Locating Exterior Defects on Hardwood Logs Using High Resolution Laser Scanning

1. Locating Exterior Defects on Hardwood Logs Using High Resolution Laser Scanning Liya Thomas1, Ed Thomas2, Lamine Mili3, and Clifford A. Shaffer4 1 and 4: Department of Computer Science 3: Dept. Electrical and Computer Engineering Virginia Tech Blacksburg, Virginia, USA 2: US Forest Service Princeton, West Virginia, USA June 20, 2005

2. Log Scanning, Why? • Accurately locating defects allows operators to improve product value • Expected savings would be $1.2 billion • Fewer trees need to be harvested • Helps strengthen domestic wood products industry

3. Log Defect Detection and Classification at a Glance • Definition: • Manually or automatically detect and classify the location, shape, size, type, etc. of external or internal defects of • softwood or hardwood logs and stems. • Categories: • External vs. Internal • Softwood vs. Hardwood • CT/X-ray, MRI, Ultrasound, Microwave, Laser Scanning • Detection methods on hardwood and softwood very different

4. Internal Detection and Classification Methods • Most research groups focus on internal • Various systems over a few decades • Large and accurate data • Problems and difficulties

5. External Detection and Classification Methods • External defect detection is relatively new • Data include digital images and 3-D laser-scanned surface profile • Data do not contain information about log internal structure

6. External Defect Types Over- grown Knot Sound Knot Heavy Distortion Unsound Knot Medium Distortion Adventitious Knot Cluster Wound External Defects Adven- titious Knot Adven- titious Branch

7. External Defect Detection and Classification Using 3D Profile Data of Barked Hardwood Logs 3D Data Acquisition Log Sample Collection Radial Distance Image Detection Contours Defect Feature Extraction

8. Problem Statement • No system available • Existing technologies • Systems for softwood sawing are not directly applicable. • The system relies on laser-scanning equipment, which is safe to operators and at a reasonable cost. • Log defects should be identified in the presence of bad data (outliers).

9. Focus of This Research • Examine the modeling of circle, ellipse, and cylinder • Surface fitting using GM-estimator • Defect detection based on contour levels derived from robust radial distances • Numerical methods for solving nonlinear equations • Presently we use the iteratively reweighted least-squares (IRLS) method together with QR decomposition and Householders reflections for numerical stability.

10. Methodologies and Algorithms • Robust estimation: circle, ellipse, cylinder fitting using GME to generate appropriate reference surface in presence of missing data and severe outliers • Radial-distance extraction with respect to reference to provide a foundation—radial-distance image—for subsequent tasks • Radial-distance analysis through contouring to extract information that may help reveal the presence of defects

11. Experimental Results • New and challenging research • New robust Generalized-M Estimator with projection statistics to fit circles to log cross-section data • Radial-distance images are obtained, based on which contour images are generated • Probability of detection of 81% for the most serious defect classes, and 19% of defects falsely detected

12. Preliminary Results in Robust Regression • Data: with missing data and severe outliers • Circle fitting: robust GME algorithm with projection statistics • Outlier removal: confidence intervals

14. A 3-D Presentation of Detection Results

15. Issues to Be Addressed • More Data, More testing • System integration • Identify defects with bark patterns but no surface rise • Classify defect types • Link detection information with internal defect modeling system

16. Thank you! Liya Thomas: lithomas@vt.edu Ed Thomas: ethomas@fs.fed.us Lamine Mili: lmili@vt.edu Clifford A. Shaffer: shaffer@cs.vt.edu

17. Extra Slides

18. Log surface topology of a red oak. Note the missing data sections, both due to the size of this log and the supporting equipment during the scanning, as well as outliers that outlines the shape of supports but not part of log surface data.

19. Circle and Ellipse Fitting GME Algorithms(1) Radial-distance image from Circle Fitting From Ellipse Fitting

20. Circle and Ellipse Fitting GME Algorithms(2) Contour Levels of Radial Distances, #480 Contour Levels of Radial Distances, #480 Contour image (Circle Fitting) Contour image (Ellipse Fitting)

21. 3-D Projection of Partial Log Surface Data (with Severe Outliers)

22. Image Surface Reconstruction and Segmentation Review Haralick, Watson, et al.: Topographic Primal Sketch Tian & Murphy, Rao & Schunck: Oriented Texture Analysis Kass et al.: Active Contour Model

23. l w w h l Along Cross Section Border Line at the Base Along Log Length Illustration of an abstract external log defect

24. Circle-Fitting GM-Estimator • f(p, x +) + e = 0 • (x1 – p1 + 1 )2 + (x2 – p2 + 2) 2 – p32 + e = 0 … …

25. Circle-Fitting Functions

26. Circle-Fitting Functions: Projection Statistics