A Practical Introduction to Computer Vision with OpenCV

235 Pages·2014·12.95 MB·English

Kenneth Dawson-Howe
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A Practical Introduction to
ComPuter VIsIon
wItH oPenCV

A PRACTICAL
INTRODUCTION TO
COMPUTER VISION
WITH OPENCV

A PRACTICAL
INTRODUCTION TO
COMPUTER VISION
WITH OPENCV
KennethDawson-Howe
TrinityCollegeDublin,Ireland
Thiseditionfirstpublished2014
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1 2014
Iamgrateful to many peoplefortheirhelp andsupport during the writing
ofthisbook. Thebiggest thanksmust goto mywife Jane,mychildren,
WilliamandSusie,andmyparents, allofwhoseencouragement
hasbeenunstinting.
Imustexpressmythankstomystudentsfortheirinterestandenthusiasm
inthis subject.Itisalwaysrefreshing tohearstudents discussinghowto
solvevision problems intutorials andgreatto heartheirsolutions to
problems which areoftendifferent (andsometimes better)than my own.
Ithankmy colleagues(in particularArthur Hughes,Jeremy Jonesand
Hilary McDonald)fortheirencouragement andsupport.

Contents
Preface xiii
1 Introduction 1
1.1 ADifficultProblem 1
1.2 TheHumanVisionSystem 2
1.3 PracticalApplicationsofComputerVision 3
1.4 TheFutureofComputerVision 5
1.5 MaterialinThisTextbook 6
1.6 GoingFurtherwithComputerVision 7
2 Images 9
2.1 Cameras 9
2.1.1 TheSimplePinholeCameraModel 9
2.2 Images 10
2.2.1 Sampling 11
2.2.2 Quantisation 11
2.3 ColourImages 13
2.3.1 Red–Green–Blue(RGB)Images 14
2.3.2 Cyan–Magenta–Yellow(CMY)Images 17
2.3.3 YUVImages 17
2.3.4 HueLuminanceSaturation(HLS)Images 18
2.3.5 OtherColourSpaces 20
2.3.6 SomeColourApplications 20
2.4 Noise 22
2.4.1 TypesofNoise 23
2.4.2 NoiseModels 25
2.4.3 NoiseGeneration 26
2.4.4 NoiseEvaluation 26
2.5 Smoothing 27
2.5.1 ImageAveraging 27
2.5.2 LocalAveragingandGaussianSmoothing 28
2.5.3 RotatingMask 30
2.5.4 MedianFilter 31
viii Contents
3 Histograms 35
3.1 1DHistograms 35
3.1.1 HistogramSmoothing 36
3.1.2 ColourHistograms 37
3.2 3DHistograms 39
3.3 Histogram/ImageEqualisation 40
3.4 HistogramComparison 41
3.5 Back-projection 43
3.6 k-meansClustering 44
4 BinaryVision 49
4.1 Thresholding 49
4.1.1 ThresholdingProblems 50
4.2 ThresholdDetectionMethods 51
4.2.1 BimodalHistogramAnalysis 52
4.2.2 OptimalThresholding 52
4.2.3 OtsuThresholding 54
4.3 VariationsonThresholding 56
4.3.1 AdaptiveThresholding 56
4.3.2 BandThresholding 57
4.3.3 Semi-thresholding 58
4.3.4 MultispectralThresholding 58
4.4 MathematicalMorphology 59
4.4.1 Dilation 60
4.4.2 Erosion 62
4.4.3 OpeningandClosing 63
4.4.4 Grey-scaleandColourMorphology 65
4.5 Connectivity 66
4.5.1 Connectedness:ParadoxesandSolutions 66
4.5.2 ConnectedComponentsAnalysis 67
5 GeometricTransformations 71
5.1 ProblemSpecificationandAlgorithm 71
5.2 AffineTransformations 73
5.2.1 KnownAffineTransformations 74
5.2.2 UnknownAffineTransformations 75
5.3 PerspectiveTransformations 76
5.4 SpecificationofMoreComplexTransformations 78
5.5 Interpolation 78
5.5.1 NearestNeighbourInterpolation 79
5.5.2 BilinearInterpolation 79
5.5.3 Bi-CubicInterpolation 80
5.6 ModellingandRemovingDistortionfromCameras 80
5.6.1 CameraDistortions 81
5.6.2 CameraCalibrationandRemovingDistortion 82