width on fine image can be transformed
into line-edge on the coarse image, and
hence the cracks can be detected with a
single spatial filter in the hierarchy.
From this point of view, the rate of
reduction which is suitable to detect the
cracks. ranging: from 0.1 to. 3.0 mm: in
width can be estimated at 1/30.
2.1.1 Line reserving smooth filter In
course of reduction, however, misdetec-
tion of fine cracks arise from various
stains which are often observed on con-
crete surface, such as mud, small holes,
exfoliation and so on. The reason for the
misdetection is that the pixels corre-
sponding to such stains have rather high
value than the pixels on fine cracks and
work as noise. In order to reduce mis-
computation, the original image is pre-
processed with a line reserving smooth
filter shown in Fig. 2. A certain noise
whose length is less than the size of
filter can be smoothed in the preprocess-
ing, while the cracks seem to be linear
structures still remain.
And furthermore, to prevent an increase
in undetectable fine cracks affected by
residual noise, a series of images is
gradually reduced into 1/2 size step by
step till the coarsest image which reso-
lution is intended to detect the target
cracks (see Fig. 3).
1
8 A 2
7 3
6 4
5< >5
4 6
7
2 Y
1 8
On each point of an image, a line reserving
smooth filter finds out the maximum of mean
values on each line of 8 directions.
Fig.2. A line reserving smooth filter
The coarsest image
Reduction in size
by selecting
maximum value of
pixels
The original fine
image
Fig.3. A series of images structured hierarchically
2.2 Edge detection
After generating a series of images, edge
detection is performed on the hierarchi-
cal images. The edge detection is divided
into five stages; line-edge filtering,
thresholding, noise reduction, thinning
and vectorization (see Fig. 4).
Start
1
Line-edge filtering
1
Thresholding
I
I
Thinning
j
| |
| |
| Noise reduction |
| ]
| |
Vectorization
Ï
End
Fig. 4. The flow chart of line edge detection in
hierarchical edge detecting algorithm
2.2.1 Line-edge filtering Suzuki
(1985)'s Directional Contrast Filter is
convoluted on the image to detect line-
edge ranging from 1 to 5 pixels in width.
2.2.2 Thresholding A threshold value
is determined by computation of an aver-
age on the filtered image, following
which the image is thresholded into a
binary image. The pixel of value more
than the threshold is regarded as a part
of pixels on cracks.
2.2.3 Noise reduction A small lump
of pixels is removed as a noise, which
length and area to be eliminated are
previously determined.
2.2.4 Thinning A linear chain of
pixels which is prospected to construct a
crack is skeletonized to determine the
position of a crack.
243235 Vectorization A series of
coordinates of each chaining pixel which
represents position vector on the binary
image compose a set of crack. In the
vectorization, the coordinates are calcu-
lated by tracing along the skeleton. And
then, several sets of crack which are
extracted from a finer image are combined
with the sets from the coarsest image.
While the combination is carried out, the
sets from a finer are weighted.
S.HIERARCHICAL CRACK MEASURING ALGORITHM
The crack vectors extracted from the
coarsest image are positioned roughly
rather than from the fine image, and
hence they lead to rough-measurement of
crack width.
Detailed positioning and precise measure-
ment can be achieved by means of mapping
coarse vectors onto a finer image. The
mapping operation with measuring crack
width is carried out step by step on each
hierarchical image shown in Fig. 5, which
turns back the way of image reducing
process. As the execution is finished on
the fine original image, the detailed