Empirical mode decomposition on skeletonization pruning

This paper presents a novel skeleton pruning approach based on a 2D empirical mode like decomposition (EMD-like). The EMD algorithm can decompose any nonlinear and non-stationary data into a number of intrinsic mode functions (IMFs). When the object contour is decomposed by empirical mode like decom...

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Bibliographic Details
Published in:Image and vision computing Vol. 31; no. 8; pp. 533 - 541
Main Authors: Krinidis, Stelios, Krinidis, Michail
Format: Journal Article
Language:English
Published: Elsevier B.V 01.08.2013
Subjects:
Algorithms
Decomposition
Disks
Empirical analysis
Empirical mode decomposition
Ensemble empirical mode decomposition
Intrinsic mode
Nonlinearity
Pruning
Shape
Skeleton
Skeletonization
Pruning
Empirical mode decomposition
Intrinsic mode
Skeleton
Skeletonization
Ensemble empirical mode decomposition
ISSN:0262-8856, 1872-8138
Online Access:Get full text
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Summary:This paper presents a novel skeleton pruning approach based on a 2D empirical mode like decomposition (EMD-like). The EMD algorithm can decompose any nonlinear and non-stationary data into a number of intrinsic mode functions (IMFs). When the object contour is decomposed by empirical mode like decomposition (EMD-like), the IMFs of the object provide a workspace with very good properties for obtaining the object's skeleton. The theoretical properties and the performed experiments demonstrate that the obtained skeletons match to hand-labeled skeletons provided by human subjects. Even in the presence of significant noise and shape variations, cuts and tears, the resulted skeletons have the same topology as the original skeletons. In particular, the proposed approach produces no spurious branches as many existing skeleton pruning methods and moreover, does not displace the skeleton points, which are all centers of maximal disks. •Usage of Ensemble Empirical Mode Decomposition (EEMD) on object skeletonization•Modeling of object contour with a 2D EEMD-like procedure•The produced IMFs provide a very good and novel workspace for image skeletonization.•The proposed method is fully automated and unsupervised.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:0262-8856
1872-8138
DOI:10.1016/j.imavis.2013.04.005