Figure 18: Extraction of Road Using Ribbon Snake and Ziplock 
Snake 
In this experiment, 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. As a result Ribbon Snake algorithm can not perform 
extraction for whole roads. Besides this, Ziplock snake method 
was implemented for only uncovered edge. 
3.4 Comparison Between Ribbon Snake and Ziplock Snake 
Methods 
In this part, results of Ribbon Snake and Ziplock Snake 
algorithms were compared and evaluation tables were 
constituted. 
  
  
  
| Ribbon Snake Ziplock Snake | 
| Correctness %85.71 9484.51 I 
| Completeness %99.90 %98.87 | 
  
  
  
‘Table 8: The results of extraction of salient roads 
According to the table 8 Ribbon snake and Ziplock snake gives 
similar results for extraction of salient roads. But as shown in 
table 9, the results of ribbon snake and ziplock snake method 
are quite different for extraction of non-salient roads. Ziplock 
snake have much more low percentage than Ribbon snake at 
extraction of non-salient roads. Because of this, as mentioned 
above, Ziplock snake must be initialized by using more than 
two end points. In this manner, Ziplock snake success can be 
increased by using these points. Beside this, Ziplock snake 
might need new control points during optimization. On the 
contrast, Ribbon snake does not need any user intervention. 
Ziplock snake initialization step requires end points that are 
defined by output of the Ribbon snake method or users. On the 
other hand, Ribbon snake method requires only right 
initialization using any edge detection method. 
  
  
  
B Ribbon Snake Ziplock Snake 8 
| Correctness 9493.51 9566.13 4 
â Completeness %87.98 %98.96 F4 
3 
  
  
  
Table 9: The results of extraction of non-salient roads 
3.5 Extraction of Crossing 
Extraction of salient and non-salient roads provides not only 
minimum search space for crossings but also give some initial 
points to detect crossings. First of all, incomplete roads are 
searched, because these roads must have crossing. After that, 
center point of the end of the road that is incomplete is found. 
After end points of all incomplete roads are detected, the 
distances between them are calculated. If their distance is under 
a particular threshold, they are connected to each other as 
shown in figure 19. The threshold value is calculated as equal to 
the doubled road width. On the other hand, unit normal vectors 
of the center points are give a clue about road direction. Unit 
normal vector has been defined in chapter 3. Hence, center 
points of the lines are meet a center point that is the center of 
the crossing. 
  
Figure 19: Extraction crossings in real images 
4. CONCLUSIONS 
In this paper, Ziplock Snake and Ribbon Snake methods are 
tested and compared, some constant variables that are defined 
manually by user arc supplied automatically with the new 
method that is defined and crossing extraction is supported by a 
new approach. 
According to the experimental results, Ribbon Snake is more 
favorable than Ziplock Snake to extract salient and non-salient 
roads. While Ribbon snake and Ziplock snake gives similar 
results for extraction of salient roads, same thing can not be said 
for extraction of non-salient roads. Ziplock snake have much 
lower percentage of correctness and lower percentage of 
completeness than Ribbon snake at extraction non-salient roads. 
Because of this, as mentioned above, Ziplock snake must be 
initialized by using more than two end points. Beside this, 
Ziplock snake might need new control points during 
optimization. On the contrast, Ribbon snake does not need any 
user intervention. Ziplock snake initialization step requires end 
points that are defined by output of the Ribbon snake method or 
users. 
5. REFERENCES 
Hinz, M., Toennies, K. D., Grohmann, M., & Pohle, R. (2001). 
Active Double-Contour for Segmentation of Vessels in Digital 
Subtraction Angiography. SPIE. Medical Imaging, 1554-1562. 
San Diego, CA, USA. 
Kass, M., Witkin, A., & Terzopoulos, D. (1987). Snakes: Active 
Contour Models. /nternational Journal of Computer Vision, 
1(4) , 321-331. 
Laptev, I., Mayer, H., Lindeberg, T., Eckstein, W., Steger, C., & 
Baumgartner, A. (2000). Automatic extraction of roads from 
aerial images based on scale space and snakes. Machine Vision 
and Applications , 12, 23-31. 
Neuenschwander, W., Fua, P., Szekely, G., & Kubler, O. (1997, 
December). Ziplock Snakes. /nternational Journal of Computer 
Vision, 26(3) , 191-201. 
Ozkaya, M. (2009, June 16). Road Extraction From High 
Resolution Satellite Images. M.S. Thesis. METU. 
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