10.1007/s13595-017-0619-0

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Automated classification of wood transverse cross-section micro-imagery from 77 commercial Central-African timber species

Nubia Rosa da Silva and Maaike de Ridder and Jan M. Baetens and Jan Van den Bulcke and Melissa Rousseau and Odemir Martinez Bruno and Hans Beeckman and Joris Van Acker and Bernard De Baets

ANNALS OF FOREST SCIENCE, 74(2):, 2017

Key message Pattern recognition has become an important tool to aid in the identification and classification of timber species. In this context, the focus of this work is the classification of wood species using texture characteristics of transverse cross-section images obtained by microscopy. The results show that this approach is robust and promising. circle Context Considering the lack of automated methods for wood species classification, machine vision based on pattern recognition might offer a feasible and attractive solution because it is less dependent on expert knowledge, while existing databases containing high-quality microscopy images can be exploited. circle Aims This work focuses on the automated classification of 1221 micro-images originating from 77 commercial timber species from the Democratic Republic of Congo. circle Methods Microscopic images of transverse cross-sections of all wood species are taken in a standardized way using a magnification of 25x. The images are represented as texture feature vectors extracted using local phase quantization or local binary patterns and classified by a nearest neighbor classifier according to a triplet of labels ( species, genus, family). circle Results The classification combining both local phase quantization and linear discriminant analysis results in an average success rate of approximately 88% at species level, 89% at genus level and 90% at family level. The success rate of the classification method is remarkably high. More than 50% of the species are never misclassified or only once. The success rate is increasing from the species, over the genus to the family level. Quite often, pattern recognition results can be explained anatomically. Species with a high success rate show diagnostic features in the images used, whereas species with a low success rate often have distinctive anatomical features at other microscopic magnifications or orientations than those used in our approach. circle Conclusion This work demonstrates the potential of a semi-automated classification by resorting to pattern recognition. Semi-automated systems like this could become valuable tools complementing conventional wood identification.