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GSK(X) = XO{L} (3)
where X refers to contour map image, “O" represents
gray-scale thinning, result in a gray-scale thinned contour
image. Because of the finite width of scanned contour
(often several pixel width),the operation convergences
quickly after no more than 2 or three recursive times. The
result preserves the contour in middle axis, disappear the
submerge mostly. Furthermore, the result opened with
the structure element H clears the submerge completely.
That is
GSK'(X) 2 GSK(X)o H (4)
1 1 1
where "o" represents open operation, H=1 (D l1.
]. 1 i
Moreover, the contour now is sometimes in jagged way,
and its structure reflects as
0
0
m— — C
1
0
; mc ie
i7 1,2,...8 rotate 1 as soon as i increase 1
Design these group feature as structuring elements ( J; ) ,
by means of thinning transformation as
GSK"(.X) 2 GSK'(X)O (J;) (5)
leads to one pixel width contour.
The gray tone contour image now reveals a surprising
by-product, that is, the gray level histogram concentrates
on the maximum gray value. So luckily, it is easy to
determine threshold to segment the contour from the
image, The difficult segment problem is solved!
4. AUTOMATIC RASTER TO
VECTOR CONVERSION
4.1 Data Structure
The linear quadtree is a data structure which is based on
the regular decomposition of a square into quadrants and
sub-quadrants[6]. It represents spatial relations only by
leaf nodes with numeric keys. Contour map after
thresholding is organized in such structure not only
compact raster data in a high rate, but also improve the
operation speed on morphological transform.
Freeman code is often used to describe a vector data in
the form of number series. The number representing the
dot near the center is less than 9 when the data are
organized in 8-connect form, such as
top
4 3 2
left 5 0 1 right
6 7 8
bottom
Using this code to describe a contour helps to learn the
Properties of a contour such as length, direction and
Connectivity. Obviously these features are important in
contour line identification. The dot on a contour can be
531
caught by recursive morphological thinning with structure
elements { E;) such as
eui 0
ooo
CS — ©
i=1,2,...8 rotate T as soon as i increase 1
and the Freeman code of the dot is equal to the number i
of series ( E; )..
4.2 Contour Line Extraction
To extract contour line from annotation symbols on a
contour map is the first step of raster to vector
conversion. In general , the length of contour is far from
the length of symbols, and contour is not crossed by
themselves or each other. Although this way can
separate most contour from the other part of map, some
little arcs which are broken from the contour by manual
annotation are filtered also, but they are often important
to show features of the contour. So the method should be
improved.
In order to provide a precise mathematical description of
the important shape-size of a scanned contour map
image under consideration, the pattern spectrum is
utilized. The pattern spectrum PSK(X) of a set X in terms
of the structuring element B is given by
Card[Y, (X)-Y,,,(X)], fork >=0 6
Card[O. , (X) - b , ,(X)],fork «- -1 (6)
where k €Z and Card[X] denotes the cardinality of a set X,
Y represents open operation and ® represents close
operation. The pattern spectrum provides a measure of
similarity between an image and the collection of all
possible structure elements kB. So selecting suitable
structure elements to describe contour line not annotation
symbols such as
PSK(X) =
© ©
ooo
— © ©
i 2 1,2,...8 rotate E as soon as i increase 1, and
1.0.0
0. 0 0
0 1. 0
i Tc 2
i 7 1,2,...8 rotate 1 as soon as i increase 1
contour line contributes to lower size part of the pattern
spectrum, where annotation symbols contribute to higher
size part of the pattern spectrum. Therefore the contour
line is distinguished from annotation symbols and is
extracted easily. As you may know, the method is also
suitable for annotation symbols recognition.
4.3 Contour Gap Connecting
It is necessary to deal with the *bottle-neck" problem of
contour gap connecting, in order to set topological
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B4. Vienna 1996