Milestones
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2
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3
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3. Implementation of String Alignment methods to match boundary information
The information strings that are the result of the computations in the second step are now used
to compute an alignment score that gives a hint on the silmilarity of images. For this purpose
several alignment algorithms have to be implemented.
a) Alignment algorithms
SA_extract returns the
boundaries and strings for a given image. To get a similarity score for two of these strings the
following methods are implemented:
SA_globalAlignment,
SA_localAlignment,
SA_repeatedMatchesAlignment and
SA_overlapMatchesAlignment.
These functions compute a similarity score for a possible alignment using different methods. These
four methods are subject to further testing to see which one gives the best result.
b) Visualisation of alignments
The mentioned functions also return an encoded alignment that allows to reconstruct the
optimal alignment. This is then used in the function
SA_visualizeAlignment to show the alignment
in an image together with the boundaries. The following picture shows an example of such a
visualisation:

The red and blue lines represent the boundaries from which the information strings where extracted.
The dots show the positions at which the polarscales where used. The green lines show which points
were aligned.
SA_visualizeAlignment
can now be used to adjust the parameter of the alignment algorithms and to see which algorithm
leads to the best results.
c) Alignment test function
To make the procedure of adjusting the alignment parameter easier the function
SA_alignmentTest can be used.
d) Creation of an alignment score matrix
The final result of the alignments should be a matrix of scores in wich all elements of
two lists of strings (from a list of test images) are aligned to each other. This matrix
is returned by the function
SA_alignmentMatrix.
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