Cellular neural networks for motion estimation

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2000

Autores

M. G. Milanova (Autor) Outra; Aurélio Campilho (Autor) FEUP; Miguel Velhote Correia (Autor) FEUP

Ata de Conferência Internacional

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

Páginas: 819-822

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

BARCELONA, SPAIN, SEP 03-07, 2000

Indexação

ISI Web of Knowledge - 5 Citações

Scopus - 6 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da computação e da informação

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 4