Name
Abstract | ||
|---|---|---|
We describe a new method of achieving autocalibration that uses a stochastic optimization approach taken from the field of evolutionary computing and we perform a number of experiments on standardized data sets that show the effectiveness of the approach. The basic assumption of this method is that the internal (intrinsic) camera parameters remain constant throughout the image sequence, i.e. they are taken from the same camera without varying the focal length. We show that for the autocalibration of focal length and aspect ratio, the evolutionary method achieves comparable results without the implementation complexity of other methods. Autocalibrating from the fundamental matrix is simply transformed into a global minimization problem utilizing a cost function based on the properties of the fundamental matrix and the essential matrix. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1007/3-540-46004-7_29 | EvoWorkshops |
Keywords | Field | DocType |
stochastic optimization approach,evolutionary computing,camera parameter,focal length,aspect ratio,evolutionary method,essential matrix,new method,fundamental matrix,basic assumption,cost function,stochastic optimization | Data set,Mathematical optimization,Stochastic optimization,Essential matrix,Evolutionary algorithm,Computer science,Evolutionary computation,Focal length,Stochastic programming,Fundamental matrix (computer vision) | Conference |
Volume | ISSN | ISBN |
2279 | 0302-9743 | 3-540-43432-1 |
Citations | PageRank | References |
3 | 0.46 | 10 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anthony Whitehead | 1 | 143 | 20.84 |
| Gerhard Roth | 2 | 13 | 2.99 |