|
|
Article: Genetic input selection to a neural classifier for defect classification of radiata pine boards.
- Article from:
- Forest Products Journal
- Article date:
- July 1, 2003
- Author:
CopyrightCOPYRIGHT 2003 Forest Products Society. This material is published under license from the publisher through the Gale Group, Farmington Hills, Michigan. All inquiries regarding rights should be directed to the Gale Group. (Hide copyright information)
|
Abstract
A genetic algorithm was used to determine an appropriate set of features for automatic defect classification of radiata pine boards. The study was performed using a low-cost machine vision system composed of a color video camera, a frame grabber, and a microcomputer. The following 10 defect categories were considered, plus clear wood: birds eye & freckle, bark & pitch pockets, wane, split, blue stain, stain, pith, dead knot, live knot, and hole. A database was built containing color images of 2,958 board faces. A total of 16,800 feature vectors were extracted from these images, and partitioned into training, validation, and test sets. Each vector was ...