2D Object Detection and Recognition

325 Pages·2005·7.21 MB·English

by  Yali Amit

2D Object Detection and Recognition
Models,Algorithms,and Networks
Yali Amit
amit-79020 book May20,2002 13:3
2D Object Detection and Recognition
i
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amit-79020 book May20,2002 13:3
YaliAmit
2D Object Detection and Recognition
Models,Algorithms,andNetworks
TheMITPress
Cambridge,Massachusetts
London,England
iii
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©2002MassachusettsInstituteofTechnology
Allrightsreserved.Nopartofthisbookmaybereproducedinanyformbyanyelectronicor
mechanicalmeans(includingphotocopying,recording,orinformationstorageandretrieval)
withoutpermissioninwritingfromthepublisher.
ThisbookwassetinTimesRomanbyInteractiveCompositionCorporationandwasprinted
andboundintheUnitedStatesofAmerica.
LibraryofCongressCataloging-in-PublicationData
Amit,Yali.
2Dobjectdetectionandrecognition:models,algorithms,andnetworks/YaliAmit.
p.cm.
Includesbibliographicalreferences.
ISBN0-262-01194-8(hc.:alk.paper)
1.Computervision. I.Title.
TA1634.A452002
006.3(cid:2)7–dc21
2002016508
iv
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ToGranite,Yotam,andInbal
v
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Contents
Preface xi
Acknowledgments xv
1 Introduction 1
1.1 Low-LevelImageAnalysisandBottom-upSegmentation 1
1.2 ObjectDetectionwithDeformable-TemplateModels 3
1.3 DetectionofRigidObjects 5
1.4 ObjectRecognition 8
1.5 SceneAnalysis:MergingDetectionandRecognition 10
1.6 NeuralNetworkArchitectures 12
2 DetectionandRecognition:OverviewofModels 13
2.1 ABayesianApproachtoDetection 13
2.2 OverviewofObject-DetectionModels 18
2.3 ObjectRecognition 25
2.4 SceneAnalysis:CombiningDetectionandRecognition 27
2.5 NetworkImplementations 28
3 1DModels:DeformableContours 31
3.1 Inside-OutsideModel 31
3.2 AnEdge-BasedDataModel 40
3.3 Computation 41
vii
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viii Contents
3.4 JointEstimationoftheCurveandtheParameters 48
3.5 BibliographicalNotesandDiscussion 51
4 1DModels:DeformableCurves 57
4.1 StatisticalModel 58
4.2 Computation:DynamicProgramming 63
4.3 GlobalOptimizationonaTree-StructuredPrior 67
4.4 BibliographicalNotesandDiscussion 78
5 2DModels:DeformableImages 81
5.1 StatisticalModel 83
5.2 ConnectiontotheDeformable-ContourModel 88
5.3 Computation 88
5.4 BernoulliDataModel 93
5.5 Linearization 97
5.6 ApplicationstoBrainMatching 101
5.7 BibliographicalNotesandDiscussion 104
6 SparseModels:Formulation,Training,andStatisticalProperties 109
6.1 FromDeformableModelstoSparseModels 111
6.2 StatisticalModel 113
6.3 LocalFeatures:ComparisonArrays 118
6.4 LocalFeatures:EdgeArrangements 121
6.5 LocalFeatureStatistics 128
7 DetectionofSparseModels:DynamicProgramming 139
7.1 ThePriorModel 139
7.2 Computation:DynamicProgramming 142
7.3 DetectingPose 147
7.4 BibliographicalNotesandDiscussion 148
8 DetectionofSparseModels:Counting 151
8.1 DetectingCandidateCenters 153
8.2 ComputingPoseandInstantiationParameters 156
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ix Contents
8.3 DensityofCandidateCentersandFalsePositives 159
8.4 FurtherAnalysisofaDetection 160
8.5 Examples 163
8.6 BibliographicalNotesandDiscussion 176
9 ObjectRecognition 181
9.1 ClassificationTrees 185
9.2 ObjectRecognitionwithTrees 192
9.3 RelationalArrangements 197
9.4 Experiments 201
9.5 WhyMultipleTreesWork 209
9.6 BibliographicalNotesandDiscussion 212
10 SceneAnalysis:MergingDetectionandRecognition 215
10.1 ClassificationofChessPiecesinGray-LevelImages 216
10.2 DetectingandClassifyingCharacters 224
10.3 ObjectClustering 228
10.4 BibliographicalNotesandDiscussion 231
11 NeuralNetworkImplementations 233
11.1 BasicNetworkArchitecture 234
11.2 HebbianLearning 237
11.3 LearninganObjectModel 238
11.4 LearningClassifiers 241
11.5 Detection 248
11.6 GatingandOff-CenterRecognition 250
11.7 BiologicalAnalogies 252
11.8 BibliographicalNotesandDiscussion 255
12 Software 259
12.1 SettingThingsUp 259
12.2 ImportantDataStructures 262
12.3 LocalFeatures 265
12.4 DeformableModels 267