Image processing through machine learning for wood quality classification

• Ano de defesa: 2016
• Autor(a) principal: Vieira, Fábio Henrique Antunes [UNESP]
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade Estadual Paulista (Unesp)
• Palavras-chave em Português: Inteligência artificial; Descritores de textura; Madeira serrada; Artificial intelligence; Texture descriptors; Sawn wood
• Link de acesso: http://hdl.handle.net/11449/142813

The quality classification of wood is prescribed throughout the wood chain industry, particularly those from the processing and manufacturing fields. Those organizations have invested energy and time trying to increase value of basic items, with the purpose of accomplishing better results, in agreement to the market. The objective of this work was to compare Convolutional Neural Network, a deep learning method, for wood quality classification to other traditional Machine Learning techniques, namely Support Vector Machine (SVM), Decision Trees (DT), K-Nearest Neighbors (KNN), and Neural Networks (NN) associated with Texture Descriptors. Some of the possible options were to assess the predictive performance through the experiments with different techniques, Deep Learning and Texture Descriptors, for processing images of this material type. A camera was used to capture the 374 image samples adopted on the experiment, and their database is available for consultation. The images had some stages of processing after they have been acquired, as pre-processing, segmentation, feature analysis, and classification. The classification methods occurred through Deep Learning, more specifically Convolutional Neural Networks - CNN, and using Texture Descriptors with Support Vector Machine, Decision Trees, K-nearest Neighbors and Neural Network. Empirical results for the image dataset showed that the approach using texture descriptor method, regardless of the strategy employed, is very competitive when compared with CNN for all performed experiments, and even overcome it for this application.