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Graph scale-space theory for distributed peak and pit identification. / Loukas, Andreas; Cattani, Marco; Zuniga, Marco; Gao, Jie.

IPSN '15 : Proceedings of the 14th International Conference on Information Processing in Sensor Networks . New York, NY : Association for Computing Machinery (ACM), 2015. p. 118-129.

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Harvard

Loukas, A, Cattani, M, Zuniga, M & Gao, J 2015, Graph scale-space theory for distributed peak and pit identification. in IPSN '15 : Proceedings of the 14th International Conference on Information Processing in Sensor Networks . Association for Computing Machinery (ACM), New York, NY, pp. 118-129, IPSN 2015, Seattle, United States, 13/04/15. https://doi.org/10.1145/2737095.2737101

APA

Loukas, A., Cattani, M., Zuniga, M., & Gao, J. (2015). Graph scale-space theory for distributed peak and pit identification. In IPSN '15 : Proceedings of the 14th International Conference on Information Processing in Sensor Networks (pp. 118-129). New York, NY: Association for Computing Machinery (ACM). https://doi.org/10.1145/2737095.2737101

Vancouver

Loukas A, Cattani M, Zuniga M, Gao J. Graph scale-space theory for distributed peak and pit identification. In IPSN '15 : Proceedings of the 14th International Conference on Information Processing in Sensor Networks . New York, NY: Association for Computing Machinery (ACM). 2015. p. 118-129 https://doi.org/10.1145/2737095.2737101

Author

Loukas, Andreas ; Cattani, Marco ; Zuniga, Marco ; Gao, Jie. / Graph scale-space theory for distributed peak and pit identification. IPSN '15 : Proceedings of the 14th International Conference on Information Processing in Sensor Networks . New York, NY : Association for Computing Machinery (ACM), 2015. pp. 118-129

BibTeX

@inproceedings{b8d557cebcb5462488f51ec231b5fc74,
title = "Graph scale-space theory for distributed peak and pit identification",
abstract = "Graph filters are a recent and powerful tool to process information in graphs. Yet despite their advantages, graph filters are limited. The limitation is exposed in a filtering task that is common, but not fully solved in sensor networks: the identification of a signal's peaks and pits. Choosing the correct filter necessitates a-priori information about the signal and the network topology. Furthermore, in sparse and irregular networks graph filters introduce distortion, effectively rendering identification inaccurate, even when signal-specific information is available. Motivated by the need for a multi-scale approach, this paper extends classical results on scale-space analysis to graphs. We derive the family of scale-space kernels (or filters) that are suitable for graphs and show how these can be used to observe a signal at all possible scales: from fine to coarse. The gathered information is then used to distributedly identify the signal's peaks and pits. Our graph scale-space approach diminishes the need for a-priori knowledge, and reduces the effects caused by noise, sparse and irregular topologies, exhibiting: (i) superior resilience to noise than the state-of-the-art, and (ii) at least 20{\%} higher precision than the best graph filter, when evaluated on our testbed.",
author = "Andreas Loukas and Marco Cattani and Marco Zuniga and Jie Gao",
year = "2015",
month = "4",
day = "13",
doi = "10.1145/2737095.2737101",
language = "English",
isbn = "978-1-4503-3475-4",
pages = "118--129",
booktitle = "IPSN '15",
publisher = "Association for Computing Machinery (ACM)",
address = "United States",

}

RIS

TY - GEN

T1 - Graph scale-space theory for distributed peak and pit identification

AU - Loukas, Andreas

AU - Cattani, Marco

AU - Zuniga, Marco

AU - Gao, Jie

PY - 2015/4/13

Y1 - 2015/4/13

N2 - Graph filters are a recent and powerful tool to process information in graphs. Yet despite their advantages, graph filters are limited. The limitation is exposed in a filtering task that is common, but not fully solved in sensor networks: the identification of a signal's peaks and pits. Choosing the correct filter necessitates a-priori information about the signal and the network topology. Furthermore, in sparse and irregular networks graph filters introduce distortion, effectively rendering identification inaccurate, even when signal-specific information is available. Motivated by the need for a multi-scale approach, this paper extends classical results on scale-space analysis to graphs. We derive the family of scale-space kernels (or filters) that are suitable for graphs and show how these can be used to observe a signal at all possible scales: from fine to coarse. The gathered information is then used to distributedly identify the signal's peaks and pits. Our graph scale-space approach diminishes the need for a-priori knowledge, and reduces the effects caused by noise, sparse and irregular topologies, exhibiting: (i) superior resilience to noise than the state-of-the-art, and (ii) at least 20% higher precision than the best graph filter, when evaluated on our testbed.

AB - Graph filters are a recent and powerful tool to process information in graphs. Yet despite their advantages, graph filters are limited. The limitation is exposed in a filtering task that is common, but not fully solved in sensor networks: the identification of a signal's peaks and pits. Choosing the correct filter necessitates a-priori information about the signal and the network topology. Furthermore, in sparse and irregular networks graph filters introduce distortion, effectively rendering identification inaccurate, even when signal-specific information is available. Motivated by the need for a multi-scale approach, this paper extends classical results on scale-space analysis to graphs. We derive the family of scale-space kernels (or filters) that are suitable for graphs and show how these can be used to observe a signal at all possible scales: from fine to coarse. The gathered information is then used to distributedly identify the signal's peaks and pits. Our graph scale-space approach diminishes the need for a-priori knowledge, and reduces the effects caused by noise, sparse and irregular topologies, exhibiting: (i) superior resilience to noise than the state-of-the-art, and (ii) at least 20% higher precision than the best graph filter, when evaluated on our testbed.

UR - http://www.scopus.com/inward/record.url?scp=84954184547&partnerID=8YFLogxK

U2 - 10.1145/2737095.2737101

DO - 10.1145/2737095.2737101

M3 - Conference contribution

SN - 978-1-4503-3475-4

SP - 118

EP - 129

BT - IPSN '15

PB - Association for Computing Machinery (ACM)

CY - New York, NY

ER -

ID: 46986366