Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/84208
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Type: Conference paper
Title: Nonlinear shape manifolds as shape priors in level set segmentation and tracking
Author: Prisacariu, V.
Reid, I.
Citation: Proceedings of the 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 11) / pp.2185-2192
Publisher: IEEE
Publisher Place: USA
Issue Date: 2011
ISBN: 9781457703942
ISSN: 1063-6919
Conference Name: IEEE Conference on Computer Vision and Pattern Recognition (24th : 2011 : Colorado Springs, CO, U.S.A.)
Statement of
Responsibility: 
Victor Adrian Prisacariu, Ian Reid
Abstract: We propose a novel nonlinear, probabilistic and variational method for adding shape information to level set-based segmentation and tracking. Unlike previous work, we represent shapes with elliptic Fourier descriptors and learn their lower dimensional latent space using Gaussian Process Latent Variable Models. Segmentation is done by a nonlinear minimisation of an image-driven energy function in the learned latent space. We combine it with a 2D pose recovery stage, yielding a single, one shot, optimisation of both shape and pose. We demonstrate the performance of our method, both qualitatively and quantitatively, with multiple images, video sequences and latent spaces, capturing both shape kinematics and object class variance.
Rights: Copyright status unknown
RMID: 0020131184
DOI: 10.1109/CVPR.2011.5995687
Description (link): http://cvpr2011.org/index.html
Appears in Collections:Computer Science publications

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