In this paper a method for symmetry axis detection in binary images is presented. The method exploits the nonlinear dynamic behavior of Cellular Neural Networks (CNNs), in particular the propagation of bipolar waves. The image is represented in polar form, transforming the symmetry with respect to an arbitrarily oriented axis in a vertical symmetry: the position of the vertical axis corresponds to the angle of the original symmetry axis. The parallel CNN architecture is useful to speed up the computation, because of the high computational cost of the task. The proposed algorithm is tested on a real image with good results.
Costantini, G., Casali, D., Perfetti, R. (2006). A new CNN-based method for detection of symmetry axis. In Proceedings of the 2006 10th IEEE International Workshop on Cellular Neural Networks and Their Applications (pp.206-209). NEW YORK : IEEE [10.1109/CNNA.2006.341631].
A new CNN-based method for detection of symmetry axis
COSTANTINI, GIOVANNI;
2006-01-01
Abstract
In this paper a method for symmetry axis detection in binary images is presented. The method exploits the nonlinear dynamic behavior of Cellular Neural Networks (CNNs), in particular the propagation of bipolar waves. The image is represented in polar form, transforming the symmetry with respect to an arbitrarily oriented axis in a vertical symmetry: the position of the vertical axis corresponds to the angle of the original symmetry axis. The parallel CNN architecture is useful to speed up the computation, because of the high computational cost of the task. The proposed algorithm is tested on a real image with good results.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
