XXIX-B4, 2012
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feature vectorization.
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56 grey-scale image
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confusion can be
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en foreground and
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three components:
[he first component
le the last two
By converting an
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B4, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
RGB image into YIQ format, the grey-scale information can be
extracted without loss (Chen et al., 2000).
The conversion from RGB to YIQ (Ford et al., 1998) can be
expressed as
y 0:299 0.587 0.114 R
I|= 0596 -—0.274. —0.322
Q 0:231] —0.523 0.312
(I)
w Q
where Y component is the grey-scale value converted from
RGB value. Figure 3a and 3b shows the colour image and the
converted grey-scale image in a 25 X 25 sliding window of
Figure la.
(a) (b)
Figure 3. The image in a 25 X 25 sliding window. a Colour
image. b Gray-scale image. c Segmentation by thresholding
3.1.2 Adaptive image segmentation: The commonly used
segmentation approaches to grey-scale images include
thresholding, feature space clustering, region growing, etc.
Segmentation by thresholding is particularly useful for images
containing objects resting upon contrasting background. But for
a colour map image with many colour confusions, the converted
grey-scale image may not have obvious valley in the histogram.
So it is difficult to separate objects from background by using
the theoretical optimal threshold, especially for the image with
low contrast and low Signal-to-Noise Ratio (SNR). Figure 3c is
the experimental result using Otsu thresholding method (Fu,
2001) for image segmentation. It can be seen that the lines in
the window are bordered with noises and adhesions.
In order to achieve satisfying segmentation effect, this paper
presents an adaptive image segmentation algorithm. The
algorithm combines k-means clustering with directional region
growing to segment the specified linear feature in the sliding
window. First, k-means clustering is applied to a small
neighbourhood (5 X 5) in the centre of the sliding window, and
the pixels within the neighbourhood are divided into two
regions of object and background. Then, the object region is
expanded to the whole window by using directional region
growing.. The reason why 5X 5 neighbourhood is selected is
that there are just only a single line in that small region,
considering the line width and the distance between two lines in
a topographic map image with a resolution of 300dpi. The
neighbourhood should be correspondingly adjusted with the
variation of linear feature width and the resolution of map
image. K-means clustering is applied here because it is one of
the simplest unsupervised learning algorithms for solving
clustering problem (Wagstaff et al., 2001). K-means clustering
and directional region growing are performed automatically in
the sliding window no matter how the brightness changes,
therefore it is an adaptive segmentation algorithm.
K-means clustering
The process of k-means clustering is as follows.
Step 1: Choose the pixel with minimum grey-scale as a seed
within a 5 X 5 neighbourhood in the centre of the sliding
window.
Step 2: Find the maximum and minimum grey-scale values in
the 5 X 5 neighbourhood of the seed pixel, and let them be the
initial clustering centre c, and c, of the object and
background region respectively.
Step 3: For each pixel with grey-scale gj in the neighbourhood,
calculate q, — ra = e| and d, = lg; = Ca) ‚Id < d,; hen
the pixel belongs to the object region, otherwise the background
region.
Step 4: Calculate the average grey-scales m, and m, of the
object and background region respectively. If they converge to
c, and c, , the clustering ends; otherwise, let 7m, and m, be
the new clustering centre c, and c, , go to Step 3.
Step 5: Set all the grey-scale values of the pixels belonging to
the target region within the 5 X 5 neighbourhood to be 1.
For the grey-scale matrix of Figure 3b, k-means clustering is
performed in the 5 X 5 neighbourhood of the seed pixel (Its
grey-scale value is 105). Figure 4 shows the object and
background region after k-means clustering.
tar Lis 129 141] 166
138 110 120[167 187
156|115 05] 160 190
145 133 119 114 143
156 159|i42 110 112
Figure 4. K-means clustering within a 5 X 5 neighbourhood
Directional region growing
Based on the k-means clustering result, directional region
growing is performed in the whole sliding window. Before
presenting the algorithm, the initial direction given by the
operator is transformed into eight discrete directions di, d5, ...,
ds (as shown in Figure Sa), and the four sides of a 5 X 5
neighbourhood centred at a seed pixel are defined as Li, L5, Ls
and L; (as shown in Figure 5b).
(a) (b)
Figure 5. a Eight discrete directions. b The four sides of a 5X 5
neighbourhood
The directional region growing is described as follows.
Step 1: Initialization.
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