8 May 2001 Nonlinear features extraction applied to pollen grain images
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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.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Arnaldo de Albuquerque Araujo, Arnaldo de Albuquerque Araujo, Laurent Perroton, Laurent Perroton, Ricardo Augusto Rabelo Olivera, Ricardo Augusto Rabelo Olivera, Leonardo Max Batista Claudino, Leonardo Max Batista Claudino, Silvio Jamil Ferzoli Guimaraes, Silvio Jamil Ferzoli Guimaraes, Esther Bastos, Esther Bastos, "Nonlinear features extraction applied to pollen grain images", Proc. SPIE 4304, Nonlinear Image Processing and Pattern Analysis XII, (8 May 2001); doi: 10.1117/12.424990; https://doi.org/10.1117/12.424990
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