A higher-order active contour model for tree detection

Péter Horváth, Ian Jermyn, Zoltan Kato, Josiane Zerubia

Research output: Conference contribution

18 Citations (Scopus)

Abstract

We present a model of a 'gas of circles', the ensemble of regions in the image domain consisting of an unknown number of circles with approximately fixed radius and short range repulsive interactions, and apply it to the extraction of tree crowns from aerial images. The method uses the recently introduced 'higher order active contours' (HOACs), which incorporate long-range interactions between contour points, and thereby include prior geometric information without using a template shape. This makes them ideal when looking for multiple instances of an entity in an image. We study an existing HOAC model for networks, and show via a stability calculation that circles stable to perturbations are possible for constrained parameter sets. Combining this prior energy with a data term, we show results on aerial imagery that demonstrate the effectiveness of the method and the need for prior geometric knowledge. The model has many other potential applications.

Original languageEnglish
Title of host publicationProceedings - 18th International Conference on Pattern Recognition, ICPR 2006
Pages130-133
Number of pages4
DOIs
Publication statusPublished - dec. 1 2006
Event18th International Conference on Pattern Recognition, ICPR 2006 - Hong Kong, China
Duration: aug. 20 2006aug. 24 2006

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume2
ISSN (Print)1051-4651

Other

Other18th International Conference on Pattern Recognition, ICPR 2006
CountryChina
CityHong Kong
Period8/20/068/24/06

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ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Cite this

Horváth, P., Jermyn, I., Kato, Z., & Zerubia, J. (2006). A higher-order active contour model for tree detection. In Proceedings - 18th International Conference on Pattern Recognition, ICPR 2006 (pp. 130-133). [1699164] (Proceedings - International Conference on Pattern Recognition; Vol. 2). https://doi.org/10.1109/ICPR.2006.79