Timber Defect Detection Based On Systematic Feature Analysis And One Class Classifier.
i
TIMBER DEFECT DETECTION BASED ON SYSTEMATIC FEATURE
ANALYSIS AND ONE CLASS CLASSIFIER
UMMI RABA’AH BINTI HASHIM
A thesis submitted in fulfilment of the
requirements for the award of the degree of
Doctor of Philosophy (Computer Science)
Faculty of Computing
Universiti Teknologi Malaysia
DECEMBER 2015
iii
DEDICATION
To my beloved husband, children, parents and brothers.
iv
ACKNOWLEDGEMENT
In the name of Allah, most gracious, most merciful. Praise to Allah, for
guiding me in the right path, blessing me with the best in this life. It takes the efforts
and supports of many to bring this research study to completion. I am indebted to the
dozens of people guiding and supporting me throughout this study. I would like to
express my gratitude to the following special individuals:
1. My supervisor and co-supervisor, Assoc. Prof. Dr. Siti Zaiton binti Mohd
Hashim and Assoc. Prof. Dr. Azah Kamilah Muda, for their wonderful
guidance and continuous encouragement during the progression of my study.
2. Academicians of UTM, for their valuable teaching, comment, idea and
motivation for this research.
3. Industry experts from Hasro Malaysia, Teras Puncak and Elegant Success
(Malaysian wood products manufacturers) for their co-operation, invaluable
consultation and kind support.
4. Universiti Teknikal Malaysia Melaka (UTeM) and Ministry of Education
Malaysia for their generous financial support.
5. My husband and children, for their patience and love.
6.
.
My parents and brothers, for their blessing and care.
v
ABSTRACT
Substantial research effort has been done in the automation of timber defect
detection to improve the quality of timber products, optimise raw material resources,
increase productivity and reduce error related to human labour. This study extends
the work on automated inspection of timber boards to Malaysian timber species
hoping that the outcome will benefit the local wood product industries. This study
aims to propose a timber surface defect detection approach which is robust in
detecting various defects on multiple timber species using significant texture
features, validated using data from local timber species. In the experiments, defective
samples from Malaysian Hardwood are collected and labelled under supervision of
industry experts. Additionally, this work gives new insight into the characterisation
of timber defect images by using statistical texture from orientation independent
Grey Level Dependence Matrix (GLDM) with appropriate parameter analysis. A
Systematic Feature Analysis (SFA) which includes exploratory and confirmatory
multivariate analysis was performed to investigate the discriminative power of the
proposed feature set. The SFA produces a feature set of timber surface defects
capable of providing significant discrimination between defects and clear wood
classes. Finally, a new concept in the domain of timber defect detection based on
outlier detection concept was introduced to overcome the problem of imbalanced
data. This study proposes a robust Mahalanobis one class classifier (MC) with Fast
Minimum Covariance Determinant estimator (MC-FMCD) for species independent
timber defect detection. The experimental results show that the proposed approach
achieved superior performance over the classical Mahalanobis Distance (MD) and
robust in detecting many types of defects across timber species.
vi
ABSTRAK
Pelbagai usaha penyelidikan telah dilaksanakan dalam pengesanan kecacatan
kayu secara automatik untuk meningkatkan kualiti produk kayu, mengoptimumkan
sumber bahan mentah dan meningkatkan produktiviti. Kajian dalam bidang ini telah
dilanjutkan kepada spesies kayu Malaysia dengan harapan bahawa hasilnya akan
memberi manfaat kepada industri produk kayu tempatan. Kajian ini bertujuan untuk
mencadangkan pengesanan kecacatan permukaan kayu yang teguh dalam mengesan
pelbagai kecacatan pada pelbagai spesies kayu menggunakan ciri tekstur yang
signifikan serta disahkan menggunakan data dari spesies kayu tempatan. Sampel
kecacatan dari spesies kayu keras Malaysia dikumpul dan dilabel di bawah
pengawasan pakar-pakar industri untuk digunakan dalam kajian ini. Selain itu, kajian
ini memberi pemahaman baru dalam perwakilan atribut imej kecacatan kayu dengan
menggunakan tekstur statistik dari Matriks Pergantungan Aras Kelabu (GLDM)
berorientasi bebas berserta dengan analisa parameter yang bersesuaian. Satu
Penilaian Atribut Sistematik (SFA) merangkumi analisa eksplorasi dan pengesahan
multivariat telah dijalankan untuk mengkaji kuasa diskriminasi set atribut yang
dicadangkan. SFA tersebut telah menghasilkan perwakilan atribut yang mampu
membezakan antara kelas-kelas kecacatan kayu dan kayu baik secara signifikan.
Akhirnya, satu konsep baru dalam domain pengesanan kecacatan kayu yang
berdasarkan pengesanan anomali telah diperkenalkan untuk menangani masalah data
tidak seimbang. Kajian ini mencadangkan satu pengelas tunggal Mahalanobis (MC)
yang teguh dengan penganggar Penentu Kovarians Minimum Pantas (MC-FMCD)
untuk pengesanan kecacatan kayu tanpa mengira spesies kayu. Hasil eksperimen
menunjukkan bahawa pendekatan yang dicadangkan berjaya mencapai prestasi yang
lebih baik jika dibandingkan dengan Jarak Mahalanobis (MD) klasik dan berupaya
mengesan pelbagai jenis kecacatan pada pelbagai spesies kayu.
vii
TABLE OF CONTENTS
CHAPTER
1
TITLE
PAGE
DECLARATION
ii
DEDICATION
iii
ACKNOWLEDGEMENT
iv
ABSTRACT
v
ABSTRAK
vi
TABLE OF CONTENTS
vii
LIST OF TABLES
xii
LIST OF FIGURES
xiv
LIST OF ABBREVIATIONS
xvii
LIST OF APPENDICES
xx
TERMS AND DEFINITIONS
xxi
INTRODUCTION
1
1.1 Overview
1
1.2 Research Background
2
1.3 Problem Statement and Research Aim
13
1.4 Research Objective
14
1.5 Research Scope
14
1.6 Significance of the Study
16
1.7 Research Methodology
17
1.8 Research Contribution
19
1.9 Thesis Structure
19
viii
2
LITERATURE REVIEW
21
2.1 Introduction
21
2.2 Overview of Timber Process
26
2.3 Malaysian Timber Species
28
2.4 Timber Defects
31
2.5 Automated Vision Inspection (AVI) of Timber
33
2.5.1 Problem Background
33
2.5.2 AVI in Wood Industry
34
2.5.3 Sensors Used for AVI in Wood Industry
39
2.5.4 General Timber Defect Detection Approach
43
2.5.5 Feature Extraction on Defect Images
46
2.5.6 Defect Classification
50
2.5.7 Discussion
53
2.6 Statistical Texture Feature Based on Grey Level
Dependence Matrix (GLDM)
55
2.6.1 Problem Background
55
2.6.2 Orientation Independent GLDM
58
2.6.3 Statistical Features of GLDM
63
2.7 One Class Classification for Imbalanced Data
71
2.7.1 Introduction and Problem Background
71
2.7.2 Distance-based One Class Classifier (OCC)
73
2.7.3 Fast Minimum Covariance Determinant as Robust
Estimator
3
77
2.8 Summary
81
RESEARCH METHODOLOGY
82
3.1 Introduction
82
3.2 Problem Situation and Solution Concept
82
3.3 Research Design
87
3.3.1 Research Framework
87
3.3.2 Operational Framework
88
ix
3.3.2.1 Phase 1: Construction of timber defect
image dataset of Malaysian hardwood
89
3.3.2.2 Phase 2: Identification of significant texture
feature set representing timber defect.
90
3.3.2.3 Phase 3: Development of robust OCC with
FMCD estimator for timber defect detection
3.3.3 Overall Research Plan
3.4 Evaluation Measurement
91
92
95
3.4.1 Multivariate Analysis of Variance (Manova) to
Evaluate Feature Quality
95
3.4.2 Precision, Recall and F Measure to Measure
Detection Performance
100
3.4.3 Over Detection and Under Detection Errors to
Assess Segmentation Quality
3.5 Summary
4
5
102
103
CONSTRUCTION OF TIMBER SURFACE DEFECT
IMAGE DATASET
104
4.1 Introduction
104
4.1 Timber Samples Collection
106
4.2 Image Acquisition Setup
106
4.3 Image Labelling and Processing
110
4.4 Findings
113
4.5 Summary
116
SIGNIFICANT FEATURE SET OF TIMBER SURFACE
DEFECTS BASED ON STATISTICAL TEXTURE AND
SYSTEMATIC FEATURE ANALYSIS
117
5.1 Introduction
117
5.2 Overview of Approach
118
5.3 Feature Extraction
121
x
5.3.1 Extracting Statistical Features from GLDM
121
5.3.2 Exploring Displacement and Quantization Parameter
of GLDM
127
5.4 Evaluation of Feature Quality
133
5.4.1 Exploratory Feature Analysis
133
5.4.1.1 Univariate Feature Range Analysis
134
5.4.1.2 Bivariate Matrix of Scatter Plot
136
5.4.1.3 Multivariate Intra-Class and Inter-Class
Distance between Clear Wood and Defects
5.4.2 Confirmatory Feature Analysis
5.4.2.1 Removing Linearly Dependent Features
137
139
141
5.4.2.2 Measuring Significant Difference between
Defect Classes using Manova Statistics
143
5.4.2.3 Identifying Significant Features using Posthoc Manova (Discriminant Analysis)
5.5 Performance Validation
145
149
5.5.1 Measuring Classification Performance across
Feature Sets and Classifiers
150
5.5.2 Measuring Classification Performance of Individual
Classes
153
5.5.3 Measuring Classification Accuracy across Timber
Species
6
156
5.6 Discussion
158
5.7 Summary
159
ROBUST MAHALANOBIAN CLASSIFIER WITH FMCD
ESTIMATOR (MC-FMCD) FOR TIMBER DEFECT
DETECTION
160
6.1 Introduction
160
6.2 Overview of Approach
161
6.3 Experimental Setting for Simulated Datasets
163
xi
6.4 Experimental Results for Simulated Datasets
165
6.4.1 Detection Peformance across Various Defect Ratios
166
6.4.2 Detection Performance by Defect Type
170
6.4.3 Detection Performance between Classic MD and
Robust MC-FMCD
174
6.4.4 Summary of Detection Performance across Timber
Species
7
178
6.5 Expert Validation on Test Images
180
6.6 Discussion
185
6.7 Summary
186
CONCLUSION AND FUTURE RESEARCH
188
7.1 Summary of Research Finding
188
7.2 Research Contribution
191
7.3 Future Work Recommendation
193
7.4 Concluding Remark
195
REFERENCES
Appendices A - N
196
213 - 297
xii
LIST OF TABLES
TABLE NO.
TITLE
PAGE
2.1
List of Malaysian timber classification based on density
(MTIB, 2000)
29
2.2
Natural durability classification based on years (MTIB, 2000)
29
2.3
Characteristics of four types of timber species (MTIB, 2000)
30
2.4
List of common timber defect
32
2.5
Related works on automated inspection of wood products
36
2.6
Related studies on inspection of external wood defects
40
2.7
Images of directional matrices and rotation invariant matrix
61
3.1
Problem leading to solution
86
3.2
Overall research plan
92
3.3
Confusion matrix
102
4.1
List of data collection setting of past studies on timber
surface defect detection
109
4.2
List of classes with example of sub-images collected
114
4.3
Number of samples collection across species
116
5.1
Example of sub-image and the corresponding dependence matrix 123
5.2
List of statistical texture features extracted
124
5.3
Example of extracted features (one sample per class,
species=Meranti, d=1, q=32)
125
5.4
Texture characteristics of clear wood and defect
126
xiii
5.5
Distances between test samples and independent clear wood
samples
142
5.6
List of feature correlation with r>0.99
142
5.7
List of features removed after correlation test
143
5.8
Box's test of equality of covariance matrices
144
5.9
Manova test
144
5.10
Pillai’s Trace value across multiple quantization levels and
displacements
145
5.11
Eigenvalues and canonical correlations
146
5.12
Raw and standardized discriminant function coefficients
(Root 1)
147
5.13
Correlation between features and canonical variable
148
5.14
List of remaining features after discriminant analysis
148
5.15
List of feature sets used for performance comparison
150
5.16
Confusion matrices for D7, D5 and D4
154
5.17
Samples mistakenly classified as clear wood (undetected
defect)
155
5.18
Confusion matrices for Merbau, KSK and Rubberwood
157
6.1
Experimental Meranti dataset for various defect ratios
163
6.2
Detection performance by defect ratio
167
6.3
Detection performance by defect types
170
6.4
Detection performance on test images: Rubberwood
181
6.5
Detection performance on test images: KSK
182
6.6
Detection performance on test images: Meranti
183
6.7
Detection performance on test images: Merbau
184
xiv
LIST OF FIGURES
FIGURE NO.
TITLE
PAGE
1.1
Motivation of the study
12
1.2
Overview of research phases
18
2.1
Taxonomy of literature review
23
2.2
Timber process
26
2.3
Log cutting pattern (Cavette, 2006; Tom & Jeff, 2010)
27
2.4
The components of an AVI system in wood industry
35
2.5
Reference pixel, X with its 8 neighbouring pixels
(Haralick et al., 1973)
59
2.6
Distribution of non-zero matrix element on the left, and
contour plot showing joint probability density function of
the spatial dependence matrix on the right.
62
2.7
Research solutions to the problem of classification of
imbalanced data (Sun et al., 2009)
73
3.1
Solution concept for timber defect detection
85
3.2
Research framework
88
3.3
Operational research framework
89
4.1
Image acquisition setup
108
4.2
The process of dataset construction
111
4.3
Sample of acquired images
111
4.4
Subdivision of original image into sub-images
113
4.5
Distribution of defect samples across species
115
xv
5.1
Proposed approach in determining significant feature set
120
5.2
Procedures for extracting statistical texture features based on
GLDM
122
5.3
Pictorial representation of the orientation independent GLDM
128
5.4
Normalized feature means against displacement and
quantization
131
5.5
Energy feature range analysis
134
5.6
Entropy feature range analysis
135
5.7
Contrast feature range analysis
135
5.8
Scatter plot matrix showing pairwise comparison of features
136
5.9
Intra-class distance between clear wood samples and interclass distance between clear wood and defect samples
138
5.10
Procedures for confirmatory feature analysis
140
5.11
Classification accuracy of three proposed feature sets (D6,
D7 and D8)
151
5.12
Classification accuracy between the proposed feature set (D7)
and feature sets from previous studies
152
5.13
F scores for each class across datasets D4, D5 and D7
154
5.14
Classification accuracy across timber species
156
6.1
Flow of experiments for timber defect detection
161
6.2
Proposed MC-FMCD for robust timber defect detection
162
6.3
F score across defect ratio: (a) Meranti, (b) Rubberwood, (c)
KSK, (d) Merbau
168
6.4
OD Error and UD Error across defect ratio: (a) Meranti, (b)
Rubberwood, (c) KSK, (d) Merbau
169
6.5
F score by defect type: (a) Meranti, (b) Rubberwood, (c)
KSK, (d) Merbau
172
6.6
OD Error and UD Error by defect type: (a) Meranti, (b)
Rubberwood, (c) KSK, (d) Merbau
173
6.7
Detection performance for MC-FMCD and classic MD:
Meranti dataset
174
xvi
6.8
Detection performance for MC-FMCD and classic MD:
Rubberwood dataset
175
6.9
Detection performance for MC-FMCD and classic MD: KSK
dataset
176
6.10
Detection performance for MC-FMCD and classic MD:
Merbau dataset
177
6.11
Average detection performance by timber species
178
6.12
Average detection performance by defect type across timber
species (a) F score comparison between timber species by
defect type (b) Average F score by defect type
179
6.13
Average detection performance between MC-FMCD and
classic MD
180
6.14
Average detection performance validated by an expert
185
xvii
LIST OF ABBREVIATIONS
ANN
-
Artificial Neural Network
AUTOC
-
Autocorrelation
AVI
-
Automated Vision Inspection
BR
-
Brown Stain
BS
-
Blue Stain
CAR
-
Causal Auto Regressive Model
CCD
-
charged-coupled device
CL
-
Clear Wood
CONT
-
Contrast
COR
-
Correlation
CPROM
-
Cluster Prominence
CSHAD
-
Cluster Shade
CT
-
Computed Tomography
DENT
-
Difference entropy
DISS
-
Dissimilarity
DVAR
-
Difference variance
EN
-
Energy
ENT
-
Entropy
EPQ
-
Equal Probability Quantization
FMCD
-
Fast Minimum Covariance Determinant
FMMIS
-
Fuzzy Min-Max Neural Network for Image Segmentation
FN
-
False Negative
FP
-
False Positive
GA
-
Genetic Algorithm
GLDM
-
Grey Level Dependence Matrix
xviii
GPR
-
Ground Penetrating Radar
HL
-
Hole
HOMO
-
Homogeneity
IDMN
-
Inverse difference moment normalized
IDN
-
Inverse difference normalized
IMC1
-
Information measures of correlation 1
IMC2
-
Information measures of correlation 2
KN
-
Knot
KNN
-
K-nearest Neighbour
KSK
-
Kembang Semangkuk
LBP
-
Local Binary Pattern
MANOVA
-
Multivariate Analysis of Variance
MAXPR
-
Maximum probability
MCD
-
Minimum Covariance Determinant
MC-FMCD
-
Mahalanobian Classifier based on Robust FMCD
MD
-
Mahalanobis Distance
MGR
-
Malaysian Grading Rule
MIDA
-
Malaysian Investment Development Authority
MLP
-
Multi-layer Perceptron
MSE
-
Mean Square Error
MTIB
-
Malaysian Timber Industry Board
MVE
-
Minimum Volume Ellipsoid
MVV
-
Minimum Vector Variance
NATIP
-
National Timber Industry Policy
OCC
-
One Class Classifier
OD
-
Over Detection
PC
-
Pocket
RBFN
-
Radial Basis Function Network
RGB
-
Red Green Blue
RT
-
Rot
SAVG
-
Sum Average
SDM
-
Spatial Dependence Matrix
SENT
-
Sum Entropy
xix
SOM
-
Self-organizing Map
SOSVH
-
Sum of Squares: Variance
SP
-
Split
SSCP
-
Sum of Squares Cross Product
SVAR
-
Sum Variance
TN
-
True Negative
TP
-
True Positive
UD
-
Under Detection
WN
-
Wane
xx
LIST OF APPENDICES
APPENDIX
A
TITLE
PAGE
Related studies on inspection of internal wood
defects
Related studies on multi sensors approach to timber
defect detection
213
B
Example of orientation independent GLDM and
normalized GLDM
216
C
Plots of feature value against displacement and
quantization parameter
219
D
Univariate feature range analysis
236
E
Matrix of scatter plots comparing feature
distribution between classes
247
F
Pairwise correlation between features and its
corresponding significance, p value
249
G
SPSS Manova output
252
H
Experimental dataset for various defect ratios
260
I
Expert validation sheet
267
J
UTM letter of permission for data collection
280
K
Biography of industry experts
284
L
Letter of dataset certification
287
M
Photo album
291
N
List of Publication
297
xxi
TERMS AND DEFINITIONS
TERM
DEFINITION
Wood
A hard fibrous material that makes up most of the substance of a
tree
Log
A part of the trunk that has been cut off from a felled tree
Timber
Wood boards sawn from logs
Primary wood
industry
Businesses that process logs or other tree sections directly
into timber, veneer, plywood, wood chips or other primary
wood products.
Sawmill
A factory where logs are sawn into timbers
Secondary wood
industry
Businesses that process primary wood products such as timber
into secondary wood products such as furniture, doors, and
parquet flooring.
Rough mill
The first production area/stage in a secondary wood product
industry where timber is being moulded and cut into rough sized
components/parts. At this stage, undesirable characteristics or
defects are removed.
Defect
Flaws or anomalies found on timber that affect its properties and
limit its possible use.
Natural defect
Biological defects occurred during the growth of a tree where the
timber originates from.
Mechanical
defect
Defects that are caused by the handling or processing of timber,
such as during drying, sawing and moulding.
Internal defect
Defects that are found inside the timber structure
External defect
Defects that are found on the surface of timber
TIMBER DEFECT DETECTION BASED ON SYSTEMATIC FEATURE
ANALYSIS AND ONE CLASS CLASSIFIER
UMMI RABA’AH BINTI HASHIM
A thesis submitted in fulfilment of the
requirements for the award of the degree of
Doctor of Philosophy (Computer Science)
Faculty of Computing
Universiti Teknologi Malaysia
DECEMBER 2015
iii
DEDICATION
To my beloved husband, children, parents and brothers.
iv
ACKNOWLEDGEMENT
In the name of Allah, most gracious, most merciful. Praise to Allah, for
guiding me in the right path, blessing me with the best in this life. It takes the efforts
and supports of many to bring this research study to completion. I am indebted to the
dozens of people guiding and supporting me throughout this study. I would like to
express my gratitude to the following special individuals:
1. My supervisor and co-supervisor, Assoc. Prof. Dr. Siti Zaiton binti Mohd
Hashim and Assoc. Prof. Dr. Azah Kamilah Muda, for their wonderful
guidance and continuous encouragement during the progression of my study.
2. Academicians of UTM, for their valuable teaching, comment, idea and
motivation for this research.
3. Industry experts from Hasro Malaysia, Teras Puncak and Elegant Success
(Malaysian wood products manufacturers) for their co-operation, invaluable
consultation and kind support.
4. Universiti Teknikal Malaysia Melaka (UTeM) and Ministry of Education
Malaysia for their generous financial support.
5. My husband and children, for their patience and love.
6.
.
My parents and brothers, for their blessing and care.
v
ABSTRACT
Substantial research effort has been done in the automation of timber defect
detection to improve the quality of timber products, optimise raw material resources,
increase productivity and reduce error related to human labour. This study extends
the work on automated inspection of timber boards to Malaysian timber species
hoping that the outcome will benefit the local wood product industries. This study
aims to propose a timber surface defect detection approach which is robust in
detecting various defects on multiple timber species using significant texture
features, validated using data from local timber species. In the experiments, defective
samples from Malaysian Hardwood are collected and labelled under supervision of
industry experts. Additionally, this work gives new insight into the characterisation
of timber defect images by using statistical texture from orientation independent
Grey Level Dependence Matrix (GLDM) with appropriate parameter analysis. A
Systematic Feature Analysis (SFA) which includes exploratory and confirmatory
multivariate analysis was performed to investigate the discriminative power of the
proposed feature set. The SFA produces a feature set of timber surface defects
capable of providing significant discrimination between defects and clear wood
classes. Finally, a new concept in the domain of timber defect detection based on
outlier detection concept was introduced to overcome the problem of imbalanced
data. This study proposes a robust Mahalanobis one class classifier (MC) with Fast
Minimum Covariance Determinant estimator (MC-FMCD) for species independent
timber defect detection. The experimental results show that the proposed approach
achieved superior performance over the classical Mahalanobis Distance (MD) and
robust in detecting many types of defects across timber species.
vi
ABSTRAK
Pelbagai usaha penyelidikan telah dilaksanakan dalam pengesanan kecacatan
kayu secara automatik untuk meningkatkan kualiti produk kayu, mengoptimumkan
sumber bahan mentah dan meningkatkan produktiviti. Kajian dalam bidang ini telah
dilanjutkan kepada spesies kayu Malaysia dengan harapan bahawa hasilnya akan
memberi manfaat kepada industri produk kayu tempatan. Kajian ini bertujuan untuk
mencadangkan pengesanan kecacatan permukaan kayu yang teguh dalam mengesan
pelbagai kecacatan pada pelbagai spesies kayu menggunakan ciri tekstur yang
signifikan serta disahkan menggunakan data dari spesies kayu tempatan. Sampel
kecacatan dari spesies kayu keras Malaysia dikumpul dan dilabel di bawah
pengawasan pakar-pakar industri untuk digunakan dalam kajian ini. Selain itu, kajian
ini memberi pemahaman baru dalam perwakilan atribut imej kecacatan kayu dengan
menggunakan tekstur statistik dari Matriks Pergantungan Aras Kelabu (GLDM)
berorientasi bebas berserta dengan analisa parameter yang bersesuaian. Satu
Penilaian Atribut Sistematik (SFA) merangkumi analisa eksplorasi dan pengesahan
multivariat telah dijalankan untuk mengkaji kuasa diskriminasi set atribut yang
dicadangkan. SFA tersebut telah menghasilkan perwakilan atribut yang mampu
membezakan antara kelas-kelas kecacatan kayu dan kayu baik secara signifikan.
Akhirnya, satu konsep baru dalam domain pengesanan kecacatan kayu yang
berdasarkan pengesanan anomali telah diperkenalkan untuk menangani masalah data
tidak seimbang. Kajian ini mencadangkan satu pengelas tunggal Mahalanobis (MC)
yang teguh dengan penganggar Penentu Kovarians Minimum Pantas (MC-FMCD)
untuk pengesanan kecacatan kayu tanpa mengira spesies kayu. Hasil eksperimen
menunjukkan bahawa pendekatan yang dicadangkan berjaya mencapai prestasi yang
lebih baik jika dibandingkan dengan Jarak Mahalanobis (MD) klasik dan berupaya
mengesan pelbagai jenis kecacatan pada pelbagai spesies kayu.
vii
TABLE OF CONTENTS
CHAPTER
1
TITLE
PAGE
DECLARATION
ii
DEDICATION
iii
ACKNOWLEDGEMENT
iv
ABSTRACT
v
ABSTRAK
vi
TABLE OF CONTENTS
vii
LIST OF TABLES
xii
LIST OF FIGURES
xiv
LIST OF ABBREVIATIONS
xvii
LIST OF APPENDICES
xx
TERMS AND DEFINITIONS
xxi
INTRODUCTION
1
1.1 Overview
1
1.2 Research Background
2
1.3 Problem Statement and Research Aim
13
1.4 Research Objective
14
1.5 Research Scope
14
1.6 Significance of the Study
16
1.7 Research Methodology
17
1.8 Research Contribution
19
1.9 Thesis Structure
19
viii
2
LITERATURE REVIEW
21
2.1 Introduction
21
2.2 Overview of Timber Process
26
2.3 Malaysian Timber Species
28
2.4 Timber Defects
31
2.5 Automated Vision Inspection (AVI) of Timber
33
2.5.1 Problem Background
33
2.5.2 AVI in Wood Industry
34
2.5.3 Sensors Used for AVI in Wood Industry
39
2.5.4 General Timber Defect Detection Approach
43
2.5.5 Feature Extraction on Defect Images
46
2.5.6 Defect Classification
50
2.5.7 Discussion
53
2.6 Statistical Texture Feature Based on Grey Level
Dependence Matrix (GLDM)
55
2.6.1 Problem Background
55
2.6.2 Orientation Independent GLDM
58
2.6.3 Statistical Features of GLDM
63
2.7 One Class Classification for Imbalanced Data
71
2.7.1 Introduction and Problem Background
71
2.7.2 Distance-based One Class Classifier (OCC)
73
2.7.3 Fast Minimum Covariance Determinant as Robust
Estimator
3
77
2.8 Summary
81
RESEARCH METHODOLOGY
82
3.1 Introduction
82
3.2 Problem Situation and Solution Concept
82
3.3 Research Design
87
3.3.1 Research Framework
87
3.3.2 Operational Framework
88
ix
3.3.2.1 Phase 1: Construction of timber defect
image dataset of Malaysian hardwood
89
3.3.2.2 Phase 2: Identification of significant texture
feature set representing timber defect.
90
3.3.2.3 Phase 3: Development of robust OCC with
FMCD estimator for timber defect detection
3.3.3 Overall Research Plan
3.4 Evaluation Measurement
91
92
95
3.4.1 Multivariate Analysis of Variance (Manova) to
Evaluate Feature Quality
95
3.4.2 Precision, Recall and F Measure to Measure
Detection Performance
100
3.4.3 Over Detection and Under Detection Errors to
Assess Segmentation Quality
3.5 Summary
4
5
102
103
CONSTRUCTION OF TIMBER SURFACE DEFECT
IMAGE DATASET
104
4.1 Introduction
104
4.1 Timber Samples Collection
106
4.2 Image Acquisition Setup
106
4.3 Image Labelling and Processing
110
4.4 Findings
113
4.5 Summary
116
SIGNIFICANT FEATURE SET OF TIMBER SURFACE
DEFECTS BASED ON STATISTICAL TEXTURE AND
SYSTEMATIC FEATURE ANALYSIS
117
5.1 Introduction
117
5.2 Overview of Approach
118
5.3 Feature Extraction
121
x
5.3.1 Extracting Statistical Features from GLDM
121
5.3.2 Exploring Displacement and Quantization Parameter
of GLDM
127
5.4 Evaluation of Feature Quality
133
5.4.1 Exploratory Feature Analysis
133
5.4.1.1 Univariate Feature Range Analysis
134
5.4.1.2 Bivariate Matrix of Scatter Plot
136
5.4.1.3 Multivariate Intra-Class and Inter-Class
Distance between Clear Wood and Defects
5.4.2 Confirmatory Feature Analysis
5.4.2.1 Removing Linearly Dependent Features
137
139
141
5.4.2.2 Measuring Significant Difference between
Defect Classes using Manova Statistics
143
5.4.2.3 Identifying Significant Features using Posthoc Manova (Discriminant Analysis)
5.5 Performance Validation
145
149
5.5.1 Measuring Classification Performance across
Feature Sets and Classifiers
150
5.5.2 Measuring Classification Performance of Individual
Classes
153
5.5.3 Measuring Classification Accuracy across Timber
Species
6
156
5.6 Discussion
158
5.7 Summary
159
ROBUST MAHALANOBIAN CLASSIFIER WITH FMCD
ESTIMATOR (MC-FMCD) FOR TIMBER DEFECT
DETECTION
160
6.1 Introduction
160
6.2 Overview of Approach
161
6.3 Experimental Setting for Simulated Datasets
163
xi
6.4 Experimental Results for Simulated Datasets
165
6.4.1 Detection Peformance across Various Defect Ratios
166
6.4.2 Detection Performance by Defect Type
170
6.4.3 Detection Performance between Classic MD and
Robust MC-FMCD
174
6.4.4 Summary of Detection Performance across Timber
Species
7
178
6.5 Expert Validation on Test Images
180
6.6 Discussion
185
6.7 Summary
186
CONCLUSION AND FUTURE RESEARCH
188
7.1 Summary of Research Finding
188
7.2 Research Contribution
191
7.3 Future Work Recommendation
193
7.4 Concluding Remark
195
REFERENCES
Appendices A - N
196
213 - 297
xii
LIST OF TABLES
TABLE NO.
TITLE
PAGE
2.1
List of Malaysian timber classification based on density
(MTIB, 2000)
29
2.2
Natural durability classification based on years (MTIB, 2000)
29
2.3
Characteristics of four types of timber species (MTIB, 2000)
30
2.4
List of common timber defect
32
2.5
Related works on automated inspection of wood products
36
2.6
Related studies on inspection of external wood defects
40
2.7
Images of directional matrices and rotation invariant matrix
61
3.1
Problem leading to solution
86
3.2
Overall research plan
92
3.3
Confusion matrix
102
4.1
List of data collection setting of past studies on timber
surface defect detection
109
4.2
List of classes with example of sub-images collected
114
4.3
Number of samples collection across species
116
5.1
Example of sub-image and the corresponding dependence matrix 123
5.2
List of statistical texture features extracted
124
5.3
Example of extracted features (one sample per class,
species=Meranti, d=1, q=32)
125
5.4
Texture characteristics of clear wood and defect
126
xiii
5.5
Distances between test samples and independent clear wood
samples
142
5.6
List of feature correlation with r>0.99
142
5.7
List of features removed after correlation test
143
5.8
Box's test of equality of covariance matrices
144
5.9
Manova test
144
5.10
Pillai’s Trace value across multiple quantization levels and
displacements
145
5.11
Eigenvalues and canonical correlations
146
5.12
Raw and standardized discriminant function coefficients
(Root 1)
147
5.13
Correlation between features and canonical variable
148
5.14
List of remaining features after discriminant analysis
148
5.15
List of feature sets used for performance comparison
150
5.16
Confusion matrices for D7, D5 and D4
154
5.17
Samples mistakenly classified as clear wood (undetected
defect)
155
5.18
Confusion matrices for Merbau, KSK and Rubberwood
157
6.1
Experimental Meranti dataset for various defect ratios
163
6.2
Detection performance by defect ratio
167
6.3
Detection performance by defect types
170
6.4
Detection performance on test images: Rubberwood
181
6.5
Detection performance on test images: KSK
182
6.6
Detection performance on test images: Meranti
183
6.7
Detection performance on test images: Merbau
184
xiv
LIST OF FIGURES
FIGURE NO.
TITLE
PAGE
1.1
Motivation of the study
12
1.2
Overview of research phases
18
2.1
Taxonomy of literature review
23
2.2
Timber process
26
2.3
Log cutting pattern (Cavette, 2006; Tom & Jeff, 2010)
27
2.4
The components of an AVI system in wood industry
35
2.5
Reference pixel, X with its 8 neighbouring pixels
(Haralick et al., 1973)
59
2.6
Distribution of non-zero matrix element on the left, and
contour plot showing joint probability density function of
the spatial dependence matrix on the right.
62
2.7
Research solutions to the problem of classification of
imbalanced data (Sun et al., 2009)
73
3.1
Solution concept for timber defect detection
85
3.2
Research framework
88
3.3
Operational research framework
89
4.1
Image acquisition setup
108
4.2
The process of dataset construction
111
4.3
Sample of acquired images
111
4.4
Subdivision of original image into sub-images
113
4.5
Distribution of defect samples across species
115
xv
5.1
Proposed approach in determining significant feature set
120
5.2
Procedures for extracting statistical texture features based on
GLDM
122
5.3
Pictorial representation of the orientation independent GLDM
128
5.4
Normalized feature means against displacement and
quantization
131
5.5
Energy feature range analysis
134
5.6
Entropy feature range analysis
135
5.7
Contrast feature range analysis
135
5.8
Scatter plot matrix showing pairwise comparison of features
136
5.9
Intra-class distance between clear wood samples and interclass distance between clear wood and defect samples
138
5.10
Procedures for confirmatory feature analysis
140
5.11
Classification accuracy of three proposed feature sets (D6,
D7 and D8)
151
5.12
Classification accuracy between the proposed feature set (D7)
and feature sets from previous studies
152
5.13
F scores for each class across datasets D4, D5 and D7
154
5.14
Classification accuracy across timber species
156
6.1
Flow of experiments for timber defect detection
161
6.2
Proposed MC-FMCD for robust timber defect detection
162
6.3
F score across defect ratio: (a) Meranti, (b) Rubberwood, (c)
KSK, (d) Merbau
168
6.4
OD Error and UD Error across defect ratio: (a) Meranti, (b)
Rubberwood, (c) KSK, (d) Merbau
169
6.5
F score by defect type: (a) Meranti, (b) Rubberwood, (c)
KSK, (d) Merbau
172
6.6
OD Error and UD Error by defect type: (a) Meranti, (b)
Rubberwood, (c) KSK, (d) Merbau
173
6.7
Detection performance for MC-FMCD and classic MD:
Meranti dataset
174
xvi
6.8
Detection performance for MC-FMCD and classic MD:
Rubberwood dataset
175
6.9
Detection performance for MC-FMCD and classic MD: KSK
dataset
176
6.10
Detection performance for MC-FMCD and classic MD:
Merbau dataset
177
6.11
Average detection performance by timber species
178
6.12
Average detection performance by defect type across timber
species (a) F score comparison between timber species by
defect type (b) Average F score by defect type
179
6.13
Average detection performance between MC-FMCD and
classic MD
180
6.14
Average detection performance validated by an expert
185
xvii
LIST OF ABBREVIATIONS
ANN
-
Artificial Neural Network
AUTOC
-
Autocorrelation
AVI
-
Automated Vision Inspection
BR
-
Brown Stain
BS
-
Blue Stain
CAR
-
Causal Auto Regressive Model
CCD
-
charged-coupled device
CL
-
Clear Wood
CONT
-
Contrast
COR
-
Correlation
CPROM
-
Cluster Prominence
CSHAD
-
Cluster Shade
CT
-
Computed Tomography
DENT
-
Difference entropy
DISS
-
Dissimilarity
DVAR
-
Difference variance
EN
-
Energy
ENT
-
Entropy
EPQ
-
Equal Probability Quantization
FMCD
-
Fast Minimum Covariance Determinant
FMMIS
-
Fuzzy Min-Max Neural Network for Image Segmentation
FN
-
False Negative
FP
-
False Positive
GA
-
Genetic Algorithm
GLDM
-
Grey Level Dependence Matrix
xviii
GPR
-
Ground Penetrating Radar
HL
-
Hole
HOMO
-
Homogeneity
IDMN
-
Inverse difference moment normalized
IDN
-
Inverse difference normalized
IMC1
-
Information measures of correlation 1
IMC2
-
Information measures of correlation 2
KN
-
Knot
KNN
-
K-nearest Neighbour
KSK
-
Kembang Semangkuk
LBP
-
Local Binary Pattern
MANOVA
-
Multivariate Analysis of Variance
MAXPR
-
Maximum probability
MCD
-
Minimum Covariance Determinant
MC-FMCD
-
Mahalanobian Classifier based on Robust FMCD
MD
-
Mahalanobis Distance
MGR
-
Malaysian Grading Rule
MIDA
-
Malaysian Investment Development Authority
MLP
-
Multi-layer Perceptron
MSE
-
Mean Square Error
MTIB
-
Malaysian Timber Industry Board
MVE
-
Minimum Volume Ellipsoid
MVV
-
Minimum Vector Variance
NATIP
-
National Timber Industry Policy
OCC
-
One Class Classifier
OD
-
Over Detection
PC
-
RBFN
-
Radial Basis Function Network
RGB
-
Red Green Blue
RT
-
Rot
SAVG
-
Sum Average
SDM
-
Spatial Dependence Matrix
SENT
-
Sum Entropy
xix
SOM
-
Self-organizing Map
SOSVH
-
Sum of Squares: Variance
SP
-
Split
SSCP
-
Sum of Squares Cross Product
SVAR
-
Sum Variance
TN
-
True Negative
TP
-
True Positive
UD
-
Under Detection
WN
-
Wane
xx
LIST OF APPENDICES
APPENDIX
A
TITLE
PAGE
Related studies on inspection of internal wood
defects
Related studies on multi sensors approach to timber
defect detection
213
B
Example of orientation independent GLDM and
normalized GLDM
216
C
Plots of feature value against displacement and
quantization parameter
219
D
Univariate feature range analysis
236
E
Matrix of scatter plots comparing feature
distribution between classes
247
F
Pairwise correlation between features and its
corresponding significance, p value
249
G
SPSS Manova output
252
H
Experimental dataset for various defect ratios
260
I
Expert validation sheet
267
J
UTM letter of permission for data collection
280
K
Biography of industry experts
284
L
Letter of dataset certification
287
M
Photo album
291
N
List of Publication
297
xxi
TERMS AND DEFINITIONS
TERM
DEFINITION
Wood
A hard fibrous material that makes up most of the substance of a
tree
Log
A part of the trunk that has been cut off from a felled tree
Timber
Wood boards sawn from logs
Primary wood
industry
Businesses that process logs or other tree sections directly
into timber, veneer, plywood, wood chips or other primary
wood products.
Sawmill
A factory where logs are sawn into timbers
Secondary wood
industry
Businesses that process primary wood products such as timber
into secondary wood products such as furniture, doors, and
parquet flooring.
Rough mill
The first production area/stage in a secondary wood product
industry where timber is being moulded and cut into rough sized
components/parts. At this stage, undesirable characteristics or
defects are removed.
Defect
Flaws or anomalies found on timber that affect its properties and
limit its possible use.
Natural defect
Biological defects occurred during the growth of a tree where the
timber originates from.
Mechanical
defect
Defects that are caused by the handling or processing of timber,
such as during drying, sawing and moulding.
Internal defect
Defects that are found inside the timber structure
External defect
Defects that are found on the surface of timber