  Coverage of image features play an important role in many vision algorithms
since their distribution affect the estimated homography. This paper presents a
Genetic Algorithm (GA) in order to select the optimal set of features yielding
maximum coverage of the image which is measured by a robust method based on
spatial statistics. It is shown with statistical tests on two datasets that the
metric yields better coverage and this is also confirmed by an accuracy test on
the computed homography for the original set and the newly selected set of
features. Results have demonstrated that the new set has similar performance in
terms of the accuracy of the computed homography with the original one with an
extra benefit of using fewer number of features ultimately reducing the time
required for descriptor calculation and matching.
