Professor Ian Jermyn i.h.jermyn@durham.ac.uk
Professor
Globally Optimal Regions and Boundaries as Minimum Ratio Cycles
Jermyn, I.H.; Ishikawa, H.
Authors
H. Ishikawa
Abstract
We describe a new form of energy functional for the modeling and identification of regions in images. The energy is defined on the space of boundaries in the image domain and can incorporate very general combinations of modeling information both from the boundary (intensity gradients, etc.) and from the interior of the region (texture, homogeneity, etc.). We describe two polynomial-time digraph algorithms for finding the global minima of this energy. One of the algorithms is completely general, minimizing the functional for any choice of modeling information. It runs in a few seconds on a 256×256 image. The other algorithm applies to a subclass of functionals, but has the advantage of being extremely parallelizable. Neither algorithm requires initialization.
Citation
Jermyn, I., & Ishikawa, H. (2001). Globally Optimal Regions and Boundaries as Minimum Ratio Cycles. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(10), 1075-1088. https://doi.org/10.1109/34.954599
Journal Article Type | Article |
---|---|
Publication Date | Oct 1, 2001 |
Deposit Date | Aug 12, 2011 |
Publicly Available Date | Feb 24, 2016 |
Journal | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Print ISSN | 0162-8828 |
Electronic ISSN | 1939-3539 |
Publisher | Institute of Electrical and Electronics Engineers |
Peer Reviewed | Peer Reviewed |
Volume | 23 |
Issue | 10 |
Pages | 1075-1088 |
DOI | https://doi.org/10.1109/34.954599 |
Files
Accepted Journal Article
(1.2 Mb)
PDF
Copyright Statement
© 2001 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
You might also like
Modality-Constrained Density Estimation via Deformable Templates
(2021)
Journal Article
Assessing the Non-Uniqueness of a Well Test Interpretation Model Using a Bayesian Approach
(2020)
Conference Proceeding
Statistical Characterisation of Fluvial Sand Bodies: Implications for Complex Reservoir Models
(2019)
Conference Proceeding
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search