Elektronika 2009-11.pdf - Instytut Systemów Elektronicznych

The achieved results can be explained easily. Very small
values of r (from 1 to 10) obviously gave poor recognition results,
because small number of bins in histogram leads to small
discrimination property. On the other hand, large number of
bins taken into consideration gave worse results as well. It can
be explained by the small number of points under processing
in the examined objects. The test contours had usually about
hundred points, which resulted in worse rates for higher values
of r. The important conclusion here is the necessity of usage
of shapes with higher number of points (at least several hundreds)
or usage of small number of bins in PDH descriptor.
As we can see in Fig. 2. the best results are achieved
when r is between 9 and 60. Therefore the exact analysis of
three performed tests will be presented for r ranging from 1 to
60. This will help in noticing the characteristics of the PDH descriptor
in the presence of various shape distortions.
A very interesting property can be noticed when small
number of bins is used. The recognition is far from ideal in that
case. However, even the small value of r gives surprisingly
good recognition rate, i.e. for r higher than 4 the described
factor is above 80%. That proves the good characteristics of
a histogram in pattern recognition.
The recognition results for rotation are presented in Fig. 3.,
and for scaling (down and up) in Fig. 4. As we can see, results
for rotation and scaling up are similar. The perfect result
(100%) is achieved several times. However, the scaling down
deformation is much more challenging - only for r between 13
and 20 the results are acceptable. After the parameter exceeds
20 they are getting worse. Again, it is a result of the
small number of pixels under processing.
Conclusions
In the paper the results of an experiment on deformed binary
trademarks were presented. The goal of the research was to
find the best values of numbers of bins (parameter r) in the
histogram when using Point Distance Histogram (PDH) for
small shapes. Three tests were performed. Firstly the randomly
rotated objects were explored, then, scaled down and
up. In each case, the influence of extraction of points from
bitmaps was noticeable as well.
As it turned out, small size of a test object, more precisely
- small number of points to process in a contour had the most
significant influence on recognition rates (RR). The results
were worse for scaling down (the best RR was equal to 90%)
than for scaling up and rotation (100% several times). The
analysis of the RR under varying values of parameter r indicated
that for small contours the best value is r = 18, 20 or 25.
This results primarily from the parameters established for scaling
down. In the case of another two tests - for rotation and
scaling up, the maximal suggested parameter value should
be r = 51, since for the higher values the results were worse.
To sum up, if we cannot ensure the appropriate size of
objects to recognise, the parameter r from 18 to 25 should
be used. If objects are larger the value close to 50 is a better
choice.
Another issue is worth noticing. During the experiments
the obvious property of PDH method was confirmed. It works
better for objects with larger number of points. Therefore, the
usage of larger contours, if possible, is advised. However, in
real situations sometimes the extracted shapes are small. In
that case the values established using the experiments presented
in this paper should be used.
References
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pp. 1-19.
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4A, 2007, pp. 90-93.
[3] Frejlichowski D.: The Point Distance Histogram for Analysis of
Erythrocyte Shapes. Polish Journal of Environmental Studies,
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[4] Kuchariew G., Przetwarzanie i analiza obrazów cyfrowych, Wydawnictwo
Uczelniane Politechniki Szczecińskiej, 1998.
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[6] Saykol E., Gudukbay U., Ulusoy O.: A histogram-based approach
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Open virtual steganographic laboratory
(Otwarte wirtualne laboratorium steganograficzne)
dr inż. PAWEŁ FORCZMAŃSKI, mgr inż. MICHAŁ WĘGRZYN
Zachodniopomorski Uniwersytet Technologiczny w Szczecinie, Wydział Informatyki
Among many different applications where data hiding techniques
can be used, one that has received huge attention in recent
years is steganography. In that scenario, not just the
embedded message is hidden, but the communication process
itself is tried to be concealed. Steganography is both art. and
science of undetectable communication [1]. Opposed to cryptography
which encrypts a message, steganography achieves
private communication by hiding message in cover object so
not only content is known but also very existence of message.
Throughout the history many different media types have been
used to hide information. Nowadays steganographic applications
[9,16] are using computer file system, transmission protocols,
audio files, video files, text files or images, which are the
most popular cover objects. Discovering the presence of hidden
messages and determining their attributes are goals of
steganalysis, which, in general, involves visual analysis and
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