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(b) 
  
(c) 
Figure 6: (a), (b) and (c) Other Detected road lines using 
Ribbon Snake 
Ribbon snake algorithm is defined and performed for salient 
road in the literature as mentioned above. Ribbon Snake has 
also been applied for non-salient roads in this paper. Figure 7 
and 8 show results of non-salient road extraction using Ribbon 
Snake. Figure 7 is a 1-meter resolution synthetic image that is 
manually shaded. On the other hand figure 8 is a 1-meter 
resolution test image which has real shadows. 
  
Figure 7: Result of Non-Salient road extraction 
Table 2 shows evaluation of non-salient roads extraction using 
Ribbon Snake. 
  
  
  
  
  
  
  
  
Figure 7 Figure 8 Average à | 
“Correctness %100 %87.016 9193.59 à 
ü Completeness %75.962 %100 %87.98 i 
i Image Size (pixel) 344x355 497x433 £3 : 
  
Table 2: Evaluation of results for Non-Salient Roads Using 
Ribbon Snake 
    
Figure 8: Detected non-salient road lines using Ribbon Snake 
On the other hand, the detection might fail, because of the 
wrong elasticity and rigidity parameters, and initialization. 
Elasticity and rigidity parameters manually defined by user 
might cause incorrect extraction of roads in an image. Besides 
this, proper initialization of ribbon snake is an important factor 
affecting the results significantly. Canny filter is not sufficient 
to detect line in the complex images. Thus if initialization step 
is performed as inadequate, energy minimization formula is 
applied for wrongly initialized snake positions as shown in 
figure 9. 
  
Figure 9: Failed detection using Ribbon Snake 
Ribbon snake can be initialized using any edge detection 
algorithm but features like roads affect this step’s accuracy. 
Ribbon snake uses all this extracted information regardless of 
the features being irrelevant. Even if minimization algorithm is 
executed correctly, incorrect initialization causes failure in 
detection of roads. 
3.2 Ziplock Snake 
Ziplock snake method is developed by changing discrete 
representation of the traditional snake to decrease false 
detection range. The method has two application parts that are 
initialization and optimization of snake. Also, the size of 
Gaussian Kernel Filter is important for extraction. Different 
sizes of Gaussian Kernel Filter give different results. 
3.2.1 Experiments of Gaussian Kernel Filters 
During the experiments, Gaussian Kernel Filters in size of 
(10x10), (20x20), (30x30), (40x40) and (50x50) have been 
tried. Figure 10 shows the results of extraction using Gaussian 
Kernel Filter in size of (10x10), (20x20), (30x30), (40x40) and 
(50x50) and table 3 shows numerical results. 
  
  
  
  
Gaussian Gaussian Gaussian Gaussian Gaussian 
Kernel Kernel Kernel Kernel Kernel 
(10x10) (20x20) (30x30) (40x40) (50x50) 
Figure Figure Figure 10(c) Figure Figure 10(e) 
10(a) 10(b) 10(d) 
Correctness %83.87 %82.99 %88.57 %73.33 %65.56 
Completeness %95.47 %94.36 %97.35 %98.53 %97.77 
Image Size 348x434 342x434 342x434 342x434 342x434 
(pixel) 
  
  
  
  
  
  
  
  
Table 3: Evaluation of Different Size of Gaussian Kernel Filter 
Certain Gaussian blurring helps to detection, but excessive 
blurring causes to disappear of features. Therefore performance 
of extraction decreases.