Share Email Print

Proceedings Paper

Nonlinear features extraction applied to pollen grain images
Author(s): Arnaldo de Albuquerque Araujo; Laurent Perroton; Ricardo Augusto Rabelo Olivera; Leonardo Max Batista Claudino; Silvio Jamil Ferzoli Guimaraes; Esther Bastos
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

In this work, we introduced an unsupervised segmentation and classification method based on combining two approaches: the wavelet analysis and a neural network indexation technique. The wavelet approach exploits multispectral and multiresolution analysis, providing texture description, which is a very interesting attribute. The resulting extracted features are used to perform the classification of a database of pollen grain images. This classification is performed by a neural network together with a clustering algorithm.

Paper Details

Date Published: 8 May 2001
PDF: 11 pages
Proc. SPIE 4304, Nonlinear Image Processing and Pattern Analysis XII, (8 May 2001); doi: 10.1117/12.424990
Show Author Affiliations
Arnaldo de Albuquerque Araujo, Univ. Federal de Minas Gerais (Brazil)
Laurent Perroton, Ecole Superieure d'Ingenieurs en Electrotechnique et Electronique (France)
Ricardo Augusto Rabelo Olivera, Univ. Federal de Minas Gerais (Brazil)
Leonardo Max Batista Claudino, Univ. Federal de Minas Gerais (Brazil)
Silvio Jamil Ferzoli Guimaraes, Univ. Federal de Minas Gerais (Brazil)
Esther Bastos, Fundacao Ezequiel Dias (Brazil)

Published in SPIE Proceedings Vol. 4304:
Nonlinear Image Processing and Pattern Analysis XII
Edward R. Dougherty; Jaakko T. Astola, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?