Nonlinear features extraction applied to pollen grain images

Arnaldo de Albuquerque Araujo; Laurent Perroton; Ricardo Augusto Rabelo Olivera; Leonardo Max Batista Claudino; Silvio Jamil Ferzoli Guimaraes; Esther Bastos

doi:10.1117/12.424990

8 May 2001 Nonlinear features extraction applied to pollen grain images

Arnaldo de Albuquerque Araujo, Laurent Perroton, Ricardo Augusto Rabelo Olivera, Leonardo Max Batista Claudino, Silvio Jamil Ferzoli Guimaraes, Esther Bastos

Author Affiliations +

Proceedings Volume 4304, Nonlinear Image Processing and Pattern Analysis XII; (2001) https://doi.org/10.1117/12.424990
Event: Photonics West 2001 - Electronic Imaging, 2001, San Jose, CA, United States

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.

Citation Download Citation

Arnaldo de Albuquerque Araujo, Laurent Perroton, Ricardo Augusto Rabelo Olivera, Leonardo Max Batista Claudino, Silvio Jamil Ferzoli Guimaraes, and Esther Bastos "Nonlinear features extraction applied to pollen grain images", Proc. SPIE 4304, Nonlinear Image Processing and Pattern Analysis XII, (8 May 2001); https://doi.org/10.1117/12.424990

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