Cellular Neural Networks for Segmentation of Image Sequence.
Mariofanna G. Milanova, Adel Said Elmaghraby, Stuart H. Rubin · 2000
The Cellular Neural Networks (CNN) model is now a paradigm of cellular analog programmable multidimensional processor array with distributed local logic and memory. CNNs consist of many parallel analogue processors computing in real time. One desirable feature is that these processors arranged in a two dimensional grid only have local connections, which lend themselves easily to VLSI implementations. In this paper, we present a new algorithm for image segmentation using CNN. We start from a mathematical viewpoint (i.e., statistical regularization based on Markov Random Field, (MRF) and proceed by mapping the algorithm onto a cellular neural network. Because of the temporal dynamics inherent in the cells of the CNN it is well suited to processing time-varying images. A robust motion estimation algorithm is achieved by using a spatiotemporal neighborhood for modeling pixel interactions.