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Issue No.11 - November (1997 vol.19)
pp: 1185-1198
ABSTRACT
<p><b>Abstract</b>—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 infeasible. The new approach exploits a resampling technique recently introduced in statistics, the bootstrap. Distributions for the output variables are obtained by perturbing the <it>nuisance properties</it> 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 <it>given</it> 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.</p>
INDEX TERMS
Performance evaluation, edge detection, bootstrap.
CITATION
Kyujin Cho, Peter Meer, Javier Cabrera, "Performance Assessment Through Bootstrap", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.19, no. 11, pp. 1185-1198, November 1997, doi:10.1109/34.632979
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