Abstract: A method and system for detecting and controlling the quality of debarking at the surface of a wooden log provide information on main parameters related to the debarking quality of the log surface, including fiber loss and residual bark. The debarking quality detecting method and system involve measurement of three-dimensional profile of at least a portion of the log surface to generate corresponding profile image data, which is processed to generate data indicative of the texture of the log surface. The texture data is then analyzed to generate resulting data on the debarking quality parameters. That quality indicative information can be generated on a continuous basis to provide an objective assessment of the quality performance in real-time in view of target productivity, and may then be used to perform optimal adjustments of the debarker operating parameters, either manually by the operator or automatically through feedback control.

Abstract: An optical apparatus and a method for identifying wood species of a raw wooden log involve directing light onto a representative portion of a peripheral surface of the wooden log, sensing light reflected on the illuminated representative log portion to generate reflection intensity image data including color image data, subdividing the reflection intensity image data into a plurality of image data regions each containing a preset number of image pixels, analyzing the image data regions to generate associated texture data, analyzing the color and texture data associated with each image data region to assign thereto a probable one of a plurality of species indications, and selecting a majority species indication for the inspected wooden log.

Abstract: An optical apparatus and a method for identifying wood species of a raw wooden log involve directing light onto a representative portion of a peripheral surface of the wooden log, sensing light reflected on the illuminated representative log portion to generate reflection intensity image data including color image data, subdividing the reflection intensity image data into a plurality of image data regions each containing a preset number of image pixels, analyzing the image data regions to generate associated texture data, analyzing the color and texture data associated with each image data region to assign thereto a probable one of a plurality of species indications, and selecting a majority species indication for the inspected wooden log.

Abstract: A method and system for detecting and controlling the quality of debarking at the surface of a wooden log provide information on main parameters related to the debarking quality of the log surface, including fiber loss and residual bark. The debarking quality detecting method and system involve measurement of three-dimensional profile of at least a portion of the log surface to generate corresponding profile image data, which is processed to generate data indicative of the texture of the log surface. The texture data is then analyzed to generate resulting data on the debarking quality parameters. That quality indicative information can be generated on a continuous basis to provide an objective assessment of the quality performance in real-time in view of target productivity, and may then be used to perform optimal adjustments of the debarker operating parameters, either manually by the operator or automatically through feedback control.

Abstract: A method for estimating the quality of wood chips for use in a pulp and paper production process includes estimating a plurality of wood chip quality-related properties characterizing the wood chips, and associating with these properties a plurality of corresponding wood chip property-related quality indexes. A wood chip quality model combining the wood chip property-related quality indexes is used to generate a resulting chip quality index. The properties may include wood species composition, size distribution, impurities content, moisture content and freshness characterizing the wood chips.

Abstract: Method and apparatus for testing the quality of reclaimable waste paper matter containing contaminants such as brown cardboard or colored plastic bag fragments employ image data analysis techniques to provide quality indication data useful for establishing reclaimed pulp process parameters. Polychromatic light directed onto an inspected area the matter is sensed following reflection thereon to generate color image pixel data representing values of color components within a color space for pixels forming an image of the inspected area. The image data is processed by comparison with color classification data related to one or more contaminants, to identify the pixels likely to be associated with the presence of the contaminant in the inspected area. The classification color data is derived from statistical distribution through Bayesian estimation of a probability that each said pixel be associated with the presence of each contaminant.