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2002


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]

2002

pdf [BibTex]

2001


Thumb xl bildschirmfoto 2012 12 11 um 10.41.35
Dynamic coupled component analysis

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

In IEEE Proc. Computer Vision and Pattern Recognition, CVPR’01, 2, pages: 643-650, IEEE, Kauai, Hawaii, December 2001 (inproceedings)

pdf [BibTex]

2001

pdf [BibTex]


Thumb xl bildschirmfoto 2012 12 11 um 11.56.46
Robust principal component analysis for computer vision

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

In Int. Conf. on Computer Vision, ICCV-2001, II, pages: 362-369, Vancouver, BC, USA, 2001 (inproceedings)

pdf Project Page [BibTex]

pdf Project Page [BibTex]


Thumb xl bildschirmfoto 2012 12 11 um 10.58.16
Learning image statistics for Bayesian tracking

Sidenbladh, H., Black, M. J.

In Int. Conf. on Computer Vision, ICCV-2001, II, pages: 709-716, Vancouver, BC, USA, 2001 (inproceedings)

pdf [BibTex]

pdf [BibTex]


no image
Encoding/decoding of arm kinematics from simultaneously recorded MI neurons

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

Society for Neuroscience Abst. Vol. 27, Program No. 572.14, 2001 (conference)

abstract [BibTex]

abstract [BibTex]


Thumb xl bildschirmfoto 2012 12 11 um 12.05.35
Learning and tracking cyclic human motion

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

In Advances in Neural Information Processing Systems 13, NIPS, pages: 894-900, (Editors: Leen, Todd K. and Dietterich, Thomas G. and Tresp, Volker), The MIT Press, 2001 (inproceedings)

pdf [BibTex]

pdf [BibTex]

2000


Thumb xl ijcv2000teaser
Probabilistic detection and tracking of motion boundaries

Black, M. J., Fleet, D. J.

Int. J. of Computer Vision, 38(3):231-245, July 2000 (article)

Abstract
We propose a Bayesian framework for representing and recognizing local image motion in terms of two basic models: translational motion and motion boundaries. Motion boundaries are represented using a non-linear generative model that explicitly encodes the orientation of the boundary, the velocities on either side, the motion of the occluding edge over time, and the appearance/disappearance of pixels at the boundary. We represent the posterior probability distribution over the model parameters given the image data using discrete samples. This distribution is propagated over time using a particle filtering algorithm. To efficiently represent such a high-dimensional space we initialize samples using the responses of a low-level motion discontinuity detector. The formulation and computational model provide a general probabilistic framework for motion estimation with multiple, non-linear, models.

pdf pdf from publisher Video [BibTex]

2000

pdf pdf from publisher Video [BibTex]


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]

pdf code [BibTex]


no image
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]


no image
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]


Thumb xl bildschirmfoto 2012 12 06 um 09.22.34
Design and use of linear models for image motion analysis

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

Int. J. of Computer Vision, 36(3):171-193, 2000 (article)

Abstract
Linear parameterized models of optical flow, particularly affine models, have become widespread in image motion analysis. The linear model coefficients are straightforward to estimate, and they provide reliable estimates of the optical flow of smooth surfaces. Here we explore the use of parameterized motion models that represent much more varied and complex motions. Our goals are threefold: to construct linear bases for complex motion phenomena; to estimate the coefficients of these linear models; and to recognize or classify image motions from the estimated coefficients. We consider two broad classes of motions: i) generic “motion features” such as motion discontinuities and moving bars; and ii) non-rigid, object-specific, motions such as the motion of human mouths. For motion features we construct a basis of steerable flow fields that approximate the motion features. For object-specific motions we construct basis flow fields from example motions using principal component analysis. In both cases, the model coefficients can be estimated directly from spatiotemporal image derivatives with a robust, multi-resolution scheme. Finally, we show how these model coefficients can be use to detect and recognize specific motions such as occlusion boundaries and facial expressions.

pdf [BibTex]

pdf [BibTex]


Thumb xl bildschirmfoto 2012 12 06 um 09.48.16
Robustly estimating changes in image appearance

Black, M. J., Fleet, D. J., Yacoob, Y.

Computer Vision and Image Understanding, 78(1):8-31, 2000 (article)

Abstract
We propose a generalized model of image “appearance change” in which brightness variation over time is represented as a probabilistic mixture of different causes. We define four generative models of appearance change due to (1) object or camera motion; (2) illumination phenomena; (3) specular reflections; and (4) “iconic changes” which are specific to the objects being viewed. These iconic changes include complex occlusion events and changes in the material properties of the objects. We develop a robust statistical framework for recovering these appearance changes in image sequences. This approach generalizes previous work on optical flow to provide a richer description of image events and more reliable estimates of image motion in the presence of shadows and specular reflections.

pdf pdf from publisher DOI [BibTex]

pdf pdf from publisher DOI [BibTex]

1999


Thumb xl bildschirmfoto 2013 01 14 um 09.07.06
Edges as outliers: Anisotropic smoothing using local image statistics

Black, M. J., Sapiro, G.

In Scale-Space Theories in Computer Vision, Second Int. Conf., Scale-Space ’99, pages: 259-270, LNCS 1682, Springer, Corfu, Greece, September 1999 (inproceedings)

Abstract
Edges are viewed as statistical outliers with respect to local image gradient magnitudes. Within local image regions we compute a robust statistical measure of the gradient variation and use this in an anisotropic diffusion framework to determine a spatially varying "edge-stopping" parameter σ. We show how to determine this parameter for two edge-stopping functions described in the literature (Perona-Malik and the Tukey biweight). Smoothing of the image is related the local texture and in regions of low texture, small gradient values may be treated as edges whereas in regions of high texture, large gradient magnitudes are necessary before an edge is preserved. Intuitively these results have similarities with human perceptual phenomena such as masking and "popout". Results are shown on a variety of standard images.

pdf [BibTex]

1999

pdf [BibTex]


Thumb xl bildschirmfoto 2013 01 07 um 12.35.15
Probabilistic detection and tracking of motion discontinuities

(Marr Prize, Honorable Mention)

Black, M. J., Fleet, D. J.

In Int. Conf. on Computer Vision, ICCV-99, pages: 551-558, ICCV, Corfu, Greece, September 1999 (inproceedings)

pdf [BibTex]

pdf [BibTex]


Thumb xl bildschirmfoto 2012 12 06 um 09.38.15
Parameterized modeling and recognition of activities

Yacoob, Y., Black, M. J.

Computer Vision and Image Understanding, 73(2):232-247, 1999 (article)

Abstract
In this paper we consider a class of human activities—atomic activities—which can be represented as a set of measurements over a finite temporal window (e.g., the motion of human body parts during a walking cycle) and which has a relatively small space of variations in performance. A new approach for modeling and recognition of atomic activities that employs principal component analysis and analytical global transformations is proposed. The modeling of sets of exemplar instances of activities that are similar in duration and involve similar body part motions is achieved by parameterizing their representation using principal component analysis. The recognition of variants of modeled activities is achieved by searching the space of admissible parameterized transformations that these activities can undergo. This formulation iteratively refines the recognition of the class to which the observed activity belongs and the transformation parameters that relate it to the model in its class. We provide several experiments on recognition of articulated and deformable human motions from image motion parameters.

pdf pdf from publisher DOI [BibTex]

pdf pdf from publisher DOI [BibTex]


Thumb xl bildschirmfoto 2013 01 14 um 09.12.47
Explaining optical flow events with parameterized spatio-temporal models

Black, M. J.

In IEEE Proc. Computer Vision and Pattern Recognition, CVPR’99, pages: 326-332, IEEE, Fort Collins, CO, 1999 (inproceedings)

pdf video [BibTex]

pdf video [BibTex]


Thumb xl paircover
Artscience Sciencart

Black, M. J., Levy, D., PamelaZ,

In Art and Innovation: The Xerox PARC Artist-in-Residence Program, pages: 244-300, (Editors: Harris, C.), MIT-Press, 1999 (incollection)

abstract [BibTex]

abstract [BibTex]

1998


Thumb xl bildschirmfoto 2012 12 06 um 10.05.20
Summarization of video-taped presentations: Automatic analysis of motion and gesture

Ju, S. X., Black, M. J., Minneman, S., Kimber, D.

IEEE Trans. on Circuits and Systems for Video Technology, 8(5):686-696, September 1998 (article)

Abstract
This paper presents an automatic system for analyzing and annotating video sequences of technical talks. Our method uses a robust motion estimation technique to detect key frames and segment the video sequence into subsequences containing a single overhead slide. The subsequences are stabilized to remove motion that occurs when the speaker adjusts their slides. Any changes remaining between frames in the stabilized sequences may be due to speaker gestures such as pointing or writing, and we use active contours to automatically track these potential gestures. Given the constrained domain, we define a simple set of actions that can be recognized based on the active contour shape and motion. The recognized actions provide an annotation of the sequence that can be used to access a condensed version of the talk from a Web page.

pdf pdf from publisher DOI [BibTex]

1998

pdf pdf from publisher DOI [BibTex]


Thumb xl bildschirmfoto 2013 01 14 um 09.33.36
The Digital Office: Overview

Black, M., Berard, F., Jepson, A., Newman, W., Saund, E., Socher, G., Taylor, M.

In AAAI Spring Symposium on Intelligent Environments, pages: 1-6, Stanford, March 1998 (inproceedings)

pdf [BibTex]

pdf [BibTex]


Thumb xl bildschirmfoto 2012 12 06 um 12.22.18
Robust anisotropic diffusion

Black, M. J., Sapiro, G., Marimont, D., Heeger, D.

IEEE Transactions on Image Processing, 7(3):421-432, March 1998 (article)

Abstract
Relations between anisotropic diffusion and robust statistics are described in this paper. Specifically, we show that anisotropic diffusion can be seen as a robust estimation procedure that estimates a piecewise smooth image from a noisy input image. The edge-stopping; function in the anisotropic diffusion equation is closely related to the error norm and influence function in the robust estimation framework. This connection leads to a new edge-stopping; function based on Tukey's biweight robust estimator that preserves sharper boundaries than previous formulations and improves the automatic stopping of the diffusion. The robust statistical interpretation also provides a means for detecting the boundaries (edges) between the piecewise smooth regions in an image that has been smoothed with anisotropic diffusion. Additionally, we derive a relationship between anisotropic diffusion and regularization with line processes. Adding constraints on the spatial organization of the line processes allows us to develop new anisotropic diffusion equations that result in a qualitative improvement in the continuity of edges

pdf pdf from publisher [BibTex]

pdf pdf from publisher [BibTex]


Thumb xl bildschirmfoto 2013 01 14 um 09.46.31
A framework for modeling appearance change in image sequences

Black, M. J., Fleet, D. J., Yacoob, Y.

In Sixth International Conf. on Computer Vision, ICCV’98, pages: 660-667, Mumbai, India, January 1998 (inproceedings)

Abstract
Image "appearance" may change over time due to a variety of causes such as 1) object or camera motion; 2) generic photometric events including variations in illumination (e.g. shadows) and specular reflections; and 3) "iconic changes" which are specific to the objects being viewed and include complex occlusion events and changes in the material properties of the objects. We propose a general framework for representing and recovering these "appearance changes" in an image sequence as a "mixture" of different causes. The approach generalizes previous work on optical flow to provide a richer description of image events and more reliable estimates of image motion.

pdf video [BibTex]

pdf video [BibTex]


Thumb xl bildschirmfoto 2013 01 14 um 09.49.49
Parameterized modeling and recognition of activities

Yacoob, Y., Black, M. J.

In Sixth International Conf. on Computer Vision, ICCV’98, pages: 120-127, Mumbai, India, January 1998 (inproceedings)

Abstract
A framework for modeling and recognition of temporal activities is proposed. The modeling of sets of exemplar activities is achieved by parameterizing their representation in the form of principal components. Recognition of spatio-temporal variants of modeled activities is achieved by parameterizing the search in the space of admissible transformations that the activities can undergo. Experiments on recognition of articulated and deformable object motion from image motion parameters are presented.

pdf [BibTex]

pdf [BibTex]


Thumb xl bildschirmfoto 2013 01 14 um 09.23.21
Motion feature detection using steerable flow fields

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

In IEEE Conf. on Computer Vision and Pattern Recognition, CVPR-98, pages: 274-281, IEEE, Santa Barbara, CA, 1998 (inproceedings)

Abstract
The estimation and detection of occlusion boundaries and moving bars are important and challenging problems in image sequence analysis. Here, we model such motion features as linear combinations of steerable basis flow fields. These models constrain the interpretation of image motion, and are used in the same way as translational or affine motion models. We estimate the subspace coefficients of the motion feature models directly from spatiotemporal image derivatives using a robust regression method. From the subspace coefficients we detect the presence of a motion feature and solve for the orientation of the feature and the relative velocities of the surfaces. Our method does not require the prior computation of optical flow and recovers accurate estimates of orientation and velocity.

pdf [BibTex]

pdf [BibTex]


Thumb xl paybotteaser
PLAYBOT: A visually-guided robot for physically disabled children

Tsotsos, J. K., Verghese, G., Dickinson, S., Jenkin, M., Jepson, A., Milios, E., Nuflo, F., Stevenson, S., Black, M., Metaxas, D., Culhane, S., Ye, Y., Mann, R.

Image & Vision Computing, Special Issue on Vision for the Disabled, 16(4):275-292, 1998 (article)

Abstract
This paper overviews the PLAYBOT project, a long-term, large-scale research program whose goal is to provide a directable robot which may enable physically disabled children to access and manipulate toys. This domain is the first test domain, but there is nothing inherent in the design of PLAYBOT that prohibits its extension to other tasks. The research is guided by several important goals: vision is the primary sensor; vision is task directed; the robot must be able to visually search its environment; object and event recognition are basic capabilities; environments must be natural and dynamic; users and environments are assumed to be unpredictable; task direction and reactivity must be smoothly integrated; and safety is of high importance. The emphasis of the research has been on vision for the robot this is the most challenging research aspect and the major bottleneck to the development of intelligent robots. Since the control framework is behavior-based, the visual capabilities of PLAYBOT are described in terms of visual behaviors. Many of the components of PLAYBOT are briefly described and several examples of implemented sub-systems are shown. The paper concludes with a description of the current overall system implementation, and a complete example of PLAYBOT performing a simple task.

pdf pdf from publisher DOI [BibTex]

pdf pdf from publisher DOI [BibTex]


Thumb xl bildschirmfoto 2013 01 14 um 09.18.33
Visual surveillance of human activity

L. Davis, S. F., Harwood, D., Yacoob, Y., Hariatoglu, I., Black, M.

In Asian Conference on Computer Vision, ACCV, 1998 (inproceedings)

pdf [BibTex]

pdf [BibTex]


Thumb xl bildschirmfoto 2013 01 14 um 09.29.19
A Probabilistic framework for matching temporal trajectories: Condensation-based recognition of gestures and expressions

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

In European Conf. on Computer Vision, ECCV-98, pages: 909-924, Freiburg, Germany, 1998 (inproceedings)

pdf [BibTex]

pdf [BibTex]


Thumb xl bildschirmfoto 2012 12 06 um 12.33.38
EigenTracking: Robust matching and tracking of articulated objects using a view-based representation

Black, M. J., Jepson, A.

International Journal of Computer Vision, 26(1):63-84, 1998 (article)

Abstract
This paper describes an approach for tracking rigid and articulated objects using a view-based representation. The approach builds on and extends work on eigenspace representations, robust estimation techniques, and parameterized optical flow estimation. First, we note that the least-squares image reconstruction of standard eigenspace techniques has a number of problems and we reformulate the reconstruction problem as one of robust estimation. Second we define a “subspace constancy assumption” that allows us to exploit techniques for parameterized optical flow estimation to simultaneously solve for the view of an object and the affine transformation between the eigenspace and the image. To account for large affine transformations between the eigenspace and the image we define a multi-scale eigenspace representation and a coarse-to-fine matching strategy. Finally, we use these techniques to track objects over long image sequences in which the objects simultaneously undergo both affine image motions and changes of view. In particular we use this “EigenTracking” technique to track and recognize the gestures of a moving hand.

pdf pdf from publisher video [BibTex]


Thumb xl bildschirmfoto 2013 01 14 um 09.40.25
Recognizing temporal trajectories using the Condensation algorithm

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

In Int. Conf. on Automatic Face and Gesture Recognition, pages: 16-21, Nara, Japan, 1998 (inproceedings)

pdf [BibTex]

pdf [BibTex]


Thumb xl cipollabook
Looking at people in action - An overview

Yacoob, Y., Davis, L. S., Black, M., Gavrila, D., Horprasert, T., Morimoto, C.

In Computer Vision for Human–Machine Interaction, (Editors: R. Cipolla and A. Pentland), Cambridge University Press, 1998 (incollection)

publisher site google books [BibTex]

publisher site google books [BibTex]

1997


Thumb xl sharpening
Robust anisotropic diffusion and sharpening of scalar and vector images

Black, M. J., Sapiro, G., Marimont, D., Heeger, D.

In Int. Conf. on Image Processing, ICIP, 1, pages: 263-266, Vol. 1, Santa Barbara, CA, October 1997 (inproceedings)

Abstract
Relations between anisotropic diffusion and robust statistics are described. We show that anisotropic diffusion can be seen as a robust estimation procedure that estimates a piecewise smooth image from a noisy input image. The "edge-stopping" function in the anisotropic diffusion equation is closely related to the error norm and influence function in the robust estimation framework. This connection leads to a new "edge-stopping" function based on Tukey's biweight robust estimator, that preserves sharper boundaries than previous formulations and improves the automatic stopping of the diffusion. The robust statistical interpretation also provides a means for detecting the boundaries (edges) between the piecewise smooth regions in the image. We extend the framework to vector-valued images and show applications to robust image sharpening.

pdf publisher site [BibTex]

1997

pdf publisher site [BibTex]


Thumb xl bildschirmfoto 2013 01 14 um 10.31.38
Robust anisotropic diffusion: Connections between robust statistics, line processing, and anisotropic diffusion

Black, M. J., Sapiro, G., Marimont, D., Heeger, D.

In Scale-Space Theory in Computer Vision, Scale-Space’97, pages: 323-326, LNCS 1252, Springer Verlag, Utrecht, the Netherlands, July 1997 (inproceedings)

pdf [BibTex]

pdf [BibTex]


Thumb xl bildschirmfoto 2013 01 14 um 10.05.56
Learning parameterized models of image motion

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

In IEEE Conf. on Computer Vision and Pattern Recognition, CVPR-97, pages: 561-567, Puerto Rico, June 1997 (inproceedings)

Abstract
A framework for learning parameterized models of optical flow from image sequences is presented. A class of motions is represented by a set of orthogonal basis flow fields that are computed from a training set using principal component analysis. Many complex image motions can be represented by a linear combination of a small number of these basis flows. The learned motion models may be used for optical flow estimation and for model-based recognition. For optical flow estimation we describe a robust, multi-resolution scheme for directly computing the parameters of the learned flow models from image derivatives. As examples we consider learning motion discontinuities, non-rigid motion of human mouths, and articulated human motion.

pdf [BibTex]

pdf [BibTex]


Thumb xl bildschirmfoto 2013 01 14 um 10.13.51
Analysis of gesture and action in technical talks for video indexing

Ju, S. X., Black, M. J., Minneman, S., Kimber, D.

In IEEE Conf. on Computer Vision and Pattern Recognition, pages: 595-601, CVPR-97, Puerto Rico, June 1997 (inproceedings)

Abstract
In this paper, we present an automatic system for analyzing and annotating video sequences of technical talks. Our method uses a robust motion estimation technique to detect key frames and segment the video sequence into subsequences containing a single overhead slide. The subsequences are stabilized to remove motion that occurs when the speaker adjusts their slides. Any changes remaining between frames in the stabilized sequences may be due to speaker gestures such as pointing or writing and we use active contours to automatically track these potential gestures. Given the constrained domain we define a simple ``vocabulary'' of actions which can easily be recognized based on the active contour shape and motion. The recognized actions provide a rich annotation of the sequence that can be used to access a condensed version of the talk from a web page.

pdf [BibTex]

pdf [BibTex]


Thumb xl bildschirmfoto 2013 01 14 um 10.36.36
Modeling appearance change in image sequences

Black, M. J., Yacoob, Y., Fleet, D. J.

In Advances in Visual Form Analysis, pages: 11-20, Proceedings of the Third International Workshop on Visual Form, Capri, Italy, May 1997 (inproceedings)

abstract [BibTex]

abstract [BibTex]


Thumb xl yasersmile
Recognizing facial expressions in image sequences using local parameterized models of image motion

Black, M. J., Yacoob, Y.

Int. Journal of Computer Vision, 25(1):23-48, 1997 (article)

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 performed with high accuracy in extensive laboratory experiments involving 40 subjects as well as in television and movie sequences.

pdf pdf from publisher abstract video [BibTex]


Thumb xl bildschirmfoto 2013 01 15 um 11.00.33
Recognizing human motion using parameterized models of optical flow

Black, M. J., Yacoob, Y., Ju, X. S.

In Motion-Based Recognition, pages: 245-269, (Editors: Mubarak Shah and Ramesh Jain,), Kluwer Academic Publishers, Boston, MA, 1997 (incollection)

pdf [BibTex]

pdf [BibTex]

1996


Thumb xl bildschirmfoto 2013 01 14 um 10.40.24
Cardboard people: A parameterized model of articulated motion

Ju, S. X., Black, M. J., Yacoob, Y.

In 2nd Int. Conf. on Automatic Face- and Gesture-Recognition, pages: 38-44, Killington, Vermont, October 1996 (inproceedings)

Abstract
We extend the work of Black and Yacoob on the tracking and recognition of human facial expressions using parameterized models of optical flow to deal with the articulated motion of human limbs. We define a "cardboard person model" in which a person's limbs are represented by a set of connected planar patches. The parameterized image motion of these patches is constrained to enforce articulated motion and is solved for directly using a robust estimation technique. The recovered motion parameters provide a rich and concise description of the activity that can be used for recognition. We propose a method for performing view-based recognition of human activities from the optical flow parameters that extends previous methods to cope with the cyclical nature of human motion. We illustrate the method with examples of tracking human legs over long image sequences.

pdf [BibTex]

1996

pdf [BibTex]


Thumb xl bildschirmfoto 2012 12 07 um 11.52.07
Estimating optical flow in segmented images using variable-order parametric models with local deformations

Black, M. J., Jepson, A.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(10):972-986, October 1996 (article)

Abstract
This paper presents a new model for estimating optical flow based on the motion of planar regions plus local deformations. The approach exploits brightness information to organize and constrain the interpretation of the motion by using segmented regions of piecewise smooth brightness to hypothesize planar regions in the scene. Parametric flow models are estimated in these regions in a two step process which first computes a coarse fit and estimates the appropriate parameterization of the motion of the region (two, six, or eight parameters). The initial fit is refined using a generalization of the standard area-based regression approaches. Since the assumption of planarity is likely to be violated, we allow local deformations from the planar assumption in the same spirit as physically-based approaches which model shape using coarse parametric models plus local deformations. This parametric+deformation model exploits the strong constraints of parametric approaches while retaining the adaptive nature of regularization approaches. Experimental results on a variety of images indicate that the parametric+deformation model produces accurate flow estimates while the incorporation of brightness segmentation provides precise localization of motion boundaries.

pdf pdf from publisher [BibTex]

pdf pdf from publisher [BibTex]


Thumb xl bildschirmfoto 2012 12 07 um 11.59.00
On the unification of line processes, outlier rejection, and robust statistics with applications in early vision

Black, M., Rangarajan, A.

International Journal of Computer Vision , 19(1):57-92, July 1996 (article)

Abstract
The modeling of spatial discontinuities for problems such as surface recovery, segmentation, image reconstruction, and optical flow has been intensely studied in computer vision. While “line-process” models of discontinuities have received a great deal of attention, there has been recent interest in the use of robust statistical techniques to account for discontinuities. This paper unifies the two approaches. To achieve this we generalize the notion of a “line process” to that of an analog “outlier process” and show how a problem formulated in terms of outlier processes can be viewed in terms of robust statistics. We also characterize a class of robust statistical problems for which an equivalent outlier-process formulation exists and give a straightforward method for converting a robust estimation problem into an outlier-process formulation. We show how prior assumptions about the spatial structure of outliers can be expressed as constraints on the recovered analog outlier processes and how traditional continuation methods can be extended to the explicit outlier-process formulation. These results indicate that the outlier-process approach provides a general framework which subsumes the traditional line-process approaches as well as a wide class of robust estimation problems. Examples in surface reconstruction, image segmentation, and optical flow are presented to illustrate the use of outlier processes and to show how the relationship between outlier processes and robust statistics can be exploited. An appendix provides a catalog of common robust error norms and their equivalent outlier-process formulations.

pdf pdf from publisher DOI [BibTex]


Thumb xl bildschirmfoto 2013 01 14 um 10.48.32
Skin and Bones: Multi-layer, locally affine, optical flow and regularization with transparency

(Nominated: Best paper)

Ju, S., Black, M. J., Jepson, A. D.

In IEEE Conf. on Computer Vision and Pattern Recognition, CVPR’96, pages: 307-314, San Francisco, CA, June 1996 (inproceedings)

pdf [BibTex]

pdf [BibTex]


Thumb xl bildschirmfoto 2013 01 14 um 10.52.58
EigenTracking: Robust matching and tracking of articulated objects using a view-based representation

Black, M. J., Jepson, A.

In Proc. Fourth European Conf. on Computer Vision, ECCV’96, pages: 329-342, LNCS 1064, Springer Verlag, Cambridge, England, April 1996 (inproceedings)

pdf video [BibTex]

pdf video [BibTex]


Thumb xl miximages
Mixture Models for Image Representation

Jepson, A., Black, M.

PRECARN ARK Project Technical Report ARK96-PUB-54, March 1996 (techreport)

Abstract
We consider the estimation of local greylevel image structure in terms of a layered representation. This type of representation has recently been successfully used to segment various objects from clutter using either optical ow or stereo disparity information. We argue that the same type of representation is useful for greylevel data in that it allows for the estimation of properties for each of several different components without prior segmentation. Our emphasis in this paper is on the process used to extract such a layered representation from a given image In particular we consider a variant of the EM algorithm for the estimation of the layered model and consider a novel technique for choosing the number of layers to use. We briefly consider the use of a simple version of this approach for image segmentation and suggest two potential applications to the ARK project

pdf [BibTex]

pdf [BibTex]


Thumb xl bildschirmfoto 2012 12 07 um 12.09.01
The robust estimation of multiple motions: Parametric and piecewise-smooth flow fields

Black, M. J., Anandan, P.

Computer Vision and Image Understanding, 63(1):75-104, January 1996 (article)

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 paper presents 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 paper 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 pdf from publisher [BibTex]

pdf pdf from publisher [BibTex]


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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]

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]


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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]


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Image segmentation using robust mixture models

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

US Pat. 5,802,203, June 1995 (patent)

pdf on-line at USPTO [BibTex]