Details
| Original language | English |
|---|---|
| Pages (from-to) | 243-262 |
| Number of pages | 20 |
| Journal | International Journal of Computer Vision |
| Volume | 73 |
| Issue number | 3 |
| Publication status | Published - 25 Sept 2006 |
| Externally published | Yes |
Abstract
In this article we present the integration of 3-D shape knowledge into a variational model for level set based image segmentation and contour based 3-D pose tracking. Given the surface model of an object that is visible in the image of one or multiple cameras calibrated to the same world coordinate system, the object contour extracted by the segmentation method is applied to estimate the 3-D pose parameters of the object. Vice-versa, the surface model projected to the image plane helps in a top-down manner to improve the extraction of the contour. While common alternative segmentation approaches, which integrate 2-D shape knowledge, face the problem that an object can look very differently from various viewpoints, a 3-D free form model ensures that for each view the model can fit the data in the image very well. Moreover, one additionally solves the problem of determining the object's pose in 3-D space. The performance is demonstrated by numerous experiments with a monocular and a stereo camera system.
Keywords
- Pose estimation, Segmentation, Shape priors, Variational methods
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Computer Science(all)
- Artificial Intelligence
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In: International Journal of Computer Vision, Vol. 73, No. 3, 25.09.2006, p. 243-262.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Three-dimensional shape knowledge for joint image segmentation and pose tracking
AU - Rosenhahn, Bodo
AU - Brox, Thomas
AU - Weickert, Joachim
N1 - Funding information: We gratefully acknowledge funding by the German Research Foundation (DFG) under the projects Ro2497/1, We2602/1, and Cr250/1.
PY - 2006/9/25
Y1 - 2006/9/25
N2 - In this article we present the integration of 3-D shape knowledge into a variational model for level set based image segmentation and contour based 3-D pose tracking. Given the surface model of an object that is visible in the image of one or multiple cameras calibrated to the same world coordinate system, the object contour extracted by the segmentation method is applied to estimate the 3-D pose parameters of the object. Vice-versa, the surface model projected to the image plane helps in a top-down manner to improve the extraction of the contour. While common alternative segmentation approaches, which integrate 2-D shape knowledge, face the problem that an object can look very differently from various viewpoints, a 3-D free form model ensures that for each view the model can fit the data in the image very well. Moreover, one additionally solves the problem of determining the object's pose in 3-D space. The performance is demonstrated by numerous experiments with a monocular and a stereo camera system.
AB - In this article we present the integration of 3-D shape knowledge into a variational model for level set based image segmentation and contour based 3-D pose tracking. Given the surface model of an object that is visible in the image of one or multiple cameras calibrated to the same world coordinate system, the object contour extracted by the segmentation method is applied to estimate the 3-D pose parameters of the object. Vice-versa, the surface model projected to the image plane helps in a top-down manner to improve the extraction of the contour. While common alternative segmentation approaches, which integrate 2-D shape knowledge, face the problem that an object can look very differently from various viewpoints, a 3-D free form model ensures that for each view the model can fit the data in the image very well. Moreover, one additionally solves the problem of determining the object's pose in 3-D space. The performance is demonstrated by numerous experiments with a monocular and a stereo camera system.
KW - Pose estimation
KW - Segmentation
KW - Shape priors
KW - Variational methods
UR - http://www.scopus.com/inward/record.url?scp=33846871044&partnerID=8YFLogxK
U2 - 10.1007/s11263-006-9965-3
DO - 10.1007/s11263-006-9965-3
M3 - Article
AN - SCOPUS:33846871044
VL - 73
SP - 243
EP - 262
JO - International Journal of Computer Vision
JF - International Journal of Computer Vision
SN - 0920-5691
IS - 3
ER -