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Madagascar Documentation Tutorial 27
Figure 2: Depth slice from Figure 1 after smoothing (left) and output of edge detection
(right). school2009/channel smoothed
9 echo in=$SOURCE data format=a s c i i f l o a t n1=3 n2=57036 |
10 dd form=n a t i v e | window n1=1 f 1=−1 |
11 put
12 n1=196 o1 =33.139 d1=0.01 l a b e l 1=y unit1=km
13 n2=291 o2 =35.031 d2=0.01 l a b e l 2=x unit2=km
14 ’ ’ ’ )
15
16 # Triangle smoothing
17 Flow ( ’ smoothed ’ , ’ horizon ’ , ’ smooth r e c t 1 =20 r e c t 2 =20 ’ )
18
19 # Display r e s u l t s
20 for horizon in ( ’ horizon ’ , ’ smoothed ’ ) :
21 # −−− CHANGE BELOW −−−
22 Plot ( horizon , ’ grey c o l o r=j b i a s=0 y r e v e r s e=n w a n t t i t l e=n ’ )
23 edge = ’ edge−’+horizon
24 Flow ( edge , horizon , ’ canny max=98 | dd type=f l o a t ’ )
25 Plot ( edge , ’ grey a l l p o s=y y r e v e r s e=n w a n t t i t l e=n ’ )
26 Result ( horizon , [ horizon , edge ] , ’ SideBySideIso ’ )
27
28 End ( )
1-D SYNTHETIC
To better understand the effect of smoothing, you decide to create a one-dimensional
synthetic example shown in Figure 3(a). The synthetic contains both sharp edges and
random noise. The output of conventional triangle smoothing is shown in Figure 3(b).
We see an effect similar to the one in the real data example: random noise gets