In this paper, we present a robust method for
nonisotropic point light
source calibration through feature points selection. By analyzing the
relationship between the observed surface and its
image intensity under
near-field lighting, the
feature points selection
method is first developed to effectively address the noisy
observations and improve calibration robustness.
Afterward, to enhance
efficiency and accuracy of the calibration, a cost function of l
p
-norm is established based on the
above relationship, and an improved Newton method-based iteration
process is applied to calculate the light source parameters. The
simulations demonstrate that the proposed method is capable of
achieving robust calibration results with the estimation error less
than 2.7 mm and 0.8°, even though the image intensities are
corrupted by Gaussian white noise with standard deviation up to 0.4.
The experimental validation is performed using a self-designed
photometric stereo system, where the calibration of point light
sources is conducted, and measurements are taken on a standard sphere
and compressor blade based on the obtained calibration results, which
demonstrates the effectiveness of what we believe to be a new
method.