We are currently optimizing the edge and line detectors involved in our image
processing algorithms. We adjust various parameters involved in the process
of edge and line detection and look for the optimal parameter set which helps
generate the best performance.
<br><br>

First of all, we created reference data by manually labeling the image
database. For this purpose, we modified our front-end image segmentation
tool, incorporating the function of drawing lines. (We could only draw
polygons previously.) We labeled two sets of images from the Pre-Phase 01
database. The first set consists of 165 images, and the second one contains
159 images. Here is an

<a href="./images/img_a180_091590.gif">example</a>

of the manual work.
<br><br>

We designed a metric to measure the performance of the detectors. In this
scheme, detected lines are evaluated according to how close they are to the
corresponding reference lines in position, length and slope. To be specific,
for each reference line, we find a detected line which is the
closest to it in position. Then we compute the distance from the detected
line to this reference line, compare the lengths of both lines, and compare
the slopes of them. If all these results are within the
prescribed thresholds, we accept the detection as a correct one. Those
detected lines without any reference lines to match them are considered as
insertion errors.
<br><br>

Some preliminary

<a href="./html/table_01.html">evaluation results</a> and
<a href="./html/table_02.html">example images</a>

are shown here.
We note that in most cases, detected lines were considered incorrect
detection because they were much shorter than their references. This is
actually a common problem called "broken lines" in line detection.
Also, there were many inserted lines, which may be reduced by an adjustment
of the threshold parameters. The problems will be investigated further in
the coming experiments.