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106 results (View BibTeX file of all listed publications)

1995

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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]

1995

Black, M. J., Yacoob, Y. Recognizing facial expressions under rigid and non-rigid facial motions using local parametric models of image motion In International Workshop on Automatic Face- and Gesture-Recognition, Zurich, July 1995 (inproceedings)

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]

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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]

Black, M. J., Yacoob, Y. Tracking and recognizing rigid and non-rigid facial motions using local parametric models of image motion In Fifth International Conf. on Computer Vision, ICCV’95, pages: 347-381, Boston, MA, June 1995 (inproceedings)

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]

Black, M. J., Rosenholtz, R. A computational model for shape from texture for multiple textures Investigative Ophthalmology and Visual Science Supplement, Vol. 36, No. 4, pages: 2202, March 1995 (conference)

abstract [BibTex]

1991

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Dynamic motion estimation and feature extraction over long image sequences

Black, M. J., Anandan, P.

In Proc. IJCAI Workshop on Dynamic Scene Understanding, Sydney, Australia, August 1991 (inproceedings)

[BibTex]

1991

Black, M. J., Anandan, P. Dynamic motion estimation and feature extraction over long image sequences In Proc. IJCAI Workshop on Dynamic Scene Understanding, Sydney, Australia, August 1991 (inproceedings)

[BibTex]

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Robust dynamic motion estimation over time

(IEEE Computer Society Outstanding Paper Award)

Black, M. J., Anandan, P.

In Proc. Computer Vision and Pattern Recognition, CVPR-91,, pages: 296-302, Maui, Hawaii, June 1991 (inproceedings)

Abstract
This paper presents a novel approach to incrementally estimating visual motion over a sequence of images. We start by formulating constraints on image motion to account for the possibility of multiple motions. This is achieved by exploiting the notions of weak continuity and robust statistics in the formulation of the minimization problem. The resulting objective function is non-convex. Traditional stochastic relaxation techniques for minimizing such functions prove inappropriate for the task. We present a highly parallel incremental stochastic minimization algorithm which has a number of advantages over previous approaches. The incremental nature of the scheme makes it truly dynamic and permits the detection of occlusion and disocclusion boundaries.

pdf video abstract [BibTex]

Black, M. J., Anandan, P. Robust dynamic motion estimation over time In Proc. Computer Vision and Pattern Recognition, CVPR-91,, pages: 296-302, Maui, Hawaii, June 1991 (inproceedings)

pdf video abstract [BibTex]