Details
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Publisher | Springer Verlag |
| Pages | 664-672 |
| Number of pages | 9 |
| ISBN (print) | 3540407308, 9783540407300 |
| Publication status | Published - 2003 |
| Externally published | Yes |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 2756 |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
In this article we discuss the 2D-3D pose estimation problem of deformable 3D free-form contours. In our scenario we observe objects of any 3D shape in an image of a calibrated camera. Pose estimation means to estimate the relative position and orientation of the 3D object to the reference camera system. The object itself is modeled as free-form contour. The fusion of modeling free-form contours within the pose estimation problem is achieved by using the conformal geometric algebra: Free-form contours are modeled as unique entities with 3D Fourier descriptors and combined with an ICP (Iterative Closest Point) algorithm they are embedded in the pose problem. The modeling of object deformations within free-form pose estimation is achieved by a combination of adaptive kinematic chain segments within Fourier descriptors.
Keywords
- Fourier descriptors, Kinematic chains, Pose estimation
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
Cite this
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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag, 2003. p. 664-672 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2756).
Research output: Chapter in book/report/conference proceeding › Contribution to book/anthology › Research › peer review
}
TY - CHAP
T1 - Modeling adaptive deformations during free-form pose estimation
AU - Rosenhahn, Bodo
AU - Perwass, Christian
AU - Sommer, Gerald
PY - 2003
Y1 - 2003
N2 - In this article we discuss the 2D-3D pose estimation problem of deformable 3D free-form contours. In our scenario we observe objects of any 3D shape in an image of a calibrated camera. Pose estimation means to estimate the relative position and orientation of the 3D object to the reference camera system. The object itself is modeled as free-form contour. The fusion of modeling free-form contours within the pose estimation problem is achieved by using the conformal geometric algebra: Free-form contours are modeled as unique entities with 3D Fourier descriptors and combined with an ICP (Iterative Closest Point) algorithm they are embedded in the pose problem. The modeling of object deformations within free-form pose estimation is achieved by a combination of adaptive kinematic chain segments within Fourier descriptors.
AB - In this article we discuss the 2D-3D pose estimation problem of deformable 3D free-form contours. In our scenario we observe objects of any 3D shape in an image of a calibrated camera. Pose estimation means to estimate the relative position and orientation of the 3D object to the reference camera system. The object itself is modeled as free-form contour. The fusion of modeling free-form contours within the pose estimation problem is achieved by using the conformal geometric algebra: Free-form contours are modeled as unique entities with 3D Fourier descriptors and combined with an ICP (Iterative Closest Point) algorithm they are embedded in the pose problem. The modeling of object deformations within free-form pose estimation is achieved by a combination of adaptive kinematic chain segments within Fourier descriptors.
KW - Fourier descriptors
KW - Kinematic chains
KW - Pose estimation
UR - http://www.scopus.com/inward/record.url?scp=35248854357&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-45179-2_81
DO - 10.1007/978-3-540-45179-2_81
M3 - Contribution to book/anthology
AN - SCOPUS:35248854357
SN - 3540407308
SN - 9783540407300
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 664
EP - 672
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Verlag
ER -