2. ROAD EXTRACTION USING SNAKES 
In this paper, road extraction is divided into three parts as 
salient road extraction, non-salient road extraction and crossing 
extraction. Salient roads are found using Ribbon Snake method. 
Then Ziplock snake method is applied for incomplete roads. 
These roads are non-salient probably and Ribbon snake method 
can not obtain these types of roads. These parts are explained 
using described methods, below. 
2.1 Salient Road Extraction 
Salient Roads are roads that are not affected or prevented by 
shadows or occlusions of buildings and trees. Extraction of 
salient roads is started with the detection of lines at a coarse 
scale. 
Elimination of irrelevant features is based on length of snake. In 
these experiments, Ribbon snake is applied for not only salient 
roads but also non-salient roads and Ribbon snake method has 
found non-salient roads. Ribbon Snake fails to detect the part of 
the road covered with other features. This is due to edge 
detection algorithm not being able to identify the initial position 
of snake properly. Besides these, elasticity and rigidity 
parameters have been made adaptive to the image properties. 
2.2 Non-Salient Road Extraction 
Typical reasons of non salient roads are shadows, building, tree 
etc. To prevent incomplete road detection, ziplock snake is used 
(Neuenschwander et al. 1997). As mentioned before, ziplock 
snake needs two end points to initialize a snake and in the 
literature these end points are defined by user. Because the 
system is automatic, two points must be detected automatically 
and not defined by user. In this step ribbon snake algorithm 
solutions are important for non salient roads extraction. After 
applying ribbon snake, salient roads are extracted and these 
roads’ start and end points can be used as ziplock snake end 
points. 
2.3 Crossing Extraction 
Extractions of salient and non-salient roads provide not only 
minimum search space for the crossing but also they give some 
initial points to detect the crossing. We can use extracted roads 
to find crossings. First of all, we search incomplete roads 
because these roads must have crossings. 
3. EXPRERIMENTAL RESULTS 
In this study, all experimented gray level images are captured 
from Google Maps. They have l-meter resolution. Especially 
high resolutions images are preferred because their geometric 
properties and characteristics are discovered easily. 
3.1 Ribbon Snake 
Ribbon snake method is defined for salient roads extraction in 
Laptev et al. (2000). Ribbon snake is extended by adding a 
width component to traditional snake and defined as 
wie t= (x(s,t). v(s.t). ws, th), (C=s=1) 
where w is the half width of the ribbon snake (Laptev et al. 
2000). 
In the experiments, Ribbon Snake initialization is important. In 
Kass et al. (1987), initial snake position is defined by the user 
as semi-automatic feature extraction. In this model, initial 
position of the snake is defined by using the Canny edge 
detection filter automatically. After the edge detection step, if 
detected lines are smaller than the defined threshold value of 
length, they must be eliminated while applying the extraction 
algorithm. 
  
Figure 3: Initial Snake 
During Ribbon snake application, initial snake position is 
moved towards ribbon snake’s left and right. 
  
Figure 4: Initial snake and detected roads in a synthetic image 
After all iterations are completed, the half width that has the 
minimum total energy is established. As shown in figure 5, road 
lines are obtained, half width and initial position of ribbon 
snake in figure 3, and process stops. Another example is shown 
in figure 4. This image is a synthetic image. For this synthetic 
image, elapsed time of all processes is fewer than real images. 
Detection results are shown in figure 6 for different images and 
the results are evaluated. Table 1 shows the evaluation of salient 
roads extraction using Ribbon Snake. 
  
Figure 5: Detected road lines using Ribbon Snake 
  
  
  
Figure Figure Figure Figure Figure Average 
4 $ 6(a) 6(b) 6(c) 
Correctness %90.87 9571.58 9492.48 9483.55 à 989.21 %86.74 
Completeness %98.47 %100 %100 1 96100 99.02 | %99.50 . 
  
Image Size | 348x434 | 350x301 344x350 221x116 503x504 
(pixel) 
  
  
  
  
  
  
  
  
  
Table 1: Evaluation of results for Salient Roads Using Ribbon 
Snake 
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