Locating Exterior Defects on Hardwood Logs Using High Resolution Laser Scanning
-
Upload
debra-mcintosh -
Category
Documents
-
view
35 -
download
0
description
Transcript of Locating Exterior Defects on Hardwood Logs Using High Resolution Laser Scanning
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
20
25
x1
x 2
Confidence interval of fitted circle for log #480, x3=30.044
data fitd crclCI crcs
0 90 180 270 3600
12
24
36
48
60
72
84
96
108
120
SK
SK
OK
OK
OK
GOUGE
? (?)
x3
#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: [email protected]
Ed Thomas: [email protected]
Lamine Mili: [email protected]
Clifford A. Shaffer: [email protected]
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
Along Log Length
Along Cross Section
l
l hw
w
Border Line at the Base
f(p, x +) + e = 0
(x1 – p1 + 1 )2 + (x2 – p2 + 2) 2 – p32 + e = 0
Circle-Fitting GM-Estimator
)()p(1
2∑==
m
i ii
ii w
rwJ
σρ
0),(),( 1 =− xphRQxpH T
( ) ( )[ ] ( ) )(1)()(1
)(1)()()()1( ,,, kkTkkkTkkk rRQxpHxpHRQxpHpp −−
−+ +=
…
…
2975.0,22
2
),,1min( χ== bwherePSb
wi
i
⎪⎪
⎩
⎪⎪
⎨
⎧
>−
≤
=
for 2
for )(2
1
)(2
2
λσ
λ
σλ
λσσ
σρ
ii
i
ii
i
ii
i
ii
i
ii
i
w
r
w
r
w
r
w
r
w
r
⎪⎪
⎩
⎪⎪
⎨
⎧
>
≤
=
for )(
for
)(
λσσ
λ
λσσ
σψ
ii
i
ii
i
ii
i
ii
i
ii
i
w
r
w
rsign
w
r
w
r
w
r
( )⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢
⎣
⎡
−−
−−−−
−=
32211
3222112
3221111
2
1,
ppxpx
ppxpx
ppxpx
xpH
mm
MMM
Circle-Fitting Functions
)1)(( 3
cx
pcxd
i
ii −−−=
)(4826.1
)(max
,...,1,...,1
,...,1
1 vdmedvdmed
vdmedvdPS
T
jmj
T
kmk
T
jmj
T
i
vi
==
=
= −
−=
22/1,2 αχ −>iPS
2975.0,22
2
),,1min( χ== bwherePSb
wi
i
Circle-Fitting Functions: Projection Statistics