A higher-order active contour model of a `gas of circles' and its application to tree crown extraction
Horváth, P.; Jermyn, I.H.; Kato, Z.; Zerubia, J.
Professor Ian Jermyn email@example.com
We present a model of a ‘gas of circles’: regions in the image domain composed of a unknown number of circles of approximately the same radius. The model has applications to medical, biological, nanotechnological, and remote sensing imaging. The model is constructed using higher-order active contours (HOACs) in order to include non-trivial prior knowledge about region shape without constraining topology. The main theoretical contribution is an analysis of the local minima of the HOAC energy that allows us to guarantee stable circles, fix one of the model parameters, and constrain the rest. We apply the model to tree crown extraction from aerial images of plantations. Numerical experiments both confirm the theoretical analysis and show the empirical importance of the prior shape information.
Horváth, P., Jermyn, I., Kato, Z., & Zerubia, J. (2009). A higher-order active contour model of a `gas of circles' and its application to tree crown extraction. Pattern Recognition, 42(5), 699-709. https://doi.org/10.1016/j.patcog.2008.09.008
|Journal Article Type||Article|
|Publication Date||May 1, 2009|
|Deposit Date||Aug 12, 2011|
|Publicly Available Date||Aug 21, 2015|
|Peer Reviewed||Peer Reviewed|
|Keywords||Tree, Crown, Extraction, Aerial image, Higher-order, Active contour, Gas of circles, Prior, Shape.|
Accepted Journal Article
NOTICE: this is the author’s version of a work that was accepted for publication in Pattern Recognition. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition, 42, 5, May 2009, 10.1016/j.patcog.2008.09.008.
You might also like
Modality-Constrained Density Estimation via Deformable Templates
Assessing the Non-Uniqueness of a Well Test Interpretation Model Using a Bayesian Approach