Lumber defect scanning including multi-dimensional pattern recognition

摘要

Grain defect scanning takes into account a broad set of data representing both wood grain structure and wood grain image to provide a multi-dimensional scan vector for an inspection point with wide variation therein relative to defect types. A library of similarly structured multi-dimensional training set vectors developed during a preliminary training session with known defect types is referenced by multivariate pattern recognition analysis to classify a collection of scan vectors associated with an article under inspection. By statistically matching scan vectors with training set vectors under pattern recognition analysis, physical locations on a wood article are identified according to known defect types.