Connected component detection (CCD) is an important image processing task done by one-dimensional cellular neural networks (1-D CNNs). Recently, some sufficient conditions for 1-D CNNs with the antisymmetric template A = [s, p, - s] to perform CCD have been derived under the assumption that the outputs of the boundary cells are set to 1 or -1. In this paper, we extend these results to 1-D CNNs with the opposite-sign template A = [r, p, - s]. It is shown that the 1-D CNN can perform CCD for a wide range of parameter space. Therefore we can design 1-D CNNs which not only can perform CCD but also are robust against small perturbations of the parameters.
|Number of pages||4|
|Journal||Proceedings - IEEE International Symposium on Circuits and Systems|
|Publication status||Published - Jan 1 2007|
|Event||2007 IEEE International Symposium on Circuits and Systems, ISCAS 2007 - New Orleans, LA, United States|
Duration: May 27 2007 → May 30 2007
ASJC Scopus subject areas
- Electrical and Electronic Engineering