Towards Efficient Texture Classification and Abnormality Detection

198 Pages·2004·2.7 MB·English

Towards Efficient Texture Classification
and Abnormality Detection
Amirhassan Monadjemi
Adissertationsubmittedtothe Universityof Bristolinaccordance withtherequirementsfor
the degree of Doctorof Philosophyinthe Facultyof Engineering,Departmentof Computer
Science.
October 2004
Abstract
One of the fundamental issues in image processing and machine vision is texture, specifically
texture feature extraction, classification and abnormality detection. This thesis is concerned
with the analysis and classification of natural and random textures, where the building ele-
mentsandthestructureoftexturearenotclearlydeterminable,hencestatisticalandsignalpro-
cessing approaches are more appropriate. We investigate the advantages of multi-scale/multi-
directionalsignalprocessingmethods,higherorderstatistics-basedschemes,andcomputation-
allylowcosttextureanalysisalgorithms. Consequentlytheseadvantagesarecombinedtoform
novelalgorithms.
We developamulti-scale/multi-directionalWalsh-Hadamardtransformforfastandrobusttex-
ture feature extraction, where scale and angular decomposition properties are integrated into
an ordinaryWalsh-Hadamard transform,toincrease itstexture classificationperformance. We
also introduce a highlyaccurate Gabor Compositionmethodfor texture abnormalitydetection
which is a combination of a signal processing and a statistical method, namely Gabor filters
and co-occurrence matrices. Furthermore, to overcome the practical drawbacks of traditional
classificationapproaches,thatrequireanextensivetrainingstage,weintroduceamethodbased
onrestructuredeigenfiltersfor textureabnormalitydetectionwithina noveltydetectionframe-
work. Thisdemandsonlya minimaltrainingstageusinga fewnormalsamples.
The proposed schemes are compared with commonly used texture classification methods on
differentimagesets,includingahighresolutionoutdoorscenedatabase,samplesoftheVisTex
colour texture suite, and randomly textured normal and abnormal tiles. The results are then
analysedinordertoevaluatetextureclassificationperformance,baseduponaccuracy,general-
ityandcomputationalcosts.
Declaration
I declare that the work in this dissertation was carried out in accordance with the Regulations
of the University of Bristol. The work is original except where indicated by special reference
inthe textandnopartof the dissertationhasbeensubmittedfor anyotherdegree.
Any views expressed in the dissertation are those of the author and in no way represent those
of the Universityof Bristol.
The dissertation has not been presented to any other University for examination either in the
UnitedKingdomor overseas.
SIGNED: DATE:
Acknowledgements
Firstofall,Iwouldliketothankmysupervisors,ProfessorBarryT.ThomasandDr. MajidMirmehdifor
theirunlimitedsupport,clearguidance,novelideasandbrilliantdiscussionssinceIstartedmyeducation
hereinBristol. Idoappreciatetheireffortsandconcerns,thathaveimprovedmyacademicabilitiesover
thelastfouryears.
I am also grateful to the University of Bristol and the department of computer science, the head of
department Professor DavidMayandalldepartmental staff for their help. I aminparticular grateful to
Stuartforproofreading,thedepartment’scomputeradministratorsRobandOliverfortheirsupport,and
myfriendsAngus,Janko,Phil,James,JasonandMikefortheir timeandprecious suggestions.
TheIranianMinistryofScience,ResearchandTechnology(IMSRT)mustalsobeacknowledgedforthe
scholarship thatIwasawarded,andtheIranianstudents advisory inLondonfortheir assistance.
I owe special thanks to Bristol and Bristolians, such a lovely and lively city, with kind, polite and
generous people. I have had an enjoyable time here, with Iranian, British, and many international
friends.
WhileIwasstudyinghere,mygrandfather, grandmotherandfatherpassedawayonebyone. Iwastheir
eldest grandson and only son. Sometimes it makes me so sad that I wasnot there while they were in a
critical condition and wonder if my presence could have changed anything or not. However, I believe
thattheyaremorehappythatIdealtwithmyfurther education. MayGodblessthem.
Finally, IdonotknowhowIcan thank myfamily,myrespectable mother towhomIowemanythings,
my small son, Rastin, to whom I owe a seashore trip and a lot of weekend activities, and my dearest
wife,Atefeh,whoseinfinitesupport,incredible understanding andultimatekindness areimmeasurable.
I am certain that I could not have finished this study if she were not as remarkable as she is. When we
arrivedhere,westartedanewlife,alone,inacompletelynewenvironment, andcouldonlyrelyoneach
other inournuclear family. Sheistotallyperfect,inparticular aperfectpartner.
List of Publications
A. Monadjemi, M. Mirmehdi and B. T. Thomas. Restructured Eigenfilter Matching for
(cid:15) Novelty Detection in Random Textures. In: Proceedings of the 15th British Machine
VisionConference,BMVC 2004,pages637 646,September 2004.
(cid:0)
A. Monadjemi,M. Mirmehdiand B. T.Thomas. Fast texture classificationin highreso-
(cid:15) lutioncolourtextures. In: Proceedingsofthe2ndIranianConferenceonMachineVision,
Image ProcessingandApplications,pages94 101,February 2003.
(cid:0)
A. Monadjemi, B. T. Thomas and M. Mirmehdi. Classification in High Resolution Im-
(cid:15) ages with Multiple Classifiers. In: IASTED Visualization, Imaging and Image Process-
ing,VIIP2002Conference, J.J.Villanueva,editor,pages417 421,September 2002.
(cid:0)
A. Monadjemi, B. T. Thomas and M. Mirmehdi. Speed v. Accuracy for High Resolu-
(cid:15) tion Colour Texture Classification. In: Proceedings of the 13th British Machine Vision
Conference,BMVC2002,P.L.RosinandD.Marshall,editors,pages143 152,BMVA
Press,September 2002. (cid:0)
A. Monadjemi, B. T. Thomas and M. Mirmehdi. Experiments on High Resolution Im-
(cid:15) agesTowardsOutdoorSceneClassification. In: ProceedingsoftheSeventhComputerVi-
sionWinterWorkshop,HorstWildenauerandWalterKropatsch,editors,pages325 334,
Vienna Universityof Technology,February 2002. (cid:0)
To my dearest wife,
late father, and precious mother
Contents
ListofFigures vii
ListofTables xiii
1 Introduction 1
1.1 BackgroundandMotivation . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.4 ThesisLayout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2 Texture AnalysisandClassification: Background and Methods 10
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2 Texture: Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
i
2.3 TextureAnalysisandClassification: DifferentApproaches . . . . . . . . . . . 13
2.3.1 StatisticalApproaches . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3.2 SignalProcessingApproaches . . . . . . . . . . . . . . . . . . . . . . 19
2.4 TextonsinRandomTextures: ADifferentApproach . . . . . . . . . . . . . . 29
2.5 Colour Textureanalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.6 TextureInspectionandAbnormalityDetection . . . . . . . . . . . . . . . . . . 31
2.6.1 HowtoDetecta TexturalAbnormality? . . . . . . . . . . . . . . . . . 31
2.6.2 Ceramic TilesInspection . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.6.3 PreviousStudiesonSurface InspectionandTileDefect Detection . . . 36
2.7 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
2.7.1 PCA, KLTandEigen-basedDecomposition . . . . . . . . . . . . . . 42
2.7.2 Classifiers: ArtificialNeuralNetworks . . . . . . . . . . . . . . . . . 43
2.7.3 Classifiers: K-NearestNeighbourhoodClassifier . . . . . . . . . . . . 45
2.7.4 NoveltyDetection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3 Texture AnalysisofHighDefinition OutdoorScene Images 49
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
ii
3.2 HighResolutionOutdoorScene Data Set . . . . . . . . . . . . . . . . . . . . 51
3.3 TexturalFeature Extractors . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.3.1 Gabor Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.3.2 The NewApproach: DirectionalWalsh-HadamardTransform . . . . . 57
3.3.3 Justificationof theDWHT . . . . . . . . . . . . . . . . . . . . . . . . 66
3.4 Colour Feature Extractors . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
3.4.1 NewChromaticFeatures: H and S . . . . . . . . . . . . . . . . . . 69
p p
3.5 ClassificationTests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
3.5.1 ClassificationusingTexturalFeatures: GaborandDWHT . . . . . . . 74
3.5.2 ClassificationusingChromaticFeatures . . . . . . . . . . . . . . . . . 76
3.5.3 ClassificationUsingMergedTextureandColourFeatures . . . . . . . 77
3.6 Summaryof ComputationalCosts . . . . . . . . . . . . . . . . . . . . . . . . 78
3.7 ExperimentswithVisTex . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
3.7.1 Texture-basedClassification . . . . . . . . . . . . . . . . . . . . . . 81
3.7.2 Colour-basedClassificationonVisTex . . . . . . . . . . . . . . . . . 85
3.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
iii
4 Defect Detection inTextured Tiles 88
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
4.2 ClassificationTestsFramework . . . . . . . . . . . . . . . . . . . . . . . . . . 89
4.2.1 Data Set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
4.2.2 Classifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
4.3 ClassificationExperiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
4.3.1 OrdinaryHistograms . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
4.3.2 LocalBinaryPatterns(LBP) . . . . . . . . . . . . . . . . . . . . . . . 95
4.3.3 Co-occurrence Matrices . . . . . . . . . . . . . . . . . . . . . . . . . 95
4.3.4 Gabor Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
4.3.5 DirectionalWalsh-HadamardTransform . . . . . . . . . . . . . . . . . 103
4.3.6 Discrete Cosine Transform . . . . . . . . . . . . . . . . . . . . . . . . 106
4.3.7 Eigenfiltering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
4.4 GaborCompositionMethod . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
4.4.1 The Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
4.4.2 Justification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
4.4.3 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
iv