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1996


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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 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]


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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]


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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]

1993


Thumb xl bildschirmfoto 2013 01 14 um 11.48.36
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)

pdf abstract tech report [BibTex]

1993

pdf abstract tech report [BibTex]


Thumb xl bildschirmfoto 2013 01 14 um 11.52.45
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]


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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]