Incremental level set tracking

Shay Dekel, Nir Sochen, Shai Avidan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


We consider the problem of contour tracking in the level set framework. Level set methods rely on low level image features, and very mild assumptions on the shape of the object to be tracked. To improve their robustness to noise and occlusion, one might consider adding shape priors that give additional weight to contours that are more likely than others. This works well in practice, but assumes that the class of object to be tracked is known in advance so that the proper shape prior is learned. In this work we propose to learn the shape priors on the fly. That is, during tracking we learn an eigenspace of the shape contour and use it to detect and handle occlusions and noise. Experiments on a number of sequences reveal the advantages of our method.

Original languageEnglish
Title of host publicationMathematics and Visualization
EditorsMichael BreuB, Petros Maragos, Alfred Bruckstein
PublisherSpringer Heidelberg
Number of pages14
ISBN (Electronic)9783642341410
ISBN (Print)9783319912738, 9783540250326, 9783540250760, 9783540332749, 9783540886051, 9783642150135, 9783642216077, 9783642231742, 9783642273421, 9783642341403, 9783642341403, 9783642543005
StatePublished - 2013
Externally publishedYes
EventDagstuhl Workshop on Innovations for Shape Analysis: Models and Algorithms, 2011 - Dagstuhl, Germany
Duration: 3 Apr 20118 Apr 2011

Publication series

NameMathematics and Visualization
ISSN (Print)1612-3786
ISSN (Electronic)2197-666X


ConferenceDagstuhl Workshop on Innovations for Shape Analysis: Models and Algorithms, 2011

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2013.


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