Department of Electrical and Computer Engineering
Rutgers University, Piscataway, NJ 08854-8058, USA
The bootstrap is a numerical technique, with solid theoretical
foundations, to obtain statistical measures about the quality of
an
estimate by using only the available data. Performance assessment
through bootstrap provides the same or better accuracy than the
traditional error propagation approach, most often without
requiring
complex analytical derivations. In many computer vision tasks a
regression problem in which the measurement errors are point
dependent
has to be solved. Such regression problems are called
heteroscedastic
and appear in the linearization of quadratic forms
in ellipse fitting and epipolar geometry, in camera
calibration, or in 3D rigid motion estimation. The performance of
these complex vision tasks is difficult to evaluate analytically,
therefore we propose in this paper the use of bootstrap.
The technique is illustrated for 3D rigid
motion and fundamental matrix estimation. Experiments with real
and synthetic data show the validity of bootstrap as an
evaluation tool and the importance of taking the
heteroscedasticity into account.
Vision Algorithms: Theory and Practice ,
Lecture Notes in Computer Science,
B. Triggs, A. Zisserman, R. Szeliski (Eds),
Springer, 2000, 236-252.
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