2A-L6 Edge detection:
2D operators
CS4495/6495
Introduction to Computer Vision
Derivative theorem of convolution - 1D
•This saves us one operation:
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h f h f
f
h
xh
()
xhf
Derivative of Gaussian filter – 2D
( ) ( )
xx
I g h I g h
Derivative of Gaussian filter – 2D
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0.0030 0.0133 0.0219 0.0133 0.0030
0.0133 0.0596 0.0983 0.0596 0.0133
0.0219 0.0983 0.1621 0.0983 0.0219
0.0133 0.0596 0.0983 0.0596 0.0133
0.0030 0.0133 0.0219 0.0133 0.0030
( ) ( )
xx
I g h I g h
Derivative of Gaussian filter – 2D
11
0.0030 0.0133 0.0219 0.0133 0.0030
0.0133 0.0596 0.0983 0.0596 0.0133
0.0219 0.0983 0.1621 0.0983 0.0219
0.0133 0.0596 0.0983 0.0596 0.0133
0.0030 0.0133 0.0219 0.0133 0.0030
( ) ( )I g h I g h
Is this preferable?
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![[arxiv.org]](http://s1.studylibfr.com/store/data/009794603_1-6aa0f8bef5cc56af9bf73e355200507e-300x300.png)
![[arxiv.org]](http://s1.studylibfr.com/store/data/009794801_1-6e0c12c08d17e92518f7b5d8d06ebb87-300x300.png)
