[Studies in Computational Intelligence 481] Artur Babiarz, Robert Bieda, Karol Jędrasiak, Aleksander Nawrat (auth.), Aleksander Nawrat, Zygmunt Kuś (eds.) - Vision Based Systemsfor UAV Applications (2013, Sprin

44 R. Bieda et al.
where and are respectively the covariance matrix and the mean class vector
, 1,…,4, designated for vectors forming a class after the FLD transformation.
The process of the classification with the use of function (44) processes
as follows : Feature vector belongs to class when:
max , ,…, , (43)
In case, where the zoom ratio will be set on a bit lover level ( for e.g. 96%)
then the dimension of feature vectors after FLD transformation will reduce itself
to the one-dimension space ( 1). The arrangement of the classes can be described
as an outcome of projection of the plane on picture 6b on axis. In
this case, the classification process can be carried out on the basis of the minimizing
the differences between the feature and the mean value of this feature for
the particular classes:
min | | (44)
,..
The outcome of the activity of the classifier which bases on the above example is
show on Fig 7.
a. b.
Fig. 7. The outcome of the classification in the dimension of the features after the FLD
transformation : a. case 2D b. case 1D
As it can be noticed in Fig 7 in both cases the outcome of the classification is
correct. This gives hope for the construction of a autonomic visual system. Such
system, ‘pre-learned” is able to independently analyze the image of the surroundings
of the manipulator and send him the data needed to completion of the overriding
task.
8 Summary
The results obtained show that the construction of the co-operating classifier and
the visual system is possible. The obtained results are promising and appear to