Abstract
We present an ellipse finding and fitting algorithm that uses points and tangents, rather than just points, as the basic unit of information. These units are analyzed in a hierarchy: points with tangents are paired into triangles in the first layer and pairs of triangles in the second layer vote for ellipse centers. The remaining parameters are estimated via robust linear algebra: eigen-decomposition and iteratively reweighed least squares. Our method outperforms the state-of-the-art approach in synthetic images and microscopic images of cells.
| Original language | English (US) |
|---|---|
| Title of host publication | 2014 IEEE International Conference on Image Processing, ICIP 2014 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 3626-3630 |
| Number of pages | 5 |
| ISBN (Print) | 9781479957514 |
| DOIs | |
| State | Published - Jan 28 2014 |
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Keywords
- cell counting
- ellipse detection
- ellipse fitting
- image analysis
- pattern recognition
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
Cite this
Ellipses from triangles. / Cicconet, M.; Gunsalus, K.; Geiger, D.; Werman, M.
2014 IEEE International Conference on Image Processing, ICIP 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 3626-3630 7025736.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Ellipses from triangles
AU - Cicconet, M.
AU - Gunsalus, K.
AU - Geiger, D.
AU - Werman, M.
PY - 2014/1/28
Y1 - 2014/1/28
N2 - We present an ellipse finding and fitting algorithm that uses points and tangents, rather than just points, as the basic unit of information. These units are analyzed in a hierarchy: points with tangents are paired into triangles in the first layer and pairs of triangles in the second layer vote for ellipse centers. The remaining parameters are estimated via robust linear algebra: eigen-decomposition and iteratively reweighed least squares. Our method outperforms the state-of-the-art approach in synthetic images and microscopic images of cells.
AB - We present an ellipse finding and fitting algorithm that uses points and tangents, rather than just points, as the basic unit of information. These units are analyzed in a hierarchy: points with tangents are paired into triangles in the first layer and pairs of triangles in the second layer vote for ellipse centers. The remaining parameters are estimated via robust linear algebra: eigen-decomposition and iteratively reweighed least squares. Our method outperforms the state-of-the-art approach in synthetic images and microscopic images of cells.
KW - cell counting
KW - ellipse detection
KW - ellipse fitting
KW - image analysis
KW - pattern recognition
UR - http://www.scopus.com/inward/record.url?scp=84949927568&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84949927568&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2014.7025736
DO - 10.1109/ICIP.2014.7025736
M3 - Conference contribution
AN - SCOPUS:84949927568
SN - 9781479957514
SP - 3626
EP - 3630
BT - 2014 IEEE International Conference on Image Processing, ICIP 2014
PB - Institute of Electrical and Electronics Engineers Inc.
ER -