Cellular neural networks for motion estimation

• Type: Article in International Conference Proceedings Book
• Date: 2000

Authors

M. G. Milanova (Author) Other; Aurélio Campilho (Author) FEUP; Miguel Velhote Correia (Author) FEUP

Conference proceedings International

Title: 15TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 2, PROCEEDINGS: PATTERN RECOGNITION AND NEURAL NETWORKS

Pages: 819-822

15th International Conference on Pattern Recognition (ICPR-2000)

BARCELONA, SPAIN, SEP 03-07, 2000

Indexing

ISI Web of Knowledge - 5 Citations

Scopus - 6 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-001-2BH

DOI: 10.1109/ICPR.2000.903670

Abstract (EN): The Cellular Neural Networks (CNN) model is now a paradigm of cellular analogue 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 motion estimation using CNN. We start from a mathematical viewpoint (i.e., statistical regularisation 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 spatio-temporal neighbourhood for modelling pixel interactions.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 4