Lumber defect scanning including multi-dimensional pattern recognition

Claim 1

A method for identifying a defect relative to a given wood article, said method comprising the steps: collecting a first set of sensor data values taken from sensors directed toward a portion of a sample wood article surface, said portion of said sample wood article surface corresponding to a known defect, said set of sensor data values including a first specular reflection value and a first diffuse reflection value each representing wood grain structure at said portion of said wood article; selecting at least one mathematic function applied to said set of sensor data values and providing a given distance therebetween; collecting a second set of sensor data values taken from sensors directed toward a portion of said given wood article, said second set of sensor data values corresponding to said first set of sensor data values and including a second specular reflection value and a second diffuse reflection value; applying said mathematic function to said second set of sensor data values; and if said at least one mathematic function indicates distance between said second set of sensor data values at or below said given distance then identifying said portion of said given wood article as corresponding to said known defect.

Claim 2

A method for identifying a defect relative to a given wood article, said method comprising the steps: collecting a first set of sensor data values taken from sensors directed toward a portion of a sample wood article surface, said portion of said sample wood article surface corresponding to a known defect, said set of sensor data values including a first specular reflection value and a first diffuse reflection value each representing wood grain structure at said portion of said wood article; selecting at least one mathematic function applied to said set of sensor data values and providing a given distance therebetween; collecting a second set of sensor data values taken from sensors directed toward a portion of said given wood article, said second set of sensor data values corresponding to said first set of sensor data values and including a second specular reflection value and a second diffuse reflection value; applying said mathematic function to said second set of sensor data values; and if said at least one mathematic function indicates distance between said second set of sensor data values at or below said given distance then identifying said portion of said given wood article as corresponding to said known defect.

Claim 9

A method for detecting presence of at least one of a set of wood cell structure conditions at a wood article surface, said method comprising the steps: executing a training session wherein for each member of said set of wood cell structure conditions at least one training vector is defined, each training vector including a designation of the associated wood cell structure condition and a set of training vector sensor data values taken from sensors directed toward an example of the associated wood structure condition, said training vector sensor data values including a first specular reflection value and a first diffuse reflection value each representing wood cell structure at said example of the associated wood structure condition; executing a scanning procedure against inspection points of said given wood article wherein a scan vector is collected for each inspection point, each scan vector including location data indicating a physical location for the associated inspection point and including a set of scan vector sensor data values taken from sensors directed toward the associated inspection point and corresponding to said training vector sensor data values and including a second specular reflection value and a second diffuse reflection value; matching by pattern recognition analysis each scan vector with each training vector; and detecting presence of a given wood structure condition at a given location when a training set vector corresponding to said given wood structure condition matches under said pattern recognition analysis a scan vector for said given location.

Claim 10

A method for detecting presence of at least one of a set of wood cell structure conditions at a wood article surface, said method comprising the steps: executing a training session wherein for each member of said set of wood cell structure conditions at least one training vector is defined, each training vector including a designation of the associated wood cell structure condition and a set of training vector sensor data values taken from sensors directed toward an example of the associated wood structure condition, said training vector sensor data values including a first specular reflection value and a first diffuse reflection value each representing wood cell structure at said example of the associated wood structure condition; executing a scanning procedure against inspection points of said given wood article wherein a scan vector is collected for each inspection point, each scan vector including location data indicating a physical location for the associated inspection point and including a set of scan vector sensor data values taken from sensors directed toward the associated inspection point and corresponding to said training vector sensor data values and including a second specular reflection value and a second diffuse reflection value; matching by pattern recognition analysis each scan vector with each training vector; and detecting presence of a given wood structure condition at a given location when a training set vector corresponding to said given wood structure condition matches under said pattern recognition analysis a scan vector for said given location.

Claim 18

A method of identifying the condition of inspection of inspection points of a wood article surface, the method comprising the steps: scanning a plurality of inspection points of said wood article surface, said scanning step collecting for each inspection point both reflective grain defect data and tracheid effect data; identifying a first class of surface conditions by reference to said grain defect scanning data; and identifying a second class of surface conditions by reference to said tracheid effect data while excluding from consideration said first class of surface conditions said first class of surface conditions being more reliably detected relative to said second class of surface condition.

Claim 19

A method of identifying the condition of inspection of inspection points of a wood article surface, the method comprising the steps: scanning a plurality of inspection points of said wood article surface, said scanning step collecting for each inspection point both reflective grain defect data and tracheid effect data; identifying a first class of surface conditions by reference to said grain defect scanning data; and identifying a second class of surface conditions by reference to said tracheid effect data while excluding from consideration said first class of surface conditions said first class of surface conditions being more reliably detected relative to said second class of surface condition.