Identification of wood from the Amazon by characteristics of Haralick and Neural Network: image segmentation and polishing of the surface

iForest: Biogeosciences and Forestry

article

Giselly Lenise de Souza Vieira¹  Márcio José Moutinho da Ponte¹  Victor Hugo Pereira Moutinho¹  Ricardo Jardim-Gonçalves²  Celson Pantoja Lima¹  Marco Valério de Albuquerque Vinagre⁴ 

[1] Graduate Program in Intellectual Property and Information Transfer Technology for Innovation/Federal University of West Pará;Universidade Nova de Lisboa;Massachusetts Institute of Technology;Universidade da Amazônia

关键词: Wood Identification;    Amazon;    Technology;    Pattern Recognition;    Digital Image Processing;    Artificial Neural Networks;   

DOI  :  10.3832/ifor3906-015

学科分类：社会科学、人文和艺术（综合）

来源: Societa Italiana di Selvicoltura ed Ecologia Forestale (S I S E F)

【 摘 要 】

The identification of Amazonian timber species is a complex problem due to their great diversity and the lack of leaf material in the post-harvest inspection often hampers a correct recognition of the wood species. In this context, we developed a pattern recognition system of wood images to identify commonly traded species, with the aim of increasing the accuracy and efficiency of current identification methods. We used ten different species with three polishing treatments and twenty images for each wood species. As for the image recognition system, the textural segmentation associated with Haralick characteristics and classified by Artificial Neural Networks was used. We verified that the improvement of sandpaper granulometry increased the accuracy of species recognition. The developed model based on linear regression achieved a recognition rate of 94% in the training phase, and a post-training recognition rate of 65% for wood treated with 120-grit sandpaper mesh. We concluded that the wood pattern recognition model presented has the potential to correctly identify the wood species studied.

【 授权许可】

Unknown