[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

Recognition and Location of Objects in the Visual Field of a UAV Vision System 37
classifier in the extended 1-dimensional features space is constructed,
where for each of 1 of training vectors is placed on the “positive” side of
the hyperplane. This is the algorithm that is named the Kesler construction.
5 Experimental Researches
In the tests related to the classification that were carried out, four groups of objects
forming for classes consisting of 15, 1, … 4 training vectors in each of
class. Picture 4 shows the analyzed pictorial scenes containing objects, , on the
basis of which a training set of 60 abundance was created.
a. b.
c. d.
Fig. 4. Pictures containing the objects creating the training set of the classified: a. class -
squares, b. class -circles, c. class triangles, d. -crosses
Multiple tests carried out on the training set constructed in such a way, did not
resulted in a fully satisfactory outcome. In the iterative Rosenblatt algorithms for
the problem of the 4 independent perceptrons and for the Kesler construction
a correct solution was not obtained. Regardless of the choice of the starting point
0 and the value of the training factor , the algorithms did not find the
hyperplane (-y), for which all of the training actors of the set were correctly
classified. This problem is probably the outcome of the assumptions failure, which
were required in the construction of the perceptron, and which relate to the linear
separation of the classes in the features dimension. Further investigation into the
authenticity of such an assumption is pretty difficult due to the large dimension of
the features’ space l 22.