determined to be 1;;75pixel, h;;715pixel, and Aq? = 0.79. From
this information we can conclude that the two stamps are nearly
perfectly aligned regarding the orientation but only poorly
aligned regarding the position particularly in the direction per-
pendicular to the direction of the script. It took about 20 ms on a
2 GHz Pentium 4 to search the print in the entire image of size
652x494 allowing a 360? rotation. The 20 ms can be completely
attributed to the search of the root part. The search of the second
part was too fast to be measured. If we would search the second
part separately it would also take 20 ms to find it. Therefore, by
using our hierarchical model the recognition time was reduced
to 50%.
In the second example the variations of another writing are
analyzed (see Figure 10). The resulting search tree in the lower
right image of Figure 10 visualizes the resulting optimum
search strategy. The result is to search each letter relatively to
its neighboring letter, which corresponds to our intuition.
Figure 10. The variations in the writing are analyzed and used to
build the hierarchical model. The calculated search tree is
visualized in the lower right image, where each letter is
searched relative to its neighboring letter, as we would expect.
It took 60 ms to search the entire hierarchical model in a
512x512 image allowing a 180° rotation. 50 ms can be
attributed to the search of the root part and only 10 ms to the
search of all other parts. In a comparison to a complete search
of all parts in the entire image (450 ms) this is a reduction to
1396.
5. CONCLUSIONS
In this paper we presented an approach for hierarchical auto-
matic object decomposition for object recognition. This is use-
ful when searching for objects that consist of several parts that
can move relative to each other, which often happens in
industry, for example. A hierarchical model was automatically
created using several example images that show the relative
movements of the single parts. This model can be utilized to
efficiently search for the object in an arbitrary image. The
examples shown in section 4 emphasize the high potential of
our novel approach.
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