Lack-of-fit Detection using the Run-distribution Test

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: We assume a broad enough class of distributions as to be essentially distribution independent. The test requires no knowledge of the sensor noise level.