[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

142 A. Babiarz, R. Bieda, and K. Jaskot
Fig. 3. Schematic diagram of the steps of learning phase of the vision algorithm
The binary image is then a subject to the flood fill algorithm, which marks all
distinct objects on the field with distinct labels. It also performs calculations of
mass center, main axes of inertia and bounding box for every object. Next the
smallest objects are sorted out and only seven biggest ones are taken into account
in further processing. As a next step the additional constraint is imposed on these
objects - they have to consist of at least 25 pixels. This limit comes from practical
measurements showing that the smallest object on a field, the ball, consists of at
least 50 pixels, and all typical false objects ("noise") left on binary image consist
of at most 10 pixels.
All further processing is done only for pixels being inside of bounding boxes
and belonging to the particular object (background pixels are not affected in any
way) calculated by flood fill algorithm. The first operation performed on these
regions is RGB median filtering. This operation can be described as replacing
similar colors with the dominant color, which in turn improves the quality of histogram
based classification algorithm. The next step of the algorithm is calculation
of color RGB histograms for each region. As usual, in order to speed up the calculations,
the colors are quantized from 255 levels per component (R, G, B) to 4
levels. So the number of distinct colors recognized by the histogram is reduced
from 16.7 mln to 64 which has proved to be very sufficient.
The histograms obtained are used for classifying objects they represent to 7
classes (ball and 6 unique robots belonging to 2 teams). Classification is done by
NN (nearest neighbour) algorithm which compares each histogram with set of
reference histograms representing the 7 classes mentioned above (one reference
histogram is stored for one particular class). The histogram intersection function
[2], [3] serves here as a metric, required by NN method in order to measure a distance
between two histograms. Experiments proved that this metric provides quality
of classification similar to quadratic cross-distance at the computational cost of
bin-to-bin Euclidean metric.