@inproceedings{b19f08b5f98241d7abcfc1b989a85941,
title = "PCA-Based appearance template learning for contour tracking",
abstract = "A novel method is proposed in this paper to model changes of object appearance for object contour tracking. Principal component analysis is utilized to learn eigenvectors from a set of the object appearance in our work, and then the current object appearance can be reconstructed by a linear combination of the eigenvectors. To extract the object contour, we perform covariance matching under the variational level set framework. The proposed method is tested on several sequences under large variations, and demonstrates that it outperforms current methods without updating the appearance template.",
keywords = "Appearance template, Contour tracking, Covariance matrix, Level set, PCA",
author = "Bo Ma and Hongwei Hu and Pei Li and Yin Han",
year = "2013",
doi = "10.1007/978-3-642-42051-1_61",
language = "English",
isbn = "9783642420504",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 3",
pages = "493--500",
booktitle = "Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings",
edition = "PART 3",
note = "20th International Conference on Neural Information Processing, ICONIP 2013 ; Conference date: 03-11-2013 Through 07-11-2013",
}