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@@ -633,23 +633,43 @@ to help out. Further communication usually went through e-mails and replies
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were instantaneous! A crew to remember.
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\section{Discussion}
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+
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+There are a few points open for improvement. These are the following.
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+
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We had some good results but of course there are more things to explore.
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-For instance we did a research on three different patterns. There are more patterns
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-to try. For instane we only tried (8,3)-, (8,5)- and (12,5). The interesting to
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-do is to test which pattern gives the best result. This is also done by grid-
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-searching, changing the size of circle and the amount of neighbours.
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+For instance we did a research on three different patterns. There are more
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+patterns to try. For instance we only tried (8,3)-, (8,5)- and
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+(12,5)-neighbourhoods. What might be done is to test which pattern gives the
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+best result, for a wider range of neighbourhoods. We haven proven that the size
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+and number of points do influence the performance of the classifier, so further
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+research would be in place.
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One important feature of our framework is that the LBP class can be changed by
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-an other technique. This may be a different algorithm than LBP. Also the classifier
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-can be changed in an other classifier. By applying these kind of changes we can
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-find the best way to recognize licence plates.
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-
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-We don't do assumption when a letter is recognized. For instance dutch licence plates
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-exist of three blocks, two digits or two characters. Or for the new licence plates
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-there are three blocks, two digits followed by three characters, followed by one or
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-two digits. The assumption we can do is when there is have a case when one digit
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-is moste likely to follow by a second digit and not a character. Maybe these assumption
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-can help in future research to achieve a higher accuracy rate.
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+an other technique. This may be a different algorithm than LBP. Also the
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+classifier can be changed in an other classifier. By applying these kind of
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+changes we can find the best way to recognize licence plates.
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+
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+We don't do assumption when a letter is recognized. For instance Dutch licence
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+plates exist of three blocks, two digits or two characters. Or for the new
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+licence plates there are three blocks, two digits followed by three characters,
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+followed by one or two digits. The assumption we can do is when there is have a
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+case when one digit is most likely to follow by a second digit and not a
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+character. Maybe these assumption can help in future research to achieve a
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+higher accuracy rate.
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+
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+A possibility to improve the performance speedwise would be to separate the
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+creation of the Gaussian kernel and the convolution. This way, the kernel can
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+be cached, which is a big improvement. At this moment, we calculate this kernel
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+every time a blur is applied to a character. This was done so we could use a
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+standard Python function, but we realised too late that there is performance
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+loss due to this.
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+
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+Another performance loss was introduced by checking for each pixel if it is
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+in the image. This induces a lot of function calls and four conditional checks
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+per pixel. A faster method would be to first set a border of black pixels around
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+the image, so the inImage function is now done implicitly because it simply
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+finds a black pixel if it falls outside the original image borders.
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+
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\appendix
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Jayke Meijer