On Symmetry, Perspectivity, and Level-Set-Based Segmentation
Name
Riklin-2009-On Symmetry, Perspectivity, and Level-Set-Based Segmentation.pdf
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2.14 MB
Format
Adobe PDF
Checksum (MD5)
b5bd6c4f47093601159be57c3d1a443b
Author(s)
Riklin-Raviv, Tammy
Sochen, Nir
Kiryati, Nahum
Date Issued
August 2009
Journal
IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher
Institute of Electrical and Electronics Engineers
Citation
Riklin-Raviv, T., N. Sochen, and N. Kiryati. “On Symmetry, Perspectivity, and Level-Set-Based Segmentation.” Pattern Analysis and Machine Intelligence, IEEE Transactions on 31.8 (2009): 1458-1471. © 2009 IEEE.
Version
Final published version
Abstract
We introduce a novel variational method for the extraction of objects with either bilateral or rotational symmetry in the presence of perspective distortion. Information on the symmetry axis of the object and the distorting transformation is obtained as a by-product of the segmentation process. The key idea is the use of a flip or a rotation of the image to segment as if it were another view of the object. We call this generated image the symmetrical counterpart image. We show that the symmetrical counterpart image and the source image are related by planar projective homography. This homography is determined by the unknown planar projective transformation that distorts the object symmetry. The proposed segmentation method uses a level-set-based curve evolution technique. The extraction of the object boundaries is based on the symmetry constraint and the image data. The symmetrical counterpart of the evolving level-set function provides a dynamic shape prior. It supports the segmentation by resolving possible ambiguities due to noise, clutter, occlusions, and assimilation with the background. The homography that aligns the symmetrical counterpart to the source level-set is recovered via a registration process carried out concurrently with the segmentation. Promising segmentation results of various images of approximately symmetrical objects are shown.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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/tpami.2008.160
