TY - JOUR
T1 - Discrete‐time cellular neural networks
AU - Harrer, Hubert
AU - Nossek, Josef A.
PY - 1992
Y1 - 1992
N2 - A network structure called a discrete‐time cellular neural network is introduced. It is derived from cellular neural networks and feedback threshold networks. the architecture is discussed and its advantages are presented. Convergence is proved for a large class of templates and applications are given for the following image‐processing tasks: linear thresholding, connected component detection, hole filling, concentric contouring, increasing and decreasing objects step by step, searching for objects with minimal distance, and oscillation.
AB - A network structure called a discrete‐time cellular neural network is introduced. It is derived from cellular neural networks and feedback threshold networks. the architecture is discussed and its advantages are presented. Convergence is proved for a large class of templates and applications are given for the following image‐processing tasks: linear thresholding, connected component detection, hole filling, concentric contouring, increasing and decreasing objects step by step, searching for objects with minimal distance, and oscillation.
UR - http://www.scopus.com/inward/record.url?scp=0026915217&partnerID=8YFLogxK
U2 - 10.1002/cta.4490200503
DO - 10.1002/cta.4490200503
M3 - Article
AN - SCOPUS:0026915217
SN - 0098-9886
VL - 20
SP - 453
EP - 467
JO - International Journal of Circuit Theory and Applications
JF - International Journal of Circuit Theory and Applications
IS - 5
ER -