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Fully automated computational approaches to simultaneously estimate:
- the unknown nonlinear response function of the camera;
- the unknown overall change in gain caused by automatic exposure control
or AGC; and
- the projective coordinate transformation relating the images to one
another,
were presented (implemented and shown) in [2], and further explored
in [3] using both parametric and nonparametric methods.
A goal of this work was to
combine variable gain image sequences into a single
image of increased spatiotonal range and definition,
as well as for a front end to a wearable vision system.
More recently, Szeliski also considered the problem of estimating only the
projective coordinate transformation between images [4],
while Debevec and Malik [5] have considered the problem of
estimating only the camera response function.
Mitsunaga and Nayar have also considered the problem of estimating the
response function using a low order polynomial [6].
Mann has also considered parametric estimates
of the camera response function, by proposing a simple three parameter
function that provides a very good fit to most camera response
functions [7].
This paper concentrates on nonparametric determination of camera response
functions. The problem of nonparametric
reverse engineering a camera's response function, from differently exposed
images of identical or overlapping subject matter,
was first proposed and first solved in [2]. In this paper we present, in detail,
such a computationally efficient maximum likelihood
estimation based on least squares.
Next: Quantimetric imaging
Up: . Introduction: Variable gain image
Previous: . Introduction: Variable gain image
Steve Mann
2002-05-25