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4.5 An Example 101
Input 1 st iteration 5 th iteration 10 th iteration
Figure 4.5: Results after some given iteration steps of the black and white skeletonization.
SKELBW1:
⎡
⎤
SKELBW2:
⎡
⎤
1 1 0
2 2 2
B 1 = ⎣ 1 5 −1 ⎦ z 1 = −1 B 2 = ⎣ 0 9 0 ⎦ z 2 = −2
0 −1 0
−1 −2 −1
SKELBW3:
⎡
⎤
SKELBW4:
⎡
⎤
0 1 1
−1 0 2
B 3 = ⎣ −1 5 1 ⎦ z 3 = −1 B 4 = ⎣ −2 9 2 ⎦ z 4 = −2
0 −1 0
−1 0 2
SKELBW5:
⎡
B 5 = ⎣
SKELBW7:
⎡
B 7 = ⎣
0 −1 0
−1 5 1
0 1 1
0 −1 0
1 5 −1
1 1 0
⎤
SKELBW6:
⎡
⎦ z 5 = −1 B 6 = ⎣
⎤
FEEDBACK
⎡
TEMPLATES:
⎤
0 0 0
A n = ⎣ 0 1 0
0 0 0
SKELBW8:
⎡
⎦ z 7 = −1 B 8 = ⎣
⎦ (n = 1...8)
−1 −2 −1
0 9 0
2 2 2
2 0 −1
2 9 −2
2 0 −1
⎤
⎦ z 6 = −2
⎤
⎦ z 8 = −2
where B i denotes the control templates, while A n denotes the feedback matrix
(the same for all directions). The algorithm runs on the SKELBWI.bmp and the
size of the image is 128 × 128. The results of the algorithm are plotted below, in
Figure 4.5.
The above analogic CNN algorithm had been implemented on the experimental
system, based on Virtex-II FPGA with high-level software API (collection of
functions), which makes it possible to control the communication of the GAPU