Hierarchical 3D diffusion wavelet shape priors
Name
Essafi-2009-Hierarchical 3D diffusion wavelet shape priors.pdf
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437.83 KB
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Adobe PDF
Checksum (MD5)
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Author(s)
Essafi, Salma
Langs, Georg
Paragios, Nikos
Date Issued
May 2010
Journal
IEEE 12th International Conference on Computer Vision, 2009
Publisher
Institute of Electrical and Electronics Engineers
Citation
Essafi, S., G. Langs, and N. Paragios. “Hierarchical 3D diffusion wavelet shape priors.” Computer Vision, 2009 IEEE 12th International Conference on. 2009. 1717-1724. © 2010 Institute of Electrical and Electronics Engineers.
Version
Final published version
Abstract
In this paper, we propose a novel representation of prior knowledge for image segmentation, using diffusion wavelets that can reflect arbitrary continuous interdependencies in shape data. The application of diffusion wavelets has, so far, largely been confined to signal processing. In our approach, and in contrast to state-of-the-art methods, we optimize the coefficients, the number and the position of landmarks, and the object topology - the domain on which the wavelets are defined - during the model learning phase, in a coarse-to-fine manner. The resulting paradigm supports hierarchies both in the model and the search space, can encode complex geometric and photometric dependencies of the structure of interest, and can deal with arbitrary topologies. We report results on two challenging medical data sets, that illustrate the impact of the soft parameterization and the potential of the diffusion operator.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ICCV.2009.5459385
