Abstract: A method of board lumber grading is performed in an industrial environment on a machine learning framework configured as an interface to a machine learning-based deep convolutional network that is trained end-to-end, pixels-to-pixels on semantic segmentation. The method uses deep learning techniques that are applied to semantic segmentation to delineate board lumber characteristics, including their sizes and boundaries.

Abstract: A method of board lumber grading is performed in an industrial environment on a machine learning framework configured as an interface to a machine learning-based deep convolutional network that is trained end-to-end, pixels-to-pixels on semantic segmentation. The method uses deep learning techniques that are applied to semantic segmentation to delineate board lumber characteristics, including their sizes and boundaries.

Abstract: A method of board lumber grading is performed in an industrial environment on a machine learning framework configured as an interface to a machine learning-based deep convolutional network that is trained end-to-end, pixels-to-pixels on semantic segmentation. The method uses deep learning techniques that are applied to semantic segmentation to delineate board lumber characteristics, including their sizes and boundaries.

Abstract: A method of improving board feature quality grading facilitates human check grader reaction to grading output. The method entails specifying, for application by an automatic grading system to the faces of boards passing through a scanning zone, a virtual grade expressed by a range of values that overlap values in two of successive standard rule-based grades representing higher and lower board feature qualities; producing a signal in response to detection of a board feature quality representing a virtual grade value of a board analyzed by the automatic grading system; indicating, in response to the signal, a virtual grading designation onto the board to alert a check grader to assess whether the board exhibits board feature quality that exceeds the lower board feature quality; and assigning to the board the standard rule-based grade representing the higher board feature quality whenever the check grader's assessment concurs with the virtual grade designation.

Abstract: A method of board lumber (Table 2) grading is performed in an industrial environment on a machine learning framework (12) configured as an interface to a machine learning-based deep convolutional network (20) that is trained end-to-end, pixels-to-pixels on semantic segmentation. The method uses deep learning techniques that are applied to semantic segmentation to delineate board lumber characteristics (Table 1), including their sizes and boundaries.

Abstract: A method of improving board feature quality grading facilitates human check grader reaction to grading output. The method entails specifying, for application by an automatic grading system to the faces of boards passing through a scanning zone, a virtual grade expressed by a range of values that overlap values in two of successive standard rule-based grades representing higher and lower board feature qualities; producing a signal in response to detection of a board feature quality representing a virtual grade value of a board analyzed by the automatic grading system; indicating, in response to the signal, a virtual grading designation onto the board to alert a check grader to assess whether the board exhibits board feature quality that exceeds the lower board feature quality; and assigning to the board the standard rule-based grade representing the higher board feature quality whenever the check grader's assessment concurs with the virtual grade designation.

Abstract: A lumber check grader-actuatable interface enables a check grader to interact with grade-quality measured boards of lumber conveyed along a flow path and passing in front of the check grader. The interface accurately and continuously tracks the location of each board in front of a check grader and tracks the location of the check grader's hands relative to the boards. Gestures can, therefore, be used for a selected board to perform additional actions, such as changing the grade or changing the trims. The interface enables a check grader to walk alongside and keep pace with a board of interest as it is transported and to provide feedback to the interface about a needed change for the board of interest. By knowing which board is of interest to a check grader, the interface can display additional information for only that board without overwhelming the check grader with non-stop information overload.

Abstract: A lumber check grader-actuatable interface enables a check grader to interact with grade-quality measured boards of lumber conveyed along a flow path and passing in front of the check grader. The interface accurately and continuously tracks the location of each board in front of a check grader and tracks the location of the check grader's hands relative to the boards. Gestures can, therefore, be used for a selected board to perform additional actions, such as changing the grade or changing the trims. The interface enables a check grader to walk alongside and keep pace with a board of interest as it is transported and to provide feedback to the interface about a needed change for the board of interest. By knowing which board is of interest to a check grader, the interface can display additional information for only that board without overwhelming the check grader with non-stop information overload.