Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Advances in Visual Computing |
| Untertitel | 5th International Symposium, ISVC 2009, Proceedings |
| Seiten | 196-207 |
| Seitenumfang | 12 |
| Auflage | PART 2 |
| Publikationsstatus | Veröffentlicht - 2009 |
| Veranstaltung | 5th International Symposium on Advances in Visual Computing, ISVC 2009 - Las Vegas, NV, USA / Vereinigte Staaten Dauer: 30 Nov. 2009 → 2 Dez. 2009 |
Publikationsreihe
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Nummer | PART 2 |
| Band | 5876 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (elektronisch) | 1611-3349 |
Abstract
In this paper we analyze numerical optimization procedures in the context of level set based image segmentation. The Chan-Vese functional for image segmentation is a general and popular variational model. Given the corresponding Euler-Lagrange equation to the Chan-Vese functional the region based segmentation is usually done by solving a differential equation as an initial value problem. While most works use the standard explicit Euler method, we analyze and compare this method with two higher order methods (second and third order Runge-Kutta methods). The segmentation accuracy and the dependence of these methods on the involved parameters are analyzed by numerous experiments on synthetic images as well as on real images. Furthermore, the performance of the approaches is evaluated in a segmentation benchmark containing 1023 images. It turns out, that our proposed higher order methods perform more robustly, more accurately and faster compared to the commonly used Euler method.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
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Advances in Visual Computing: 5th International Symposium, ISVC 2009, Proceedings. PART 2. Aufl. 2009. S. 196-207 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 5876 LNCS, Nr. PART 2).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Analysis of numerical methods for level set based image segmentation
AU - Scheuermann, Björn
AU - Rosenhahn, Bodo
PY - 2009
Y1 - 2009
N2 - In this paper we analyze numerical optimization procedures in the context of level set based image segmentation. The Chan-Vese functional for image segmentation is a general and popular variational model. Given the corresponding Euler-Lagrange equation to the Chan-Vese functional the region based segmentation is usually done by solving a differential equation as an initial value problem. While most works use the standard explicit Euler method, we analyze and compare this method with two higher order methods (second and third order Runge-Kutta methods). The segmentation accuracy and the dependence of these methods on the involved parameters are analyzed by numerous experiments on synthetic images as well as on real images. Furthermore, the performance of the approaches is evaluated in a segmentation benchmark containing 1023 images. It turns out, that our proposed higher order methods perform more robustly, more accurately and faster compared to the commonly used Euler method.
AB - In this paper we analyze numerical optimization procedures in the context of level set based image segmentation. The Chan-Vese functional for image segmentation is a general and popular variational model. Given the corresponding Euler-Lagrange equation to the Chan-Vese functional the region based segmentation is usually done by solving a differential equation as an initial value problem. While most works use the standard explicit Euler method, we analyze and compare this method with two higher order methods (second and third order Runge-Kutta methods). The segmentation accuracy and the dependence of these methods on the involved parameters are analyzed by numerous experiments on synthetic images as well as on real images. Furthermore, the performance of the approaches is evaluated in a segmentation benchmark containing 1023 images. It turns out, that our proposed higher order methods perform more robustly, more accurately and faster compared to the commonly used Euler method.
UR - http://www.scopus.com/inward/record.url?scp=72449205601&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-10520-3_18
DO - 10.1007/978-3-642-10520-3_18
M3 - Conference contribution
AN - SCOPUS:72449205601
SN - 364210519X
SN - 9783642105197
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 196
EP - 207
BT - Advances in Visual Computing
T2 - 5th International Symposium on Advances in Visual Computing, ISVC 2009
Y2 - 30 November 2009 through 2 December 2009
ER -