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
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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
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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).

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Focus of This Research

1. Examine the modeling of circle, ellipse, and
   cylinder
2. Surface fitting using GM-estimator
3. Defect detection based on contour levels derived
   from robust radial distances
4. Numerical methods for solving nonlinear equations
5. 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 foundationradial-distance
  imagefor subsequent tasks
• Radial-distance analysis through contouring to
  extract information that may help reveal the
  presence of defects

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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

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Preliminary Results in Robust Regression

• Data with missing data and severe outliers
• Circle fitting robust GME algorithm with
  projection statistics
• Outlier removal confidence intervals

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A 3-D Presentation of Detection Results
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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_at_vt.edu Ed Thomas
ethomas_at_fs.fed.us Lamine Mili
lmili_at_vt.edu Clifford A. Shaffer
shaffer_at_cs.vt.edu
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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.
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Circle and Ellipse Fitting GME Algorithms(1)
Radial-distance image from Circle Fitting
From Ellipse Fitting
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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)
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3-D Projection of Partial Log Surface Data (with
Severe Outliers)
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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
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l
w
w
l
h
Along Cross Section
Border Line at the Base
Along Log Length
Illustration of an abstract external log defect
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Circle-Fitting GM-Estimator

• f(p, x ?) e 0
• (x1 p1 ?1 )2 (x2 p2 ?2) 2 p32 e 0

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Circle-Fitting Functions
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Circle-Fitting Functions Projection Statistics