Mathematical Statistics, Guanajuato, Mexico, May 2000 (inproceedings)

[BibTex]

[BibTex]


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Stochastic modeling and tracking of human motion

Ormoneit, D., Sidenbladh, H., Black, M. J., Hastie, T.

Learning 2000, Snowbird, UT, April 2000 (conference)

abstract [BibTex]

abstract [BibTex]


Thumb xl bildschirmfoto 2012 12 12 um 11.40.47
A framework for modeling the appearance of 3D articulated figures

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

In Int. Conf. on Automatic Face and Gesture Recognition, pages: 368-375, Grenoble, France, March 2000 (inproceedings)

pdf [BibTex]

pdf [BibTex]

1995


Thumb xl bildschirmfoto 2013 01 14 um 10.58.31
Robust estimation of multiple surface shapes from occluded textures

Black, M. J., Rosenholtz, R.

In International Symposium on Computer Vision, pages: 485-490, Miami, FL, November 1995 (inproceedings)

pdf [BibTex]

1995

pdf [BibTex]


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The PLAYBOT Project

Tsotsos, J. K., Dickinson, S., Jenkin, M., Milios, E., Jepson, A., Down, B., Amdur, E., Stevenson, S., Black, M., Metaxas, D., Cooperstock, J., Culhane, S., Nuflo, F., Verghese, G., Wai, W., Wilkes, D., Ye, Y.

In Proc. IJCAI Workshop on AI Applications for Disabled People, Montreal, August 1995 (inproceedings)

abstract [BibTex]

abstract [BibTex]


Thumb xl bildschirmfoto 2013 01 14 um 11.06.54
Recognizing facial expressions under rigid and non-rigid facial motions using local parametric models of image motion

Black, M. J., Yacoob, Y.

In International Workshop on Automatic Face- and Gesture-Recognition, Zurich, July 1995 (inproceedings)

video abstract [BibTex]

video abstract [BibTex]


Thumb xl bildschirmfoto 2013 01 14 um 11.24.02
Tracking and recognizing rigid and non-rigid facial motions using local parametric models of image motion

Black, M. J., Yacoob, Y.

In Fifth International Conf. on Computer Vision, ICCV’95, pages: 347-381, Boston, MA, June 1995 (inproceedings)

Abstract
This paper explores the use of local parametrized models of image motion for recovering and recognizing the non-rigid and articulated motion of human faces. Parametric flow models (for example affine) are popular for estimating motion in rigid scenes. We observe that within local regions in space and time, such models not only accurately model non-rigid facial motions but also provide a concise description of the motion in terms of a small number of parameters. These parameters are intuitively related to the motion of facial features during facial expressions and we show how expressions such as anger, happiness, surprise, fear, disgust and sadness can be recognized from the local parametric motions in the presence of significant head motion. The motion tracking and expression recognition approach performs with high accuracy in extensive laboratory experiments involving 40 subjects as well as in television and movie sequences.

pdf video publisher site [BibTex]

pdf video publisher site [BibTex]


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A computational model for shape from texture for multiple textures

Black, M. J., Rosenholtz, R.

Investigative Ophthalmology and Visual Science Supplement, Vol. 36, No. 4, pages: 2202, March 1995 (conference)

abstract [BibTex]

abstract [BibTex]

1994


Thumb xl bildschirmfoto 2013 01 14 um 11.32.33
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]


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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]


Thumb xl bildschirmfoto 2013 01 14 um 11.39.54
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]


Thumb xl bildschirmfoto 2013 01 14 um 11.42.57
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]

1992


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Psychophysical implications of temporal persistence in early vision: A computational account of representational momentum

Tarr, M. J., Black, M. J.

Investigative Ophthalmology and Visual Science Supplement, Vol. 36, No. 4, 33, pages: 1050, May 1992 (conference)

abstract [BibTex]

1992

abstract [BibTex]


Thumb xl bildschirmfoto 2013 01 14 um 12.01.23
Combining intensity and motion for incremental segmentation and tracking over long image sequences

Black, M. J.

In Proc. Second European Conf. on Computer Vision, ECCV-92, pages: 485-493, LNCS 588, Springer Verlag, May 1992 (inproceedings)

pdf video abstract [BibTex]

pdf video abstract [BibTex]