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Lumber-defect-scanning-including-multi-dimensional-pattern-recognition

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Lumber defect scanning including multi-dimensional pattern recognition

Details

Inventors: Soest, Jon F.;
Assignee: U.S. Natural Resources, Inc. (Vancouver, WA)
Primary Examiner: Boudreau; Leo
Assistant Examiner: Shawala; Bipin
Attorney, Agent or Firm: Harrington; Robert L.

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.

DETAILED DESCRIPTION In accordance with the present invention, a method for identifying a defect relative to a given wood article begins by collecting a first set of data values characterizing a portion of a sample wood article surface, the portion of the sample wood article surface corresponding to a known defect.
The data values collected include at least one value responsive to wood grain structure at the portion of said wood article.
At least one mathematic function is then selected for application to the first set of data values, the mathematic function resulting in a given clustering of or distance between said data values according to multivariate pattern recognition analysis.
The first set of data values and selected mathematic function thereby establish a training set.
The next step employed under the present invention calls for collection of a second set of data values characterizing a portion of said given wood article.
The second set of data values corresponds to the first set of data values and is also applicable the selected at least one mathematic function to indicate a clustering of or distance between the second set of data values.
If the selected mathematic function indicates sufficiently minimum distance between the second set of data values at or below the given distance, then the portion of the given wood article is identified as corresponding to the known defect associated with the first set of data values.
In accordance with one aspect of the present invention, a method for detecting the presence of at least one of a set of wood cell structure conditions at a wood article surface includes a preliminary step of executing a training session wherein for each of said set of wood cell structure conditions at least one training vector is defined.
Each training vector includes a designation of the associated wood cell structure condition and a set of training vector data values characterizing the associated wood cell structure condition.
The method next includes executing a scanning procedure against inspection points of said given wood article wherein a scan vector is collected for each inspection point

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