hould be
bviously
| method
use from
. method
artifical
0 contin-
satisfied
d will be
:cond di-
require-
rge then
us point
of corre-
> original
pixels),
or exam-
; thresh-
than the
| image,
ble, if it
1 results
ed flexi-
f image,
edge ex-
ng image
on, dis-
ic search
atic con-
f A* al-
ium path
ost of an
le.
| started
in of dis-
on are:
discrete
hese dis-
crete points will have the possibility to be connected in-
to line, otherwise it can not be connected.
(2). In the selection of equidistance discrete
points, cost function should be calculated according to
the following formula and the pointwith minimum cost
will be considered as connection point.
cost = G, — G, + 2 (GD, — GD)
where, G,, G, is the grey value of point n and object
point 0, GD,, GD, is the first derivatives of
point n and object point 0.
(3). When the number of automatic looking for
connection point is small then threshold , then the edge
will be removed automatically.
(4). The searched edge points will not be searched
again so as to ensure edge point has the limit of search
once.
(5). It is prohibited to search in opposite direction
in the automatic search so as to avoid mistaken connec-
tion.
3. Manual Editing
After automatic connection, the discrete points
which can be connected all have been connected into
curve, however, as a result of the edge of remotesens-
ing image is very complicated, part of defects still might
exist even if many measures have been taken, so it must
be through manual editing.
After automatic connection and editing, the image
boundary is formed and it is very useful for practical to
use.
4. Edge Extraction Test and Results
The test image is TM and obtained in July 1987,
located in Ji county of Hebei Province. Band 3 is select-
ed and the image size is 512X512 pixel. Medium filter-
ing is firstly used with 5X5 window to eliminate the af-
fects of noise, then first and second derivatives are de-
termined also using 5 X 5window, the point of which
first derivatives is larger than 5 and second derivatives
has zero crossing is taken as edge point. In terms of
thestatistics, first derivatives are from 6 to 65. It can be
seen that the change range for first derivatives of corre-
sponding edge point is more wide, so it is not suitable to
use a threshold to control an image. Image should be di-
vided into subimage with 110 X 110 pixel size and the
threshold of each subimage should be adjusted by visual
monitor method. After the adjustment, the threshold
change of each subimage is from 8—25.
Then initial editing will start, this step is mainly to
delete evident mistake.
After the initial editing, discrete point will be con-
nected using automatical method. The maximum search
45
region is 11 X 11 pixel. Then the final editing will be
conducted, through deleting, addition and modification,
the results of image edge extraction can come upto the
optimum standard and is shown in Fig 1. From the pro-
cessing results it can be seen that image has been divid-
ed into reasonable blockette and reached the goal of im-
age segmentation.
In order to for comparison, Gauss — Laplace
method has been used, the result is shown in Fig 2, in
the test, window size is 11 X 11 pixel, standard devia-
tion is 1. 4, threshold is 5. From the the comparison,
the new method introduced in this paper is better than
Gauss — Laplace operator.
5. Conclusion
Research on edge extraction has been conducted for
many years, the edge feature points can be extracted for
many methods. These feature points can be used for im-
age matching. but there are some difficulties in image
analysis. For the requirement of image analysis in the
feature, it isbetter to provide continuous boundary. For
this reason, a research of boundary extraction based on
zero crossing of second directional derivatives has been
carried out.
This paper describes the principles of edge extrac-
tion using zero crossing of second directional deriva-
tives, the formula for fitting of two dimension image,
first and second derivatives have been derived. In order
to overcome the difficulties of dynamic threshold in edge
extraction, threshold will be determined using manual
interaction. For reasons of edge extraction to be contin-
ued, curve tracking can be conducted using artificial in-
telligence search method and discrete point will be con-
nected automatically, and it has flexible editing func-
tion, which is able to correct the defects in edge extrac-
tion conveniently. The test proves that the edge ex-
tracted by this method is better than Gauss — Laplace
operator.
REFERENCE
1. Lu Yan, A New Algorithm of One Dimension
Edge Extraction using Sequence Analysis, sym-
posium of Digital Photogrammetry, Wuhan,
China, 1988.
2. L. Vliet & I. young, A Non — Linear Laplace
Operator as Edge Detector in Noisy Image,
Computer Vision, Graphics and Image Process-
ing, 1989.
3. Zheng Zhaobao, Image Matching Method via
Dynamic Programming, Acta Geodetica et Car-
tographica Sinica, Vol 28, No. 2, 1989.