Dr. Suchendra M. Bhandarkar, Department of Computer Science.
Dr. Timothy D. Faust, Warnell School of Forest Resources.
Dr. E. William Tollner, Department of Biological and Agricultural Engineering.
 
 
    This project is in collaboration with the Warnell School of Forest Resources and the Department of Biological and Agricultural Engineering and is funded by the US Department of Agriculture. The project deals with the design and implementation of a machine vision system CATALOG for detection and classification of some important internal defects in logs via analysis of computer axial tomography (CT or CAT) images.
 
    Internal features of hardwood logs such as knots, cracks, decay and other anomalies of tree growth determine their ultimate value. If these defects were known prior to the sawing of the log, optimized sawing plans could be devised to achieve greater value from the log. Production of lumber is essentially a destructive process. With each cut into the log, new information is divulged on the quality of the wood inside which often suggests a different and better sawing or cutting pattern.  However, since each step in the sawing process is irreversible,
the loss in the value yield has already happened and cannot be subsequently rectified.  Hardwood lumber production has traditionally had a low conversion efficiency; an average of 35% of the log is converted to usable lumber.  Improving the lumber value yield from logs has become important to many sawmill managers as the cost of logs has risen to 80% of total production costs.  Existing technologies to increase lumber volume by external log inspection have reached the point that little further progress is expected.  Substantial gains lumber value yield are possible only by internal log scanning using CT.
 
    The defect identification and classification in CATALOG consists of two phases. The first phase comprises of the segmentation of a single CT image slice which results in the extraction of 2-D defect like regions from the CT image slice.  The second phase comprises of the correlation of the 2-D defect like regions across CT image slices in order to establish 3-D support. The 2-D defect like regions with adequate 3-D support are labeled as true defects.
 
    The current version of CATALOG is capable of 3-D reconstruction and rendering of the log and its internal defects from the individual CT image slices. CATALOG is also capable of simulation and rendering of key machining operations such as sawing and veneering on the 3-D reconstruction of the logs. The current version of CATALOG is intended as a decision aid for sawyers and machinists in lumber mills and also as an interactive training tool for novice sawyers and machinists.
 
Publications
 
S.M. Bhandarkar, T.D. Faust and M. Tang, CATALOG: A System for Detection and Rendering of Internal Log Defects Using Computer Tomography, Machine Vision and Applications, under review.
 
S.M. Bhandarkar, T. Faust and M. Tang, A System for Detection of Internal Log Defects by Computer Analysis of Axial CT Images,  Proc. IEEE Intl.  Wkshp. Appl. Computer Vision, Sarasota, FL, Dec. 2-4, 1996, pp. 258-263.