  A large number of different feature detectors has been proposed so far. Any
existing approach presents strengths and weaknesses, which make a detector
optimal only for a limited range of applications. A tool capable of selecting
the optimal feature detector in relation to the operating conditions is
presented in this paper. The input images are quickly analyzed to determine
what type of image transformation is applied to them and at which amount.
Finally, the detector that is expected to obtain the highest repeatability
under such conditions, is chosen to extract features from the input images. The
efficiency and the good accuracy in determining the optimal feature detector
for any operating condition, make the proposed tool suitable to be utilized in
real visual applications. %A large number of different feature detectors has
been proposed so far. Any existing approach presents strengths and weaknesses,
which make a detector optimal only for a limited range of applications. A large
number of different local feature detectors have been proposed in the last few
years. However, each feature detector has its own strengths ad weaknesses that
limit its use to a specific range of applications. In this paper is presented a
tool capable of quickly analysing input images to determine which type and
amount of transformation is applied to them and then selecting the optimal
feature detector, which is expected to perform the best. The results show that
the performance and the fast execution time render the proposed tool suitable
for real-world vision applications.
