Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
On Optimal Pairwise Linear Classifiers for Normal Distributions: The Two-Dimensional Case
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to variable and feature selection
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
The k-Nearest Neighbor Method for Automatic Identification of Wood Products
CONIELECOMP '04 Proceedings of the 14th International Conference on Electronics, Communications and Computers
International Journal of Systems Science - Innovative Production Machines and Systems, Guest Editors: Duc-Truong Pham, Anthony Soroka and Eldaw Eldukhri
Rotational Invariant Wood Species Recognition through Wood Species Verification
ACIIDS '09 Proceedings of the 2009 First Asian Conference on Intelligent Information and Database Systems
Classification methods and inductive learning rules: what we may learn from theory
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Performance analysis of colour descriptors for parquet sorting
Expert Systems with Applications: An International Journal
Fuzzy logic-based pre-classifier for tropical wood species recognition system
Machine Vision and Applications
Hi-index | 0.00 |
The classification of wood types is needed in many industrial sectors, since it can provide relevant information concerning the features and characteristics of the final product (appearance, cost,mechanical properties, etc.). This analysis is typical in the furniture industries and the wood panel production. Usually, the analysis is performed by human experts, is not rapid, and has a nonuniform accuracy related mainly to the operator's experience and attention. This paper presents a methodology to effectively cope with the design of an automatic wood types classification system based on the analysis of the fluorescence spectra suitable for real-time applications. This paper presents an experimental set up based on a laser source, a spectrometer, and a processing system, and then, it discusses a set of techniques suitable to extract features from the spectra and how to exploit the extracted feature to train an inductive classification system capable to properly classify the wood types. Obtained experimental results show that the proposed approach can achieve a good accuracy in the classification and requires a limited computational power, hence allowing for the application in real-time industrial processes.