Kyujin Cho(1), Peter Meer(1) and Javier Cabrera(2)
(1)Department of Electrical and Computer Engineering
(2)Department of Statistics
Rutgers University, Piscataway, NJ 08855, USA
A new performance evaluation paradigm for computer vision
systems is proposed. In real situation, the complexity of the input
data and/or of the computational procedure can make traditional error
propagation methods unfeasible.
The new approach exploits a resampling technique recently
introduced in statistics, the boostrap.
Distributions for the output variables are obtained by perturbing the
nuisance properties of the input, i.e., properties with no
relevance for the output under ideal conditions.
From these bootstrap distributions, the confidence
in the adequacy of the assumptions
embedded into the computational procedure for the given input is
derived.
As an example, the new paradigm is applied to
the task of edge detection.
The performance of several edge detection methods is compared both for
synthetic data and real images.
The confidence in the output can be used to obtain an edgemap
independent of the gradient magnitude.
Appeared in
IEEE Trans. Pattern Anal. Machine Intell,
19, 1185-1198, 1997.
Return to Research: Bootstrap as a tool for computer vision
