Master Thesis - Fachbereich Informatik

138 APPENDIX A. PROFILE ANALYSIS IMPLEMENTATION DETAILS
A.2. Profile Subsampling
In many computer vision tasks it is common to perform a specific operation on lower
resolution images than the input to increase computation speed. For example one could
simply discard every second row or column two obtain an image of half size of the original
image. However, to avoid a violation of the sampling theorem it is important to apply
a low-pass filter operation on the data before. This mechanism can be used to generate
pyramids of images at different resolutions or scales. Each layer in the pyramid has half
the size of the layer above with the top layer corresponding to the original size. Before
subsampling the data a Gaussian smoothing operation is performed to suppress higher
frequencies. Thus, such pyramids are called Gaussian Pyramids in the literature [24].
The same can be applied to one-dimensional signals such as gray level profiles. In
this application, experiments have shown the information about the tube boundaries is
conserved a coarser scale. Thus, a subsampled version two levels down the pyramid instead
of the original profile is used in praxis. The data to be processed after this step is only
a fourth of the input. Obviously, the profile analysis can be accelerated by this step.
Experiments investigating whether the profile subsampling could replace step one in the
profile analysis, i.e. the smoothing with a large mean kernel, came to the conclusion that
in connection with transparent tubes and dark printing, the strong contrast of the letters
could be misclassified as tube boundary. The system tries to detect the real tube location
in a certain region around the wrong position and is likely to fail. The mean filter instead
is able to reduce the influence of the lettering and must not be replaced.
A.3. Scan Lines
As mentioned in Section 4.4.2, the profile to be evaluated is based on the normalized sum
of Nscan scan lines equally distributed over the global ROI. The reason why a single scan
line is not sufficient is shown in Figure A.1(b). Three sample profiles at different heights
(61, 80 and 100) are selected to visualize the influence of the printing. One can see the
strong contrast at the letters as well as a poor contrast at the right tube boundary. Since
it is non-deterministic of whether the printing of a particular tube is visible in an image,
one has to consider the worst case. This is a scan line passing through the printing at as
many positions possible. The global mean of the resulting profile is much lower in this case
and it is possible that the intensity of the tube at regions outside the printing is wrongly
classified as background. The result of this effect is shown in Figure A.1(d). On the other
hand, the usage of several scan lines decreases the influence of the printing significantly.
The probability that more than a few scan lines will pass through the printing is low.
For example, among the sample tubes used for testing of the prototype, the coverage of
the printing is about 16% with respect to the diameter. Thus, it is very likely to have
more than one scan line passing through tube regions without printing. In total, the
influence of the printing decreases with the number of scan lines. However, Figure A.1(c)
shows 11 scan lines equally distributed over the global ROI in y-direction are sufficient
to yield almost equal results as with considering all rows of the ROI. Here, the profile
consisting of 11 scan lines is shifted, i.e. the intensity values are lower compared to the
profile calculated from all ROI rows (90 in this example). This is due to the location of