Locating Exterior Defects on Locating Exterior Defects on Hardwood Logs Using High Hardwood Logs Using High Resolution Laser ScanningResolution Laser Scanning

Liya ThomasLiya Thomas11, Ed Thomas, Ed Thomas22, Lamine Mili, Lamine Mili33, and , and Clifford A. ShafferClifford A. Shaffer44

1 and 4: Department of Computer Science1 and 4: Department of Computer Science3: Dept. Electrical and Computer Engineering3: Dept. Electrical and Computer Engineering

Virginia TechVirginia TechBlacksburg, Virginia, USABlacksburg, Virginia, USA

2: US Forest Service2: US Forest ServicePrinceton, West Virginia, USAPrinceton, West Virginia, USA

June 20, 2005June 20, 2005

•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

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 Detection methods on hardwood and softwood very differentdifferent

•Most research groups focus on internal •Various systems over a few decades•Large and accurate data•Problems and difficulties

•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

External Defect Types

Over-Over-growngrownKnotKnot

SoundSoundKnotKnot

HeavyHeavyDistortionDistortion

Adven-Adven-titioustitiousKnotKnot

MediumMediumDistortionDistortion

Adven-Adven-titioustitiousBranchBranch

AdventitiousAdventitiousKnot Knot ClusterCluster WoundWound

ExternalExternal

DefectsDefects

UnsoundUnsoundKnotKnot

Log Sample Collection

3D Data Acquisition

Radial Distance Image

Defect Feature Extra

ction

Contours

Detection

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).

Focus of This Research1. 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.

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

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

Data: with missing data and severe outliersCircle fitting: robust GME algorithm with projection statisticsOutlier removal: confidence intervals

Preliminary Results in Robust Regression

-10 -5 0 5 10 15

5

10

15

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25

x1

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#480 ( )Log diagram for ROAK

A 3-D Presentation of Detection Results

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

1616

Thank you!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

1717

Extra SlidesExtra Slides

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.

Circle and Ellipse Fitting GME Algorithms(1)

Radial-distance image from Circle Fitting From Ellipse Fitting

Contour image (Circle Fitting) Contour image (Ellipse Fitting)

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

Haralick, Watson, et al.: Topographic Primal Sketch

Tian & Murphy, Rao & Schunck: Oriented Texture Analysis

Kass et al.: Active Contour Model

Illustration of an abstract external log defect

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