Cem Ahmet MERCAN, 702002003
Special Topics in Computational Science and Engineering:
Generalized Hough Transform
Hough Transform very important technique for detection of features such as lines and
circles, and other image properties. This produces many research to extent to which the
method can be generalized so that it can detect arbitrary shapes. The work of Merlin and
Faber (1975) and Ballard (1981) was crucial historically and led to development of the
generalized Hough transform (GHT).
In GHT, a line is drawn from a reference point, usually the object centroid, to the boundary of
the object. At each boundary point (xcj ,ycj ), the gradient with respect to the horizontal image
axis is placed in a look-up table called the R-table. The accumulator array, which is the
parameter space formed by points that reveal potential features of interest in the prototype
object, can then be set up. For the test object, the gradient angle  and thus the index to the Rtable are obtained from each edge point (uj ,vj ). Next, the respective entries in the R-table
are used to compute possible locations of reference points in the parameter space using the
following update rule:
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Before these calculations, a preparation stage requires: firstly picture converted to gray-scale
image. Then, we can convert to binary image. To ensure correct edge detection this stage very
important and it must be very stable for different type of picture. Edge detection process is
very vital for success of GHT. Because both template arbitrary image and processing image
comes from this process.
This stage bloke me to complete this study. Because of the failure of edge detection or in
other word, unstable and image dependent characteristics of edge detection method that I
used, Images can not be prepared correctly.
I try to use medical images for this study like these:
First method that I try, static tresholding and Robert’s Cross Edge detection:
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After this method, I try Otsu Optimum tresholding method:
And finally, I use Dynamic (Local) tresholding method. It gives more suitable results but still
there is an stability problem:
At last picture, I choose an arbitrary shape to process, but still I can’t finished this study:
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An view from program that I write:
References:
1) Machine Vision: Theory Algorithms and Practicalities, 2nd Edition. E. R: Davies.
Academic Press, 1997.
2) A comparative study of efficient generalised Hough transform techniques, Image and
Vision Computing, Volume 17, Issue 10, August 1999, Pages 737-748.
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