Bachelor Thesis - Computer Graphics Group

measure, we get K nearest objects. The samples are grouped according to the
corresponding gesture pattern. The winner is the pattern with more than half
of the nearest samples. In case there is no winner, the recognition is reported
to be unsuccessful.
The distance measure will be calculated as the sum of Euclidean distances of
the corresponding points. To make the coordinates comparable, the shapes of
the gesture as well as all the pattern samples have to be normalized to the
same coordinate space after the preprocessing phase. The geometric center
of the shape will be in the middle of the coordinate space. An example of a
normalized shape can be seen in figure 2.5.
0.0 x 0.0
1.0 x 1.0
Figure 2.5: Normalized shape example
The results of the algorithm depend on the value of K. If the number of
samples per pattern is the same, there is no problem. However, when it is not,
the K has to be calculated somehow. We will use the most frequent number
of pattern samples. Patterns, which do not contain the required number of
samples, will not be recognized. On the other hand, the patterns containing
at least the sufficient number of patterns, will only use the first K patterns.
The other possible solution would be to take the minimum number of pattern
samples. However, this would handicap “well behaved” patterns.
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