Improving Triangle Geometry Shape Features Through Triangle Points Selection In Digit Recognition.
IMPROVING TRIANGLE GEOMETRY SHAPE FEATURES
THROUGH TRIANGLE POINTS SELECTION IN DIGIT
RECOGNITION
NUR ATIKAH BINTI ARBAIN
MASTER OF SCIENCE IN INFORMATION AND
COMMUNICATION TECHNOLOGY
2016
Faculty of Information and Communication Technology
IMPROVING TRIANGLE GEOMETRY SHAPE FEATURES
THROUGH TRIANGLE POINTS SELECTION IN DIGIT
RECOGNITION
Nur Atikah binti Arbain
Master of Science in Information and Communication Technology
2016
IMPROVING TRIANGLE GEOMETRY SHAPE FEATURES THROUGH
TRIANGLE POINTS SELECTION IN DIGIT RECOGNITION
NUR ATIKAH BINTI ARBAIN
A thesis submitted
in fulfillment of the requirements for the degree of Master of Science in Information
and Communication Technology
Faculty of Information and Communication Technology
UNIVERSITI TEKNIKAL MALAYSIA MELAKA
2016
DECLARATION
I declare that this thesis entitled “Improving Triangle Geometry Shape Features Through
Triangle Points Selection In Digit Recognition” is the result of my own research except as
cited in the references. The thesis has not been accepted for any degree and is not
concurrently submitted in candidature of any other degree.
Signature
:
……………………………………..
Name
:
NUR ATIKAH BINTI ARBAIN
Date
:
……………………………..………
APPROVAL
I hereby declare that I have read this thesis and in my opinion this thesis is sufficient in
term of scope and quality for the award of Master of Science in Information and
Communication Technology.
Signature
:
.………………………………….
Supervisor Name
:
DR. MOHD SANUSI BIN AZMI
Date
:
…………………..………………
DEDICATION
I would like to dedicate my work to my beloved family especially to my parent for their
non-stop supporting and teaching me since I was born. Also to my siblings (Syafwan,
Amirudin, Amanina, Mursyid) for always encourage and help me. This work is a guileless
and humble reply to their kindness. May Allah bless all of them.
ABSTRACT
Geometry features has been widely used in image processing especially in face
recognition, fingerprint recognition, digit recognition, vehicle detection and also in
intrusion. Among the commonly used geometry features are the features that are based on
triangle properties. Generally, triangle properties can be used to produce the features for
image classification. To produce these features, triangle geometry need to be formed based
on three coordinates which are the corners of A, B and C. However, not all triangle
formations can be formed from the three coordinates due to the condition where corners of
A, B and C may cause a straight line problem. The straight line problem occurs when the
chosen coordinates of the corners of A, B and C are in a straight line which causes the
triangle geometry impossible to be formed. On the other hand, the straight line occurs
when the gradient of corners A, B and C produces the equivalent value. This can be
proved by the experiment conducted to identify the gradient that has equivalent value
where the position of coordinates A, B and C will determine either the triangle can be
formed or vice versa. The purpose of this study is to suggest an improvement on triangle
geometry shape through triangle point selection. To achieve this purpose, there are two
objectives suggested for this study. They are: i) to propose straight line detection
technique for corner A, B and C of triangle; and ii) to improve triangle shape by proposing
location of corners based on dominant distribution of foreground image. In the
experiment, four types of digit dataset are chosen which are IFCHDB, HODA, MNIST
and BANGLA where each datasets is consisted of testing data and training data. The
Detection of Triangle Point Selection (DTPS) is proposed to detect the triangle point that
caused a straight line to be formed. Then, the straight line problem is solved using
Triangle Geometry Based Dominant Distribution of Foreground Image (TD2FI). Next, the
Triangle Features Based Summation of Gradient and Ratio (TSGR) and Enhancement of
Proposed Triangle Features using Absolute Value (EFTA) are proposed in order to
improve the classification accuracy result. The experimental results are yielded by
comparing the results of classification accuracy between the present proposed methods
with a prior proposed method using the supervised machine learning (SML). The SML
used are the Support Vector Machine (SVM) and the Multi-Layer Perceptron (MLP). The
result of classification accuracy has shown impressive results for TD2FI, TSGR and EFTA
methods through the SVM and MLP techniques whereas the datasets from IFCHDB,
HODA and BANGLA respectively have acquired good results through the SVM technique
while MNIST dataset has acquired the highest result of classification accuracy through the
MLP technique. The result of classification accuracy for TD2FI is 94.723% from IFCHDB
dataset, 97.295% from HODA dataset, 95.4% from MNIST dataset and 90.275% from
BANGLA dataset. In conclusion, the proposed method is capable of outstripping the
straight line issue based on the position of the coordinates of corners A, B and C as well as
produce a better result for classification accuracy.
i
ABSTRAK
Fitur-fitur segi tiga telah digunakan dengan meluas dalam pemprosesan imej terutamanya
pada pengecaman wajah, pengecaman cap jari, pengecaman digit, pengesanan kenderaan
dan juga dalam pencerobohan. Satu daripada fitur-fitur geometri ialah fitur yang
berdasarkan fitur-fitur segi tiga. Pada umumnya, fitur-fitur segi tiga boleh digunakan untuk
menghasilkan fitur untuk pengelasan imej. Untuk menghasilkan fitur, geometri segi tiga
perlu dibentuk berdasarkan tiga koordinat iaitu sudut A, B dan C. Walau bagaimanapun,
tidak semua pembentukan segi tiga akan dibentuk daripada tiga koordinat kerana sudut A,
B dan C boleh menyebabkan masalah garis lurus. Masalah garis lurus berlaku apabila
koordinat-koordinat yang terpilih iaitu sudut A, B dan C berada dalam keadaan garis selari
yang mana menyebabkan geometri segi tiga mustahil untuk dibentuk. Dalam erti kata lain,
garis lurus ini berlaku apabila nilai kecerunan sudut A, B dan C adalah sama. Ini boleh
dibuktikan berdasarkan eksperimen yang telah dijalankan untuk mengenal pasti kecerunan
yang mempunyai nilai yang sama di mana kedudukan koordinat A, B and C akan
menentukan sama ada segi tiga boleh ditubuhkan dan sebaliknya Tujuan kajian ini adalah
untuk mencadangkan penambahbaikan pada bentuk geometri segi tiga dengan
menggunakan titk-titik segi tiga yang terpilih. Bagi mencapai tujuan ini, terdapat dua
objektif dicadangkan untuk kajian ini. Objektifnya ialah i) mencadangkan teknik
pengesanan garis lurus bagi sudut A, B dan C segi tiga; dan ii) menambahbaik bentuk segi
tiga dengan mencadangkan lokasi sudut berdasarkan taburan dominan imej latar depan.
Dalam eksperimen, empat jenis digit set data dipilih iaitu IFCHDB, HODA, MNIST dan
BANGLA dimana setiap set data terdiri daripada data uij dan data latihan. Pengesanan
Titik Segitiga Pemilihan dicadangkan untuk mengesan titik segitiga yang menyebabkan
garis lurus terhasil. Kemudian, masalah garis lurus diselesaikan dengan menggunakan
Segitiga Geometri Menggunakan Taburan Dominan Imej Latar Depan (STDIL). Seterusnya,
Fitur Segitiga Berdasarkan Jumlah Bagi Kecerunan dan Nisbah (FJKN) dan
Penambahbaikan Fitur Segitiga Cadangan Menggunakan Nilai Mutlak (PSCM) di
cadangkan bagi meningkatkan keputusan pengklasifikasian ketepatan. Keputusan
pengklasifikasian ketepatan akan dibandingkan antara cadangan sebelum ini dengan
cadangan kajian ini dengan menggunakan Pembelajaran Mesin Terselia (PMT). PMT yang
digunakan ialah Pembelajaran Mesin Sokongan Vektor (PMSV) dan Perseptron Multi-Aras
(PMA). Keputusan pengklasifikasian ketepatan telah menunjukkan keputusan yang baik
bagi kaedah STDLI, FJKN dan PSCM melalui teknik PMSV dan PMT dimana set data
IFCHDB, HODA dan BANGLA telah memperolehi keputusan yang tertinggi melalui teknik
PMSV manakala set data MNIST melalui teknik PMA. Keputusan ketepatan bagi STDLI
menunjukkan keputusan yang baik iaitu 94.723% untuk IFCHDB, 97.295% untuk HODA,
95.4% untuk MNIST dan 90.275% untuk BANGLA. Kesimpulannya, kaedah-kaedah yang
dicadangkan mampu mengatasi isu garis lurus berdasarkan kedudukan koordinat sudut A,
B dan C serta menghasilkan keputusan ketepatan yang lebih baik.
ii
ACKNOWLEDGEMENTS
Alhamdulillah, thanks to Allah S.W.T as this project is fully completed. I hereby
would like to take this opportunity to thank all persons who had been generously involved
in helping and assisting me while I was completing this thesis to graduate with Master of
Science (by research) at Universiti Teknikal Malaysia Melaka (UTeM).
I would firstly like to express my deepest gratitude and thanks to my supervisors,
Dr. Mohd Sanusi bin Azmi and Associate Professor Dr. Azah Kamilah Muda from Faculty
of Information and Communication Technology Universiti Teknikal Malaysia Melaka
(UTeM) for their undivided support whether morally and physically, assistance, guidance
and tolerance, which had proved to be invaluable in the completion of my thesis.
I would also like to thank Dr. Sharifah Sakinah Syed Ahmad for her guidance and
kindness in sharing her knowledge during the course of completing this thesis. I would like
to take this opportunity to express my appreciation to my family and friends for their
patience, understanding and also for their undivided support given to me throughout the
completion of my project. Last but not least, to all my colleagues and friends struggling for
Master’s who inspire a vision, providing guidance and sharing experiences.
iii
TABLE OF CONTENTS
PAGE
DECLARATION
APPROVAL
DEDICATION
ABSTRACT
ABSTRAK
ACKNOWLEDGEMENTS
TABLE OF CONTENTS
LIST OF TABLES
LIST OF FIGURES
LIST OF APPENDICES
LIST OF ABBREVIATIONS
LIST OF PUBLICATIONS
i
ii
iii
iv
vii
ix
xii
xiii
xiv
CHAPTER
1.
INTRODUCTION
1.1 Introduction
1.2 Research Background
1.3 Research Problem
1.4 Research Questions
1.5 Research Objectives
1.6 Research Significance
1.7 Research Scopes
1.8 Expected Outcomes
1.9 Thesis Organisation
1.10 Summary
1
1
3
5
7
7
8
9
9
10
11
2.
LITERATURE REVIEW
2.1 Introduction
2.2 Handwriting Recognition
2.2.1 Digit Recognition
2.3 Triangle Geometry Method
2.4 Related Work
2.4.1 Fingerprint Recognition
2.4.2 Face Recognition
2.5 Triangle Geometry Problems in Previous Research
2.6 Summary
12
12
12
15
22
24
24
31
34
35
3.
RESEARCH FRAMEWORK
3.1 Introduction
3.2 Research Framework
3.2.1 Investigation Phase
3.2.2 Implementation Phase
3.3 Data Collection and Data Preparation
3.3.1 Digit Image of IFCHDB Dataset
3.3.2 Digit Image of HODA Dataset
3.3.3 Digit Image of MNIST Dataset
iv
37
37
38
38
41
41
42
44
45
3.3.4 Digit Image of BANGLA Dataset
Pre-Processing
Proposed Feature Extraction Method
Validation and Analyze Proposed Method
Operational Framework
Experimental Design
3.8.1 Experiment I
3.8.2 Experiment II
3.9 Research Tool
3.10 Summary
47
48
49
51
52
53
53
54
54
55
4.
DETECTION OF TRIANGLE POINT SELECTION (DTPS)
4.1 Introduction
4.2 Overview of Detection of Triangle Point Selection (DTPS)
4.3 Development of Detection of Triangle Point Selection (DTPS)
4.3.1 Detection of Triangle Point in Scalene Algorithm
4.3.2 Record Selected Triangle Points
4.3.3 Result of DTPS
4.4 Summary
56
56
56
57
58
59
60
63
5.
AN ENHANCEMENT OF TRIANGLE GEOMETRY SHAPE
5.1 Introduction
5.2 Overview of Proposed Method
5.3 Triangle Geometry Using Dominant Distribution of Foreground
Image (TD2DI)
5.3.1 Procedure of TD2DI
5.4 Triangle Features Based Summation of Gradient and Ratio (TSGR)
5.4.1 Procedure of TSGR
5.5 An Enhancement of Proposed Triangle Features Using Absolute
Value (EFTA)
5.5.1 Procedure of EFTA
5.6 Performance Analysis
5.7 Statistical Validation
5.8 Summary
64
64
65
3.4
3.5
3.6
3.7
3.8
6.
7.
65
67
72
74
75
75
77
80
92
DISCUSSIONS
6.1 Introduction
6.2 Research Summary
6.3 Research Findings
6.3.1 To propose straight line detection technique for triangle points
6.3.2 To improve triangle shape by proposing location of corners
based on dominant distribution of foreground image
6.4 Research Contributions
6.5 Summary
93
93
93
94
94
CONCLUSION
7.1 Introduction
7.2 Conclusion
7.3 Research Limitations
98
98
98
99
v
95
96
97
7.4
7.5
Future Work or Further Research
Summary
100
100
REFERENCES
APPENDIX A
APPENDIX B
101
114
118
vi
LIST OF TABLES
TABLE
TITLE
PAGE
1.1
Summary of research problems, questions and objectives
1.2
Description of thesis organization
10
2.1
Position of y-coordinate (Azmi et al., 2011a)
17
2.2
Formulas for triangle features (M. S. Azmi, 2013)
19
3.1
Summary of investigation phase
39
3.2
Classification accuracy result from several prior proposed methods (%)
40
3.3
Software description
54
3.4
Hardware description
55
4.1
Positions of coordinate y for triangle centre point
58
4.2
Summary of total occurrence of straight lines
61
5.1
Fragments involved for each points of corner
70
5.2
Rules applied in TD2FI
70
5.3
Description of features in TSGR
73
5.4
Cost (c) and Gamma (γ) for each digit datasets
77
5.5
Result of classification accuracy using SVM technique
78
5.6
Result of classification accuracy using MLP technique
78
5.7
Normality tests for TD2FI, TSGR, EFTA and MSA
82
5.8
Homogeneity of variances results for TD2FI, TSGR and EFTA
83
vii
8
5.9
Results of robust tests of equality of means for TD2FI, TSGR and EFTA
83
5.10
Descriptive results for MSA, TD2FI, TSGR and EFTA
84
5.11
ANOVA results for TD2FI, TSGR and EFTA
85
5.12
Results of Tukey HSD for SVM classification accuracy
86
5.13
Results of Tukey HSD for MLP classification accuracy
87
5.14
Results of Games-Howell for SVM classification accuracy
88
5.15
Results of Games-Howell for MLP classification accuracy
89
5.16
Results of Levene test for TD2FI and MSA
90
5.17
Results of Independent samples t-test for TD2FI and MSA
91
viii
LIST OF FIGURES
FIGURE
TITLE
PAGE
1.1
Examples of straight line condition in triangle formation
6
2.1
Characterization loci features (Ebrahimpour et al., 2009)
16
2.2
Digit images from HODA dataset (Ebrahimpour et al., 2009)
16
2.3
Segregation of isolated characters into four parts (Azmi et al., 2011b)
17
2.4
25 Regions for extracting triangle features (Azmi et al., 2012)
19
2.5
Segregation of isolated binary image into two parts (M. S. Azmi, 2013)
21
2.6
Researches in digit recognition
22
2.7
Local triangle structure of fingerprint (Chen et al., 2006)
25
2.8
Similar vector triangle between two triangles (Zheng et al., 2009)
26
2.9
Positions of minutiae and parameter in triangle formation (Lai and
27
Suandi, 2011)
2.10
Conversion from minutiae representation into integer vector
28
2.11
The Delaunay Triangulation Net in (Yang et al., 2013)
29
2.12
Extended set example: a) Delaunay triangulation with point x, b)
30
Delaunay triangulation without point x, c) Combination of Delaunay
triangulation and Triangular hull of point x (Kavati et al., 2014)
2.13
The triangle spiral used by Jain and Prasad (2015)
31
2.14
Frontal and slant view of triangle feature (Gao and Xu, 2012)
32
ix
2.15
Extraction of contour features by (Zhang et al., 2012)
33
2.16
Illustration of perceptive projection of a 3D triangle by (Tin et al., 2009)
33
3.1
Research Framework
38
3.2
Digit datasets for data collection used in this study
42
3.3
Examples of IFCHDB dataset
43
3.4
Training and testing dataset of IFCHDB
44
3.5
Examples of testing and training images from HODA digit dataset
45
3.6
Algorithm for classification and categorizing HODA digit dataset
45
3.7
Process of extracting the MNIST digit dataset
46
3.8
Examples of testing and training images from MNIST dataset
47
3.9
Algorithm for classification and categorize BANGLA digit dataset
48
3.10
Pre-processing (M. S. Azmi, 2013)
49
3.11
Proposed Feature Extraction Method
50
3.12
Operational Framework
53
4.1
Phases in DTPS development
57
4.2
The 6 types of formation for scalene triangle shape
59
4.3
Process of recording the results of triangle points into CSV file
60
4.4
Examples of digit image with straight line problem
61
4.5
DTPS technique applied in scalene algorithm
62
5.1
Proposed techniques for enhancing the triangle geometry shape
65
5.2
Disparity of flow chart between proposed method by M. S. Azmi (2013)
66
and present proposed method
5.3
Cartesian Plane Zone (M. S. Azmi, 2013)
68
5.4
Illustration of binary image for Zone A
68
x
5.5
Isolation of Zone A into four parts
69
5.6
The illustration of straight line is solved using TD2FI
71
5.7
Algorithm of TD2FI for solving straight line problem
72
5.8
Illustration of combination of non-related features
73
5.9
Process of TSGR technique
74
5.10
Process of EPTA technique
76
5.11
Result of classification accuracy (SVM)
78
5.12
Result of classification accuracy (MLP)
79
xi
LIST OF APPENDICES
APPENDIX
A
TITLE
Summary of Triangle Geometry Method in Various
PAGE
114
Recognition Area
B
Grid Search Results for SVM Classification Accuracy
xii
118
LIST OF ABBREVIATIONS
DTPS
-
Detection of Triangle Point Selection
EFTA
-
Enhancement of Proposed Triangle Features Using
Absolute Value
ETSG
-
An Enhancement of Triangle Shape Using Triangle
Geometry
LIBSVM
-
Library Support Vector Machine
SSPS
-
Statistical Solution Problem Software
SVM
-
Support Vector Machine
TD2FI
-
Triangle Geometry Using Dominant Distribution of
Foreground Image
TSGR
-
Triangle Feature Based Summation of Gradient and
Ratio
WEKA
-
Waikato Environment for Knowledge Analysis
xiii
LIST OF PUBLICATIONS
Azmi, M.S., Arbain, N.A., Muda, A.K., Abal Abas, Z., and Muslim, Z., 2015. Data
Normalization for Triangle Features by Adapting Triangle Nature for better Classification.
2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing
Technologies, pp.1–4.
Arbain, N.A., Azmi, M.S., Melhem, L.B., Muda, A.K., and Rashaideh, H., 2016.
Enhancement of Triangle Coordinates For Triangle Features For Better Classification.
Jordanian Journal of Computers and Information Technology (JJCIT), 2 (2), pp.108–119.
N. A. Arbain, M. S. Azmi, S. S. S. Ahmad, I. E. A. Jalil, M. Z. Masud, and M. A. Lateh,
2016, “Detection on Straight Line Problem in Triangle Geometry Features for Digit
Recognition,” in Multi-Conference on Artificial Intelligence Technology (M-CAIT 2016).
N. A. Arbain, M. S. Azmi, A. K. Muda, I. E. A. Jalil, and M. A. Aziz, 2016, “Current
Work on Digital Paleography,” Jurnal Teknologi. (Sciences Engineering).
N. A. Arbain, M. S. Azmi, S. Sakinah, S. Ahmad, A. K. Muda, I. E. A. Jalil, and T. K.
Ming, 2016, “Dynamic Similarity Distance with Mean Average Precision Tool,” in MultiConference on Artificial Intelligence Technology (M-CAIT 2016).
xiv
N. A. Arbain, M. S. Azmi, A. K. Muda, N. A. Muda, and R. Nordin, 2016, “An
Improvement on triangle Features for Digit Recognition through Combination of Features
by Adapting Triangle Geometry,” Jurnal Teknologi. (Sciences Engineering).
xv
CHAPTER 1
INTRODUCTION
1.1
Introduction
Digit recognition is an area which has been actively explored particularly to identify
manuscript origin details such as originality, writing date, provenance, as well as number
of authors (Chaudhuri, 2006; Bulacu et al., 2007; Pan et al., 2009). The studies in digit
recognition for ROMAN characters handwriting was explored over four decades ago
(Knoll, 1969). In meantime, no publicly was available for standard datasets that can be
used by the researchers. However, the researches and development in digit recognition was
gone on a swift expansion in the last decade. The Modified NIST dataset (MNIST) was a
largest dataset for ROMAN handwriting has been developed as a result of handwritten
digit classification competition which has been held in summer of 1992 (Borji et al., 2008).
The studies of digit recognition had evolved rapidly along with improvement made on
prior methods and techniques (Sundaresan and Lin, 1998; Liu et al., 2003, 2004; Kussul
and Baidyk, 2004; Labusch et al., 2008, 2010; Moradi et al., 2010; Mirsharif et al.,
2012).
In digit recognition, the image datasets used which include CEDAR, MNIST,
HODA, BANGLA and IFCHDB (Bhowmik et al., 2004;
Kussul and Baidyk,
2004; Verma et al., 2004; Alaei et al., 2009; Javidi and Sharifizadeh, 2012; M. S. Azmi,
2013). Since images are used, image processing is required to process the images, for
example to convert image into binary pixels. Image processing is one of the vital elements
1
that is widely used in research area within engineering and computer science disciplines.
Among rapidly growing technology today, various applications were developed in order to
conduct a process of pre-processing of the selected images such as remote sensing, medical
diagnosis, industrial applications, robotics, and so on and so forth. These applications are
used to assist researchers to handle the process of feature extraction in image processing.
Feature extraction is an important stage where the input data will be converted into
a reduced sets of feature when the input data to an algorithm is too big to be handled. In
other words, the input data are transformed into a reduced sets of feature vector (Huang
and Zhou, 2010). This process is a vital stage generating feature vectors. If extracted
features are chosen correctly, the feature sets will consist of important information from
input data. This enable to analyse following tasks by using the reduced sets rather than the
whole size of input. There are several techniques are frequently used to extract images
such as Image Processing Basics, Sobel Edge Detection, Canny Edge Detection and Hough
Transformation (Aichert, 2008).
Recently, the triangle geometry method has been used to extract images. M. S.
Azmi (2013) had proposed new features from triangle properties by using the triangle
geometry method to extract digit images. The triangle geometry method also has been
extensively applied in biometric research such as face recognition and fingerprint
recognition (Jin et al., 2009; Tin et al., 2009; Lai and Suandi, 2011; Gao and Xu, 2012).
Besides, the triangle geometry also had been adopted in intrusion, vehicle detection and
digit recognition (Mozaffari et al., 2007; Haselhoff and Kummert, 2009; Ebrahimpour et
al., 2010; Tang et al., 2010; M. S. Azmi, 2013).
In triangle geometry, three points of triangle are required in forming a triangle
shape. However, problem in determining three points of triangle had caused modelling
problems. The problem occurred when the value of gradient for the generated three points
2
of triangle are equivalent and causing the three points of triangle’s position to be inline.
The inline position for three points of triangle eventually leads to the formation of a
straight line.
Thus, this study aims to solve the problem in determining three triangle points. The
triangle points need to be identified to form a triangle shape. Selected triangle points are to
be used to assist in producing the proposed method for triangle formation solution in
solving straight line problem. The solution to straight line problem is proposed in order to
improve the triangle geometry shape through three triangle points. The validation of
proposed method is carried on in order to evaluate the performance of the proposed
method.
1.2
Research Background
Feature extraction is an important task in image processing because of the
meaningful features extracted are vital in representing an object. Object is modelled and
represented by geometric forms through geometrical properties. The triangle geometry
properties such as angles of corners, ratios of sides and gradients of corners can be used to
produce triangle features. Thus, triangle shape needs to be formed to acquire triangle
properties. A triangle can be formed based on three points that linked to each other. (Page,
2015).
The triangle geometry method has been widely used in recognition area such as
face and fingerprint recognition. In face recognition, triangle points are acquired based on
body elements such as nasal tip, eyes, nose and mouth (Tin et al., 2009; Gao and Xu,
2012; Zhang et al., 2012). For fingerprint recognition, triangle points are attained based on
minutiae (Ghazvini et al., 2011; Gago-Alonso et al., 2013; Yang et al., 2013; Kavati et
al., 2014; Jadied, 2015; Jain and Prasad, 2015).
3
THROUGH TRIANGLE POINTS SELECTION IN DIGIT
RECOGNITION
NUR ATIKAH BINTI ARBAIN
MASTER OF SCIENCE IN INFORMATION AND
COMMUNICATION TECHNOLOGY
2016
Faculty of Information and Communication Technology
IMPROVING TRIANGLE GEOMETRY SHAPE FEATURES
THROUGH TRIANGLE POINTS SELECTION IN DIGIT
RECOGNITION
Nur Atikah binti Arbain
Master of Science in Information and Communication Technology
2016
IMPROVING TRIANGLE GEOMETRY SHAPE FEATURES THROUGH
TRIANGLE POINTS SELECTION IN DIGIT RECOGNITION
NUR ATIKAH BINTI ARBAIN
A thesis submitted
in fulfillment of the requirements for the degree of Master of Science in Information
and Communication Technology
Faculty of Information and Communication Technology
UNIVERSITI TEKNIKAL MALAYSIA MELAKA
2016
DECLARATION
I declare that this thesis entitled “Improving Triangle Geometry Shape Features Through
Triangle Points Selection In Digit Recognition” is the result of my own research except as
cited in the references. The thesis has not been accepted for any degree and is not
concurrently submitted in candidature of any other degree.
Signature
:
……………………………………..
Name
:
NUR ATIKAH BINTI ARBAIN
Date
:
……………………………..………
APPROVAL
I hereby declare that I have read this thesis and in my opinion this thesis is sufficient in
term of scope and quality for the award of Master of Science in Information and
Communication Technology.
Signature
:
.………………………………….
Supervisor Name
:
DR. MOHD SANUSI BIN AZMI
Date
:
…………………..………………
DEDICATION
I would like to dedicate my work to my beloved family especially to my parent for their
non-stop supporting and teaching me since I was born. Also to my siblings (Syafwan,
Amirudin, Amanina, Mursyid) for always encourage and help me. This work is a guileless
and humble reply to their kindness. May Allah bless all of them.
ABSTRACT
Geometry features has been widely used in image processing especially in face
recognition, fingerprint recognition, digit recognition, vehicle detection and also in
intrusion. Among the commonly used geometry features are the features that are based on
triangle properties. Generally, triangle properties can be used to produce the features for
image classification. To produce these features, triangle geometry need to be formed based
on three coordinates which are the corners of A, B and C. However, not all triangle
formations can be formed from the three coordinates due to the condition where corners of
A, B and C may cause a straight line problem. The straight line problem occurs when the
chosen coordinates of the corners of A, B and C are in a straight line which causes the
triangle geometry impossible to be formed. On the other hand, the straight line occurs
when the gradient of corners A, B and C produces the equivalent value. This can be
proved by the experiment conducted to identify the gradient that has equivalent value
where the position of coordinates A, B and C will determine either the triangle can be
formed or vice versa. The purpose of this study is to suggest an improvement on triangle
geometry shape through triangle point selection. To achieve this purpose, there are two
objectives suggested for this study. They are: i) to propose straight line detection
technique for corner A, B and C of triangle; and ii) to improve triangle shape by proposing
location of corners based on dominant distribution of foreground image. In the
experiment, four types of digit dataset are chosen which are IFCHDB, HODA, MNIST
and BANGLA where each datasets is consisted of testing data and training data. The
Detection of Triangle Point Selection (DTPS) is proposed to detect the triangle point that
caused a straight line to be formed. Then, the straight line problem is solved using
Triangle Geometry Based Dominant Distribution of Foreground Image (TD2FI). Next, the
Triangle Features Based Summation of Gradient and Ratio (TSGR) and Enhancement of
Proposed Triangle Features using Absolute Value (EFTA) are proposed in order to
improve the classification accuracy result. The experimental results are yielded by
comparing the results of classification accuracy between the present proposed methods
with a prior proposed method using the supervised machine learning (SML). The SML
used are the Support Vector Machine (SVM) and the Multi-Layer Perceptron (MLP). The
result of classification accuracy has shown impressive results for TD2FI, TSGR and EFTA
methods through the SVM and MLP techniques whereas the datasets from IFCHDB,
HODA and BANGLA respectively have acquired good results through the SVM technique
while MNIST dataset has acquired the highest result of classification accuracy through the
MLP technique. The result of classification accuracy for TD2FI is 94.723% from IFCHDB
dataset, 97.295% from HODA dataset, 95.4% from MNIST dataset and 90.275% from
BANGLA dataset. In conclusion, the proposed method is capable of outstripping the
straight line issue based on the position of the coordinates of corners A, B and C as well as
produce a better result for classification accuracy.
i
ABSTRAK
Fitur-fitur segi tiga telah digunakan dengan meluas dalam pemprosesan imej terutamanya
pada pengecaman wajah, pengecaman cap jari, pengecaman digit, pengesanan kenderaan
dan juga dalam pencerobohan. Satu daripada fitur-fitur geometri ialah fitur yang
berdasarkan fitur-fitur segi tiga. Pada umumnya, fitur-fitur segi tiga boleh digunakan untuk
menghasilkan fitur untuk pengelasan imej. Untuk menghasilkan fitur, geometri segi tiga
perlu dibentuk berdasarkan tiga koordinat iaitu sudut A, B dan C. Walau bagaimanapun,
tidak semua pembentukan segi tiga akan dibentuk daripada tiga koordinat kerana sudut A,
B dan C boleh menyebabkan masalah garis lurus. Masalah garis lurus berlaku apabila
koordinat-koordinat yang terpilih iaitu sudut A, B dan C berada dalam keadaan garis selari
yang mana menyebabkan geometri segi tiga mustahil untuk dibentuk. Dalam erti kata lain,
garis lurus ini berlaku apabila nilai kecerunan sudut A, B dan C adalah sama. Ini boleh
dibuktikan berdasarkan eksperimen yang telah dijalankan untuk mengenal pasti kecerunan
yang mempunyai nilai yang sama di mana kedudukan koordinat A, B and C akan
menentukan sama ada segi tiga boleh ditubuhkan dan sebaliknya Tujuan kajian ini adalah
untuk mencadangkan penambahbaikan pada bentuk geometri segi tiga dengan
menggunakan titk-titik segi tiga yang terpilih. Bagi mencapai tujuan ini, terdapat dua
objektif dicadangkan untuk kajian ini. Objektifnya ialah i) mencadangkan teknik
pengesanan garis lurus bagi sudut A, B dan C segi tiga; dan ii) menambahbaik bentuk segi
tiga dengan mencadangkan lokasi sudut berdasarkan taburan dominan imej latar depan.
Dalam eksperimen, empat jenis digit set data dipilih iaitu IFCHDB, HODA, MNIST dan
BANGLA dimana setiap set data terdiri daripada data uij dan data latihan. Pengesanan
Titik Segitiga Pemilihan dicadangkan untuk mengesan titik segitiga yang menyebabkan
garis lurus terhasil. Kemudian, masalah garis lurus diselesaikan dengan menggunakan
Segitiga Geometri Menggunakan Taburan Dominan Imej Latar Depan (STDIL). Seterusnya,
Fitur Segitiga Berdasarkan Jumlah Bagi Kecerunan dan Nisbah (FJKN) dan
Penambahbaikan Fitur Segitiga Cadangan Menggunakan Nilai Mutlak (PSCM) di
cadangkan bagi meningkatkan keputusan pengklasifikasian ketepatan. Keputusan
pengklasifikasian ketepatan akan dibandingkan antara cadangan sebelum ini dengan
cadangan kajian ini dengan menggunakan Pembelajaran Mesin Terselia (PMT). PMT yang
digunakan ialah Pembelajaran Mesin Sokongan Vektor (PMSV) dan Perseptron Multi-Aras
(PMA). Keputusan pengklasifikasian ketepatan telah menunjukkan keputusan yang baik
bagi kaedah STDLI, FJKN dan PSCM melalui teknik PMSV dan PMT dimana set data
IFCHDB, HODA dan BANGLA telah memperolehi keputusan yang tertinggi melalui teknik
PMSV manakala set data MNIST melalui teknik PMA. Keputusan ketepatan bagi STDLI
menunjukkan keputusan yang baik iaitu 94.723% untuk IFCHDB, 97.295% untuk HODA,
95.4% untuk MNIST dan 90.275% untuk BANGLA. Kesimpulannya, kaedah-kaedah yang
dicadangkan mampu mengatasi isu garis lurus berdasarkan kedudukan koordinat sudut A,
B dan C serta menghasilkan keputusan ketepatan yang lebih baik.
ii
ACKNOWLEDGEMENTS
Alhamdulillah, thanks to Allah S.W.T as this project is fully completed. I hereby
would like to take this opportunity to thank all persons who had been generously involved
in helping and assisting me while I was completing this thesis to graduate with Master of
Science (by research) at Universiti Teknikal Malaysia Melaka (UTeM).
I would firstly like to express my deepest gratitude and thanks to my supervisors,
Dr. Mohd Sanusi bin Azmi and Associate Professor Dr. Azah Kamilah Muda from Faculty
of Information and Communication Technology Universiti Teknikal Malaysia Melaka
(UTeM) for their undivided support whether morally and physically, assistance, guidance
and tolerance, which had proved to be invaluable in the completion of my thesis.
I would also like to thank Dr. Sharifah Sakinah Syed Ahmad for her guidance and
kindness in sharing her knowledge during the course of completing this thesis. I would like
to take this opportunity to express my appreciation to my family and friends for their
patience, understanding and also for their undivided support given to me throughout the
completion of my project. Last but not least, to all my colleagues and friends struggling for
Master’s who inspire a vision, providing guidance and sharing experiences.
iii
TABLE OF CONTENTS
PAGE
DECLARATION
APPROVAL
DEDICATION
ABSTRACT
ABSTRAK
ACKNOWLEDGEMENTS
TABLE OF CONTENTS
LIST OF TABLES
LIST OF FIGURES
LIST OF APPENDICES
LIST OF ABBREVIATIONS
LIST OF PUBLICATIONS
i
ii
iii
iv
vii
ix
xii
xiii
xiv
CHAPTER
1.
INTRODUCTION
1.1 Introduction
1.2 Research Background
1.3 Research Problem
1.4 Research Questions
1.5 Research Objectives
1.6 Research Significance
1.7 Research Scopes
1.8 Expected Outcomes
1.9 Thesis Organisation
1.10 Summary
1
1
3
5
7
7
8
9
9
10
11
2.
LITERATURE REVIEW
2.1 Introduction
2.2 Handwriting Recognition
2.2.1 Digit Recognition
2.3 Triangle Geometry Method
2.4 Related Work
2.4.1 Fingerprint Recognition
2.4.2 Face Recognition
2.5 Triangle Geometry Problems in Previous Research
2.6 Summary
12
12
12
15
22
24
24
31
34
35
3.
RESEARCH FRAMEWORK
3.1 Introduction
3.2 Research Framework
3.2.1 Investigation Phase
3.2.2 Implementation Phase
3.3 Data Collection and Data Preparation
3.3.1 Digit Image of IFCHDB Dataset
3.3.2 Digit Image of HODA Dataset
3.3.3 Digit Image of MNIST Dataset
iv
37
37
38
38
41
41
42
44
45
3.3.4 Digit Image of BANGLA Dataset
Pre-Processing
Proposed Feature Extraction Method
Validation and Analyze Proposed Method
Operational Framework
Experimental Design
3.8.1 Experiment I
3.8.2 Experiment II
3.9 Research Tool
3.10 Summary
47
48
49
51
52
53
53
54
54
55
4.
DETECTION OF TRIANGLE POINT SELECTION (DTPS)
4.1 Introduction
4.2 Overview of Detection of Triangle Point Selection (DTPS)
4.3 Development of Detection of Triangle Point Selection (DTPS)
4.3.1 Detection of Triangle Point in Scalene Algorithm
4.3.2 Record Selected Triangle Points
4.3.3 Result of DTPS
4.4 Summary
56
56
56
57
58
59
60
63
5.
AN ENHANCEMENT OF TRIANGLE GEOMETRY SHAPE
5.1 Introduction
5.2 Overview of Proposed Method
5.3 Triangle Geometry Using Dominant Distribution of Foreground
Image (TD2DI)
5.3.1 Procedure of TD2DI
5.4 Triangle Features Based Summation of Gradient and Ratio (TSGR)
5.4.1 Procedure of TSGR
5.5 An Enhancement of Proposed Triangle Features Using Absolute
Value (EFTA)
5.5.1 Procedure of EFTA
5.6 Performance Analysis
5.7 Statistical Validation
5.8 Summary
64
64
65
3.4
3.5
3.6
3.7
3.8
6.
7.
65
67
72
74
75
75
77
80
92
DISCUSSIONS
6.1 Introduction
6.2 Research Summary
6.3 Research Findings
6.3.1 To propose straight line detection technique for triangle points
6.3.2 To improve triangle shape by proposing location of corners
based on dominant distribution of foreground image
6.4 Research Contributions
6.5 Summary
93
93
93
94
94
CONCLUSION
7.1 Introduction
7.2 Conclusion
7.3 Research Limitations
98
98
98
99
v
95
96
97
7.4
7.5
Future Work or Further Research
Summary
100
100
REFERENCES
APPENDIX A
APPENDIX B
101
114
118
vi
LIST OF TABLES
TABLE
TITLE
PAGE
1.1
Summary of research problems, questions and objectives
1.2
Description of thesis organization
10
2.1
Position of y-coordinate (Azmi et al., 2011a)
17
2.2
Formulas for triangle features (M. S. Azmi, 2013)
19
3.1
Summary of investigation phase
39
3.2
Classification accuracy result from several prior proposed methods (%)
40
3.3
Software description
54
3.4
Hardware description
55
4.1
Positions of coordinate y for triangle centre point
58
4.2
Summary of total occurrence of straight lines
61
5.1
Fragments involved for each points of corner
70
5.2
Rules applied in TD2FI
70
5.3
Description of features in TSGR
73
5.4
Cost (c) and Gamma (γ) for each digit datasets
77
5.5
Result of classification accuracy using SVM technique
78
5.6
Result of classification accuracy using MLP technique
78
5.7
Normality tests for TD2FI, TSGR, EFTA and MSA
82
5.8
Homogeneity of variances results for TD2FI, TSGR and EFTA
83
vii
8
5.9
Results of robust tests of equality of means for TD2FI, TSGR and EFTA
83
5.10
Descriptive results for MSA, TD2FI, TSGR and EFTA
84
5.11
ANOVA results for TD2FI, TSGR and EFTA
85
5.12
Results of Tukey HSD for SVM classification accuracy
86
5.13
Results of Tukey HSD for MLP classification accuracy
87
5.14
Results of Games-Howell for SVM classification accuracy
88
5.15
Results of Games-Howell for MLP classification accuracy
89
5.16
Results of Levene test for TD2FI and MSA
90
5.17
Results of Independent samples t-test for TD2FI and MSA
91
viii
LIST OF FIGURES
FIGURE
TITLE
PAGE
1.1
Examples of straight line condition in triangle formation
6
2.1
Characterization loci features (Ebrahimpour et al., 2009)
16
2.2
Digit images from HODA dataset (Ebrahimpour et al., 2009)
16
2.3
Segregation of isolated characters into four parts (Azmi et al., 2011b)
17
2.4
25 Regions for extracting triangle features (Azmi et al., 2012)
19
2.5
Segregation of isolated binary image into two parts (M. S. Azmi, 2013)
21
2.6
Researches in digit recognition
22
2.7
Local triangle structure of fingerprint (Chen et al., 2006)
25
2.8
Similar vector triangle between two triangles (Zheng et al., 2009)
26
2.9
Positions of minutiae and parameter in triangle formation (Lai and
27
Suandi, 2011)
2.10
Conversion from minutiae representation into integer vector
28
2.11
The Delaunay Triangulation Net in (Yang et al., 2013)
29
2.12
Extended set example: a) Delaunay triangulation with point x, b)
30
Delaunay triangulation without point x, c) Combination of Delaunay
triangulation and Triangular hull of point x (Kavati et al., 2014)
2.13
The triangle spiral used by Jain and Prasad (2015)
31
2.14
Frontal and slant view of triangle feature (Gao and Xu, 2012)
32
ix
2.15
Extraction of contour features by (Zhang et al., 2012)
33
2.16
Illustration of perceptive projection of a 3D triangle by (Tin et al., 2009)
33
3.1
Research Framework
38
3.2
Digit datasets for data collection used in this study
42
3.3
Examples of IFCHDB dataset
43
3.4
Training and testing dataset of IFCHDB
44
3.5
Examples of testing and training images from HODA digit dataset
45
3.6
Algorithm for classification and categorizing HODA digit dataset
45
3.7
Process of extracting the MNIST digit dataset
46
3.8
Examples of testing and training images from MNIST dataset
47
3.9
Algorithm for classification and categorize BANGLA digit dataset
48
3.10
Pre-processing (M. S. Azmi, 2013)
49
3.11
Proposed Feature Extraction Method
50
3.12
Operational Framework
53
4.1
Phases in DTPS development
57
4.2
The 6 types of formation for scalene triangle shape
59
4.3
Process of recording the results of triangle points into CSV file
60
4.4
Examples of digit image with straight line problem
61
4.5
DTPS technique applied in scalene algorithm
62
5.1
Proposed techniques for enhancing the triangle geometry shape
65
5.2
Disparity of flow chart between proposed method by M. S. Azmi (2013)
66
and present proposed method
5.3
Cartesian Plane Zone (M. S. Azmi, 2013)
68
5.4
Illustration of binary image for Zone A
68
x
5.5
Isolation of Zone A into four parts
69
5.6
The illustration of straight line is solved using TD2FI
71
5.7
Algorithm of TD2FI for solving straight line problem
72
5.8
Illustration of combination of non-related features
73
5.9
Process of TSGR technique
74
5.10
Process of EPTA technique
76
5.11
Result of classification accuracy (SVM)
78
5.12
Result of classification accuracy (MLP)
79
xi
LIST OF APPENDICES
APPENDIX
A
TITLE
Summary of Triangle Geometry Method in Various
PAGE
114
Recognition Area
B
Grid Search Results for SVM Classification Accuracy
xii
118
LIST OF ABBREVIATIONS
DTPS
-
Detection of Triangle Point Selection
EFTA
-
Enhancement of Proposed Triangle Features Using
Absolute Value
ETSG
-
An Enhancement of Triangle Shape Using Triangle
Geometry
LIBSVM
-
Library Support Vector Machine
SSPS
-
Statistical Solution Problem Software
SVM
-
Support Vector Machine
TD2FI
-
Triangle Geometry Using Dominant Distribution of
Foreground Image
TSGR
-
Triangle Feature Based Summation of Gradient and
Ratio
WEKA
-
Waikato Environment for Knowledge Analysis
xiii
LIST OF PUBLICATIONS
Azmi, M.S., Arbain, N.A., Muda, A.K., Abal Abas, Z., and Muslim, Z., 2015. Data
Normalization for Triangle Features by Adapting Triangle Nature for better Classification.
2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing
Technologies, pp.1–4.
Arbain, N.A., Azmi, M.S., Melhem, L.B., Muda, A.K., and Rashaideh, H., 2016.
Enhancement of Triangle Coordinates For Triangle Features For Better Classification.
Jordanian Journal of Computers and Information Technology (JJCIT), 2 (2), pp.108–119.
N. A. Arbain, M. S. Azmi, S. S. S. Ahmad, I. E. A. Jalil, M. Z. Masud, and M. A. Lateh,
2016, “Detection on Straight Line Problem in Triangle Geometry Features for Digit
Recognition,” in Multi-Conference on Artificial Intelligence Technology (M-CAIT 2016).
N. A. Arbain, M. S. Azmi, A. K. Muda, I. E. A. Jalil, and M. A. Aziz, 2016, “Current
Work on Digital Paleography,” Jurnal Teknologi. (Sciences Engineering).
N. A. Arbain, M. S. Azmi, S. Sakinah, S. Ahmad, A. K. Muda, I. E. A. Jalil, and T. K.
Ming, 2016, “Dynamic Similarity Distance with Mean Average Precision Tool,” in MultiConference on Artificial Intelligence Technology (M-CAIT 2016).
xiv
N. A. Arbain, M. S. Azmi, A. K. Muda, N. A. Muda, and R. Nordin, 2016, “An
Improvement on triangle Features for Digit Recognition through Combination of Features
by Adapting Triangle Geometry,” Jurnal Teknologi. (Sciences Engineering).
xv
CHAPTER 1
INTRODUCTION
1.1
Introduction
Digit recognition is an area which has been actively explored particularly to identify
manuscript origin details such as originality, writing date, provenance, as well as number
of authors (Chaudhuri, 2006; Bulacu et al., 2007; Pan et al., 2009). The studies in digit
recognition for ROMAN characters handwriting was explored over four decades ago
(Knoll, 1969). In meantime, no publicly was available for standard datasets that can be
used by the researchers. However, the researches and development in digit recognition was
gone on a swift expansion in the last decade. The Modified NIST dataset (MNIST) was a
largest dataset for ROMAN handwriting has been developed as a result of handwritten
digit classification competition which has been held in summer of 1992 (Borji et al., 2008).
The studies of digit recognition had evolved rapidly along with improvement made on
prior methods and techniques (Sundaresan and Lin, 1998; Liu et al., 2003, 2004; Kussul
and Baidyk, 2004; Labusch et al., 2008, 2010; Moradi et al., 2010; Mirsharif et al.,
2012).
In digit recognition, the image datasets used which include CEDAR, MNIST,
HODA, BANGLA and IFCHDB (Bhowmik et al., 2004;
Kussul and Baidyk,
2004; Verma et al., 2004; Alaei et al., 2009; Javidi and Sharifizadeh, 2012; M. S. Azmi,
2013). Since images are used, image processing is required to process the images, for
example to convert image into binary pixels. Image processing is one of the vital elements
1
that is widely used in research area within engineering and computer science disciplines.
Among rapidly growing technology today, various applications were developed in order to
conduct a process of pre-processing of the selected images such as remote sensing, medical
diagnosis, industrial applications, robotics, and so on and so forth. These applications are
used to assist researchers to handle the process of feature extraction in image processing.
Feature extraction is an important stage where the input data will be converted into
a reduced sets of feature when the input data to an algorithm is too big to be handled. In
other words, the input data are transformed into a reduced sets of feature vector (Huang
and Zhou, 2010). This process is a vital stage generating feature vectors. If extracted
features are chosen correctly, the feature sets will consist of important information from
input data. This enable to analyse following tasks by using the reduced sets rather than the
whole size of input. There are several techniques are frequently used to extract images
such as Image Processing Basics, Sobel Edge Detection, Canny Edge Detection and Hough
Transformation (Aichert, 2008).
Recently, the triangle geometry method has been used to extract images. M. S.
Azmi (2013) had proposed new features from triangle properties by using the triangle
geometry method to extract digit images. The triangle geometry method also has been
extensively applied in biometric research such as face recognition and fingerprint
recognition (Jin et al., 2009; Tin et al., 2009; Lai and Suandi, 2011; Gao and Xu, 2012).
Besides, the triangle geometry also had been adopted in intrusion, vehicle detection and
digit recognition (Mozaffari et al., 2007; Haselhoff and Kummert, 2009; Ebrahimpour et
al., 2010; Tang et al., 2010; M. S. Azmi, 2013).
In triangle geometry, three points of triangle are required in forming a triangle
shape. However, problem in determining three points of triangle had caused modelling
problems. The problem occurred when the value of gradient for the generated three points
2
of triangle are equivalent and causing the three points of triangle’s position to be inline.
The inline position for three points of triangle eventually leads to the formation of a
straight line.
Thus, this study aims to solve the problem in determining three triangle points. The
triangle points need to be identified to form a triangle shape. Selected triangle points are to
be used to assist in producing the proposed method for triangle formation solution in
solving straight line problem. The solution to straight line problem is proposed in order to
improve the triangle geometry shape through three triangle points. The validation of
proposed method is carried on in order to evaluate the performance of the proposed
method.
1.2
Research Background
Feature extraction is an important task in image processing because of the
meaningful features extracted are vital in representing an object. Object is modelled and
represented by geometric forms through geometrical properties. The triangle geometry
properties such as angles of corners, ratios of sides and gradients of corners can be used to
produce triangle features. Thus, triangle shape needs to be formed to acquire triangle
properties. A triangle can be formed based on three points that linked to each other. (Page,
2015).
The triangle geometry method has been widely used in recognition area such as
face and fingerprint recognition. In face recognition, triangle points are acquired based on
body elements such as nasal tip, eyes, nose and mouth (Tin et al., 2009; Gao and Xu,
2012; Zhang et al., 2012). For fingerprint recognition, triangle points are attained based on
minutiae (Ghazvini et al., 2011; Gago-Alonso et al., 2013; Yang et al., 2013; Kavati et
al., 2014; Jadied, 2015; Jain and Prasad, 2015).
3