Comparison analysis of nonlinear segmentation methods for selected wood features in sugar maple

Camilo Reyes, Sebastián Ossandón

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

Resumen

Two classification methods, a feed-forward neural network and a fuzzy logic algorithm, were used for the automatic identification of CT images for selected wood features in sugar maple, one of the most important hardwoods in eastern Canada. Three wood characteristics were selected for automatic identification together with the background as a default. Local features, such as position and local pixel values were used as the neural networks inputs. The fuzzy sets consisted of four different possible pixel values and four possible distances from the center of the log. The fuzzy method used in this study was of the Mamdani type. Five sugar maple logs were randomly selected for this study. One of the logs is used for the training of the neural network and the others for validation and comparison. The structure of the neural network was optimized and was used for the segmentation of the other logs. An efficiency function, consisting of the number of pixels correctly labeled, was defined for the evaluation of the segmentation process. This study shows that a segmentation based on a fuzzy method has better capabilities for generalization than one based on a feed-forward method.

Idioma originalInglés
Páginas (desde-hasta)203-210
Número de páginas8
PublicaciónJournal of Wood Science
Volumen58
N.º3
DOI
EstadoPublicada - jun. 2012
Publicado de forma externa

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