Incremental level set tracking

Shay Dekel; Nir Sochen; Shai Avidan

doi:10.1007/978-3-642-34141-0_18

Incremental level set tracking

Shay Dekel, Nir Sochen, Shai Avidan

Tel Aviv University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

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 language	English
Title of host publication	Mathematics and Visualization
Editors	Michael BreuB, Petros Maragos, Alfred Bruckstein
Publisher	Springer Heidelberg
Pages	407-420
Number of pages	14
ISBN (Electronic)	9783642341410
ISBN (Print)	9783319912738, 9783540250326, 9783540250760, 9783540332749, 9783540886051, 9783642150135, 9783642216077, 9783642231742, 9783642273421, 9783642341403, 9783642341403, 9783642543005
DOIs	https://doi.org/10.1007/978-3-642-34141-0_18
State	Published - 2013
Externally published	Yes
Event	Dagstuhl Workshop on Innovations for Shape Analysis: Models and Algorithms, 2011 - Dagstuhl, Germany Duration: 3 Apr 2011 → 8 Apr 2011

Publication series

Name	Mathematics and Visualization
Volume	0
ISSN (Print)	1612-3786
ISSN (Electronic)	2197-666X

Conference

Conference	Dagstuhl Workshop on Innovations for Shape Analysis: Models and Algorithms, 2011
Country/Territory	Germany
City	Dagstuhl
Period	3/04/11 → 8/04/11

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2013.

Access to Document

10.1007/978-3-642-34141-0_18

Cite this

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title = "Incremental level set tracking",

abstract = "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.",

author = "Shay Dekel and Nir Sochen and Shai Avidan",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2013.; Dagstuhl Workshop on Innovations for Shape Analysis: Models and Algorithms, 2011 ; Conference date: 03-04-2011 Through 08-04-2011",

year = "2013",

doi = "10.1007/978-3-642-34141-0_18",

language = "אנגלית",

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Dekel, S, Sochen, N & Avidan, S 2013, Incremental level set tracking. in M BreuB, P Maragos & A Bruckstein (eds), Mathematics and Visualization. Mathematics and Visualization, vol. 0, Springer Heidelberg, pp. 407-420, Dagstuhl Workshop on Innovations for Shape Analysis: Models and Algorithms, 2011, Dagstuhl, Germany, 3/04/11. https://doi.org/10.1007/978-3-642-34141-0_18

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Y1 - 2013

N2 - 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.

AB - 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.

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Incremental level set tracking

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