  This paper presents a technique for finding the surface normal of an object
from a set of images obtained under different lighting positions. The method
presented is based on the principles of Photometric Stereo (PS) combined with
Optimum Experimental Design (OED) and Parameter Estimation (PE). Unclear by the
approach of photometric stereo, and many models based thereon, is how to
position the light sources. So far, this is done by using heuristic approaches
this leads to suboptimal and non-data driven positioning of the light sources.
But what if the optimal positions of the light sources are calculated for
photometric stereo? To this end, in this contribution, the effect of
positioning the light sources on the quality of the normal vector for PS is
evaluated. Furthermore, a new approach in this direction is derived and
formulated. For the calculation of the surface normal of a Lambertian surface,
the approach based on calibrated photometric stereo; for the estimation the
optimal position of the light sources the approach is premised on parameter
estimation and optimum experimental design. The approach is tested using
synthetic and real-data. Based on results it can be seen that the surface
normal estimated with the new method is more detailed than with conventional
methods.
