Jet based feature classification

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Standard

Jet based feature classification. / Lillholm, Martin; Pedersen, Kim Steenstrup.

Proceedings of the 17th International Conference on Pattern Recognition, 2004: ICPR 2004. IEEE, 2004. s. 787-790.

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Harvard

Lillholm, M & Pedersen, KS 2004, Jet based feature classification. i Proceedings of the 17th International Conference on Pattern Recognition, 2004: ICPR 2004. IEEE, s. 787-790, 17th International Conference on Pattern Recognition, Cambridge, Storbritannien, 23/08/2004. https://doi.org/10.1109/ICPR.2004.1334376

APA

Lillholm, M., & Pedersen, K. S. (2004). Jet based feature classification. I Proceedings of the 17th International Conference on Pattern Recognition, 2004: ICPR 2004 (s. 787-790). IEEE. https://doi.org/10.1109/ICPR.2004.1334376

Vancouver

Lillholm M, Pedersen KS. Jet based feature classification. I Proceedings of the 17th International Conference on Pattern Recognition, 2004: ICPR 2004. IEEE. 2004. s. 787-790 https://doi.org/10.1109/ICPR.2004.1334376

Author

Lillholm, Martin ; Pedersen, Kim Steenstrup. / Jet based feature classification. Proceedings of the 17th International Conference on Pattern Recognition, 2004: ICPR 2004. IEEE, 2004. s. 787-790

Bibtex

@inproceedings{bf86f3e06aaa11dd8d9f000ea68e967b,
title = "Jet based feature classification",
abstract = "We investigate to which extent the {"}raw{"} mapping of Taylor series coefficients into jet-space can be used as a {"}language{"} for describing local image structure in terms of geometrical image features. Based on empirical data from the van Hateren database, we discuss modelling of probability densities for different feature types, calculate feature posterior maps, and finally perform classification or simultaneous feature detection in a Bayesian framework. We introduce the Brownian image model as a generic background class and extend with empirically estimated densities for edges and blobs. We give examples of simultaneous feature detection across scale.",
author = "Martin Lillholm and Pedersen, {Kim Steenstrup}",
year = "2004",
doi = "10.1109/ICPR.2004.1334376",
language = "English",
isbn = "0-7695-2128-2",
pages = "787--790",
booktitle = "Proceedings of the 17th International Conference on Pattern Recognition, 2004",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Jet based feature classification

AU - Lillholm, Martin

AU - Pedersen, Kim Steenstrup

PY - 2004

Y1 - 2004

N2 - We investigate to which extent the "raw" mapping of Taylor series coefficients into jet-space can be used as a "language" for describing local image structure in terms of geometrical image features. Based on empirical data from the van Hateren database, we discuss modelling of probability densities for different feature types, calculate feature posterior maps, and finally perform classification or simultaneous feature detection in a Bayesian framework. We introduce the Brownian image model as a generic background class and extend with empirically estimated densities for edges and blobs. We give examples of simultaneous feature detection across scale.

AB - We investigate to which extent the "raw" mapping of Taylor series coefficients into jet-space can be used as a "language" for describing local image structure in terms of geometrical image features. Based on empirical data from the van Hateren database, we discuss modelling of probability densities for different feature types, calculate feature posterior maps, and finally perform classification or simultaneous feature detection in a Bayesian framework. We introduce the Brownian image model as a generic background class and extend with empirically estimated densities for edges and blobs. We give examples of simultaneous feature detection across scale.

U2 - 10.1109/ICPR.2004.1334376

DO - 10.1109/ICPR.2004.1334376

M3 - Article in proceedings

SN - 0-7695-2128-2

SP - 787

EP - 790

BT - Proceedings of the 17th International Conference on Pattern Recognition, 2004

PB - IEEE

ER -

ID: 5520639