Name
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICIP.2014.7025736 | Image Processing |
Keywords | Field | DocType |
computational geometry,curve fitting,eigenvalues and eigenfunctions,iterative methods,least squares approximations,object detection,parameter estimation,eigen-decomposition,ellipse detection,ellipse finding algorithm,ellipse fitting algorithm,image analysis,iteratively reweighed least squares,parameter estimation,pattern recognition,points-with-tangents,robust linear algebra,cell counting,ellipse detection,ellipse fitting,image analysis,pattern recognition | Least squares,Fitting algorithm,Linear algebra,Combinatorics,Pattern recognition,Tangent,Artificial intelligence,Ellipse,Geometry,Mathematics | Conference |
ISSN | Citations | PageRank |
1522-4880 | 3 | 0.38 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marcelo Cicconet | 1 | 3 | 0.38 |
| Kristin C. Gunsalus | 2 | 9 | 1.19 |
| Davi Geiger | 3 | 1050 | 353.66 |
| Michael Werman | 4 | 3 | 0.38 |