Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
526322 | Computer Vision and Image Understanding | 2009 | 11 Pages |
Abstract
In addition to computing the tensor from the cameras, we also investigate how it can be further optimized relative to error measures in the 3D and 2D spaces. This optimization is evaluated for sets of real 3DÂ +Â 2DÂ +Â 2D data by comparing the reconstruction to some of the triangulation methods found in the literature, in particular the so-called optimal method that minimizes 2D L2 errors. The general conclusion is that, depending on the choice of error measure and the optimization implementation, it is possible to find a tensor that produces smaller 3D errors (both L1 and L2) but slightly larger 2D errors than the optimal method does. Alternatively, we may find a tensor that gives approximately comparable results to the optimal method in terms of both 3D and 2D errors. This means that the proposed tensor based method of triangulation is both computationally efficient and can be calibrated to produce small reconstruction or reprojection errors for a given data set.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Klas Nordberg,