View alignment with dynamically updated affine tracking

Fernando de la Torre; Shaogang Gong; Stephen McKenna

doi:10.1109/AFGR.1998.670999

View alignment with dynamically updated affine tracking

Fernando de la Torre (Lead / Corresponding author), Shaogang Gong (Lead / Corresponding author), Stephen McKenna (Lead / Corresponding author)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Citations (Scopus)

Abstract

The authors propose a framework for fast view alignment using adaptive affine tracking. They address the issue of modelling both shape and texture information in eigenspace for view alignment. They present an effective bootstrapping process based on colour segmentation and selective attention. They recover affine parameters with dynamic updates to the eigenspace using most recent history and perform predictions in parameter space. Experimental results are given to illustrate their approach

Original language	English
Title of host publication	Third IEEE International Conference on Automatic Face and Gesture Recognition
Subtitle of host publication	Proceedings
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	510-515
Number of pages	6
ISBN (Print)	0818683449
DOIs	https://doi.org/10.1109/AFGR.1998.670999
Publication status	Published - 1998
Event	3rd IEEE International Conference on Automatic Face and Gesture Recognition - New Public Hall, Nara, Japan Duration: 14 Apr 1998 → 16 Apr 1998

Conference

Conference	3rd IEEE International Conference on Automatic Face and Gesture Recognition
Abbreviated title	FG'98
Country/Territory	Japan
City	Nara
Period	14/04/98 → 16/04/98

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10.1109/AFGR.1998.670999

Cite this

@inproceedings{24cb733bf4824864a2c321cabed50365,

title = "View alignment with dynamically updated affine tracking",

abstract = "The authors propose a framework for fast view alignment using adaptive affine tracking. They address the issue of modelling both shape and texture information in eigenspace for view alignment. They present an effective bootstrapping process based on colour segmentation and selective attention. They recover affine parameters with dynamic updates to the eigenspace using most recent history and perform predictions in parameter space. Experimental results are given to illustrate their approach",

author = "{de la Torre}, Fernando and Shaogang Gong and Stephen McKenna",

year = "1998",

doi = "10.1109/AFGR.1998.670999",

language = "English",

isbn = "0818683449 ",

pages = "510--515",

booktitle = "Third IEEE International Conference on Automatic Face and Gesture Recognition",

publisher = "IEEE",

note = "3rd IEEE International Conference on Automatic Face and Gesture Recognition, FG'98 ; Conference date: 14-04-1998 Through 16-04-1998",

}

de la Torre, F, Gong, S & McKenna, S 1998, View alignment with dynamically updated affine tracking. in Third IEEE International Conference on Automatic Face and Gesture Recognition : Proceedings. IEEE, Piscataway, NJ, pp. 510-515, 3rd IEEE International Conference on Automatic Face and Gesture Recognition, Nara, Japan, 14/04/98. https://doi.org/10.1109/AFGR.1998.670999

View alignment with dynamically updated affine tracking. / de la Torre, Fernando (Lead / Corresponding author); Gong, Shaogang (Lead / Corresponding author); McKenna, Stephen (Lead / Corresponding author).
Third IEEE International Conference on Automatic Face and Gesture Recognition : Proceedings. Piscataway, NJ: IEEE, 1998. p. 510-515.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - View alignment with dynamically updated affine tracking

AU - de la Torre, Fernando

AU - Gong, Shaogang

AU - McKenna, Stephen

PY - 1998

Y1 - 1998

N2 - The authors propose a framework for fast view alignment using adaptive affine tracking. They address the issue of modelling both shape and texture information in eigenspace for view alignment. They present an effective bootstrapping process based on colour segmentation and selective attention. They recover affine parameters with dynamic updates to the eigenspace using most recent history and perform predictions in parameter space. Experimental results are given to illustrate their approach

AB - The authors propose a framework for fast view alignment using adaptive affine tracking. They address the issue of modelling both shape and texture information in eigenspace for view alignment. They present an effective bootstrapping process based on colour segmentation and selective attention. They recover affine parameters with dynamic updates to the eigenspace using most recent history and perform predictions in parameter space. Experimental results are given to illustrate their approach

U2 - 10.1109/AFGR.1998.670999

DO - 10.1109/AFGR.1998.670999

M3 - Conference contribution

SN - 0818683449

SP - 510

EP - 515

BT - Third IEEE International Conference on Automatic Face and Gesture Recognition

PB - IEEE

CY - Piscataway, NJ

T2 - 3rd IEEE International Conference on Automatic Face and Gesture Recognition

Y2 - 14 April 1998 through 16 April 1998

ER -

View alignment with dynamically updated affine tracking

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Conference

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