  The first contribution of this paper is architecture of a multipurpose
system, which delegates a range of object detection tasks to a classifier,
applied in special grid positions of the tested image. The second contribution
is Gray Level-Radius Co-occurrence Matrix, which describes local image texture
and topology and, unlike common second order statistics methods, is robust to
image resolution. The third contribution is a parametrically controlled
automatic synthesis of unlimited number of numerical features for
classification. The fourth contribution is a method of optimizing parameters C
and gamma in LibSVM-based classifier, which is 20-100 times faster than the
commonly applied method. The work is essentially experimental, with
demonstration of various methods for definition of objects of interest in
images and video sequences.
