Automatic analysis of log end face images in the sawmill industry

At present grading of sawlogs in a sawmill relies on visual inspection wherein a human expert grades a log every few seconds as it passes on a conveyor belt. This tedious and difficult work is prone to substantial inter- and intra-grader variability. This dissertation presents methods for automatic analysis of end faces from Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst). Two features are extracted: the pith (centre core) position and the number of annual rings. The pith detection uses local orientations to estimate the centre of the annual rings in a manner that is robust to disturbances as knots and cracks as well as partial coverage of dirt or snow. The number of annual rings is counted using the polar distance transform, a tool developed here. This transform combines the image intensity and the circular shape of the rings so that the annual ring pattern can be outlined in rough and noisy images. First the marks on end faces from uneven sawing are removed using an automatic method developed in this work. The data are images of untreated end faces mostly acquired at sawmills. A large amount of the data was imaged using a camera mounted above a conveyor belt at a sawmill, collecting images everymonth during one year. In total, the data consists of over 4000 images of pine and spruce. In this dissertation an algorithmfor generating synthetic log end face images is also presented. The synthetic data can be used as a tool for developing image analysis methods. The annual ring measurements were thoroughly evaluated on pine end face images acquired using the mounted end face camera. This evaluation shows that the method performs equally well as an experienced manual grader for grading the logs into quality classes. The method can thus be used as a component of an automatic grading system, overseen by a manual grader.