Cellular Neural Networks with switching two types of templates
Yoshihiro Kato, Yasuhiro Ueda, Yoko Uwate, Yoshifumi Nishio · 2011
In this study, we propose Cellular Neural Networks with switching two types of templates. In the CNN, space varying system is known that it can perform complex processing. Generally, the space varying CNN is not easy to design. However, we can set existing template on each cell of CNN by the proposed method. In binarization, complex portions of input image are not processed well by using the conventional CNN. On the other hand, the complex portion can be processed well by the proposed method. In the edge detection, the indistinct portion is not detected by the conventional CNN with “Edge detection” template of 3×3 matrix. It is difficult for CNN to recognize that it is the edge or not. Additionally, the detected edge is too bold and some noises are left with “Edge detection” template of 5×5 matrix. By switching these templates in case, we can detect edge in indistinct position. In pattern formation, generally, simple pattern is formed by using one template. On the other hand, some complex patterns are formed by the proposed method. From some simulation results, we confirm that the proposed method is effective for various image processing.