Abstract
In this paper, we are concerned with the problem of deciding whether a fitted model accurately describes the data to which it has been fitted. We present an effective method of testing the lack-of-fit of a parametric model to data, with applications to computer vision. Our test is important to the computer vision community in two ways:
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We assume a broad enough class of distributions as to be essentially distribution independent.
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The test requires no knowledge of the sensor noise level.
We present results of experiments that compare the test with the standard χ2 statistic. The experiments are designed to represent typical vision tasks, namely feature tracking and segmentation. We show that our test is more sensitive than the χ2 unless the noise level is perfectly known.
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© 1994 Springer-Verlag Berlin Heidelberg
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Fitzgibbon, A.W., Fisher, R.B. (1994). Lack-of-fit detection using the run-distribution test. In: Eklundh, JO. (eds) Computer Vision — ECCV '94. ECCV 1994. Lecture Notes in Computer Science, vol 801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0028348
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DOI: https://doi.org/10.1007/BFb0028348
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