Multimedia Techniques for Device and Ambient Intelligence

209 Pages·2009·7.82 MB·English

by  Kangjun Lee

Ernesto Damiani
Jechang Jeong
Editors
Multimedia
Techniques
for Device
and Ambient
Intelligence
123
Multimedia Techniques for
Device and Ambient Intelligence
.
Ernesto Damiani Jechang Jeong
Editors
Multimedia Techniques for
Device and Ambient

Intelligence
Editors
Ernesto Damiani Jechang Jeong
Dipartimento di. Tecnologie Department of Electronics
dell’Informazione & Computer Engineering
Università degli studi di Milano Hanyang University
via Bramante 65 17 Haengdang-dong
26013 Crema Seoul, Seongdong-Gu
Italy Korea
d [email protected] j [email protected]

ISBN978-0-387-88776-0 e-ISBN978-0-387-88777-7
DOI10.1007/978-0-387-88777-7
Springer Dordrecht Heidelberg London New York
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Preface
AmbientIntelligenceusedtobeavisionofourfuture.Today,itismorelikeacen-
tralaspectofourlife:wealreadylivesurroundedbyanewgenerationofelectronic
devices,helpingusinmostofourworkandleisureendeavors.AmbientIntelligence
technologiescombineconceptsofubiquitouscomputingandintelligentsystemsto
puthumansatthecenteroftechnologicaldevelopment.Manyyoungpeople,inpar-
ticular,arefondofambientintelligenceandseemtohavefoundaneasyandnatural
waytoexploittheinformationandknowledgeprovidedbythenetworkconnecting
thesedevicesandbythedevicethemselves.Insuchascenario,givingtothedevices
withthecapabilityofextractingandprocessingmultimediainformationiscrucial.
Indeed, time-honored research areas such as video processing and image analysis
arenowexperiencingasecondyouth,andplayacrucialroleinsupportingthede-
vices’advancedmultimediacapabilities.
MultimediaTechniquesforDeviceandAmbientIntelligence(MTDAI)isaedited
volumewrittenbywell-recognizedinternationalresearchersincluding,butnotlim-
itedto,extendedchapter-styleversionsofthepaperspresentedatthehomonymous
MTDAI seminar that we started in 2008, in the unique setting of villa Braida at
MoglianoVeneto,nearVenice.TheMTDAIseminarisintendedtobringtogether,
without the usual formalities of a conference, a number of top researchers from
academia and industry interested in multimedia issues. MTDAI is based on short
presentationsandopendiscussions,fosteringinterdisciplinarycollaborationanden-
couragingtheexplorationofnewfrontiersintheareaofambientanddeviceintelli-
gence.
Aftertheseminar,someMTDAIauthorswereaskedtoreviseandextendtheircon-
tributions, taking into account the lively discussion and remarks made during the
seminar.Also,acallforchapterwaspublished,attractingsomeinterestingpropos-
als of additional chapters. A rigorous refereeing was then carried out; the result is
thisbook,presentingthestate-of-the-artandsomerecentresearchresultsinthefield
ofimageunderstandinganditsapplicationstodeviceandambientintelligence.The
bookisdividedintotwoparts:thefirstpartdiscussesnewlow-leveltechniquesfor
imageandvideounderstanding,whilethesecondpartpresentsaseriesofnovelap-
plications,focusingonmultimedia-orientedknowledgemanagement.
vi Preface
Puttingtogetherabooklikethisisalwaysateameffort,andwegratefullyacknowl-
edge the hard work and dedication of many people. First of all, e appreciate the
fundamentalworkoftheMTDAIcommitteemembers,whoacceptedtohandlethe
refereeing of the book chapters, and contributed with valuable comments and ob-
servation.Wealsowouldliketoacknowledgethehelp,supportandpatienceofthe
Springerpublishingteam.Butevenmoreimportantly,wewishtothanktheauthors
whohavecontributedtheirbestresearchworktothisvolume.Webelievethatwhile
fully attaining the rigorousness and originality one would expect from a scientific
editedvolume,theircontributionsretainmuchofthelivelinessandappealtonon-
specialistswhichareamajorfeatureofourMTDAIseminar.
Milan,Seoul ErnestoDamiani
February2009 JechangJeong
Contents
PartI LowLevelApproachforImageandVideoUnderstanding
1 GOPStructureConversioninTranscodingMPEG-2toH.264/AVC . 3
KangjunLee,GwanggilJeonandJechangJeong
1.1 Introduction.............................................. 3
1.2 GOPStructureConversion.................................. 5
1.2.1 MVScalingintheTemporalDirection................ 5
1.2.2 CorrelationBetweentheCurrentMBModeandthe
ReferenceRegion ................................. 6
1.3 ProposedAlgorithms ...................................... 8
1.3.1 AdaptiveSearchRangeSelectionthroughtheMV
LinearityTestintheTemporalDirection .............. 8
1.3.2 AdaptiveModeDecisionMethodBasedonRegion
Information ...................................... 9
1.4 SimulationResults ........................................ 13
1.5 Conclusion............................................... 16
References..................................................... 16
2 SimpleLowLevelFeaturesforImageAnalysis................... 17
PaoloFalcoz
2.1 Introduction.............................................. 17
2.2 TheRoleofColor......................................... 19
2.2.1 ColorSpaces ..................................... 20
2.2.2 HSLandHSV .................................... 23
2.2.3 CIE-Lab ......................................... 25
2.2.4 ColorFlattening................................... 26
2.3 BlobDetection ........................................... 27
2.4 EdgeDetection ........................................... 30
2.5 SimpleShapes............................................ 33
2.5.1 ScaleandPositionInvariants:ProcrustesAnalysis ...... 33
2.5.2 ShapeAlignment:IterativeClosestPoint.............. 34
viii Contents
2.5.3 ShapeEncodingandMatching:CurvatureSpaceScale .. 35
2.6 Combinationofsimplefeatures ............................. 37
2.7 Conclusions.............................................. 38
References..................................................... 39
3 FastandrobustFaceDetection ................................ 43
MarcoAnisetti
3.1 Relatedwork............................................. 43
3.1.1 Feature-based..................................... 44
3.1.1.1 Lowlevel ............................... 44
3.1.1.2 Skin-map ............................... 45
3.1.1.3 Featureanalysis.......................... 48
3.1.1.4 Templatebased .......................... 49
3.1.2 Appearance-based................................. 50
3.2 Introduction.............................................. 52
3.3 Facedetectiononvideostream .............................. 52
3.3.1 Efficientobjectdetection ........................... 53
3.3.2 Appearance-basedfacedetection..................... 56
3.3.3 Features-basedfacedetection ....................... 58
3.3.3.1 AdaptiveSkindetection ................... 59
3.3.3.2 Eyesdetectionandvalidation .............. 61
3.3.3.3 Facenormalization ....................... 64
3.4 Experimentalresults....................................... 64
3.4.1 DiscussionandConclusions......................... 68
References..................................................... 69
4 Automatic3DFacialFittingforTrackinginVideoSequence ....... 73
ValerioBellandi
4.1 The3DFaceModel ....................................... 73
4.2 3Dmorphingbasis ........................................ 75
4.2.1 3Dmorphingbasisforshapeandexpression........... 77
4.2.1.1 ShapeUnit .............................. 77
4.2.1.2 ExpressionUnit.......................... 79
4.3 Appearancebasis ......................................... 81
4.3.0.3 PCA ................................... 81
4.3.0.4 Image-basedPCA ........................ 82
4.4 3DIlluminationbasis...................................... 87
4.5 TheGeneralPurposes3DTrackingAlgorithm ................. 90
4.5.1 FeatureLocation .................................. 92
4.6 Modeladaptation ......................................... 94
4.6.1 Feature-basedposeestimation....................... 95
4.6.2 Shapeandexpressioninference...................... 98
4.6.3 3DTracking-basedModelrefinement.................101
4.6.4 Initialrefinement..................................101
4.6.5 Deeprefinement ..................................103
4.7 Experimentalresults.......................................105
Contents ix
References.....................................................110
PartII MultimediaKnowledge-BasedApproachesandApplications
5 Input Devices and Interaction Techniques for VR-Enhanced
Medicine................................................... 115
LuigiGalloandGiuseppeDePietro
5.1 Introduction..............................................116
5.2 RelatedWorks............................................117
5.3 RequirementsAnalysis.....................................119
5.4 InteractionMetaphorsandTechniques........................121
5.4.1 RealisticMetaphors................................122
5.4.1.1 ARealisticMetaphor:VirtualHand..........122
5.4.2 MagicMetaphors..................................122
5.4.2.1 AMagicMetaphor:VirtualPointer. .........123
5.4.3 Pros and Cons of Realistic vs. Magic Interaction
Metaphors .......................................123
5.5 TheProposedInputDevice:theWiimote .....................124
5.5.1 Communication...................................125
5.5.2 Inputs ...........................................125
5.5.3 Outputs..........................................125
5.5.4 Classification .....................................125
5.6 TheProposedInteractionTechniques.........................126
5.6.1 TheManipulationState. ............................126
5.6.1.1 Pointing.................................127
5.6.1.2 TranslationandZooming. .................127
5.6.1.3 Rotation.................................128
5.6.2 TheCroppingState. ...............................130
5.7 Discussion ...............................................132
References.....................................................132
6 BridgingSensingandDecisionMakinginAmbientIntelligence
Environments .............................................. 135
ElieRaad,BecharaAlBounaandRichardChbeir
6.1 Introduction..............................................136
6.2 RelatedWorks............................................137
6.3 Preliminaries .............................................139
6.4 Templates................................................141
6.5 UncertaintyResolverviaAggregationFunctions ...............142
6.5.1 Average-basedFunction ............................143
6.5.2 BayesianNetwork-BasedFunction...................143
6.5.3 ”DempsterandShafer”-BasedFunction...............146
6.5.4 DecisionTree-BasedFunction.......................148
6.6 Experimentation ..........................................150
6.6.1 AggregationFunctionAccuracyandTimeProcessing ...151
6.6.2 ValueDistribution .................................153
x Contents
6.6.2.1 Test1:Valueshigherthan0.5 ..............153
6.6.2.2 Test2:Valueslessthan0.5 ................154
6.6.2.3 Test3:RandomValues....................154
6.6.2.4 Test5:75%ofthevaluesarelessthan0.5....156
6.6.2.5 Test6:Equallydistributedvalues ...........157
6.6.2.6 Test7:Distributionchange ................158
6.6.2.7 Test8:Influenceofthenumberofreturned
values0and1ontheaggregatedresult ......158
6.6.2.8 Discussion ..............................159
6.6.3 TemplateTuning ..................................160
6.6.3.1 Case1:usingthemultimediafunction f .....161
1
6.6.3.2 Case2:usingthemultimediafunction f .....162
2
6.6.3.3 Uncertaintythresholdtuning ...............162
6.7 Conclusion...............................................163
References.....................................................163
7 Ambient Intelligence in Multimedia and Virtual Reality
Environmentsfortherehabilitation ............................ 165
AttilaBenkoandSikLanyiCecilia
7.1 Introduction..............................................166
7.2 UsingAIbyspecialneedsusers .............................167
7.2.1 VisualImpairmentandPartiallySightedPeople ........167
7.2.2 DeafandHard-of-HearingPeople....................168
7.2.3 PhysicallyDisabledPersons.........................168
7.2.4 MentallyDisabledPeople...........................169
7.2.5 SmartHome......................................169
7.3 AdetailedexampleofusingAIinvirtualrealityforrehabilitation.170
7.4 Futurevision .............................................174
7.5 Conclusion...............................................175
7.6 Acknowledgement ........................................175
References.....................................................175
8 ArtificialNeuralNetworksforProcessingGraphswithApplication
toImageUnderstanding:ASurvey............................. 179
MonicaBianchiniandFrancoScarselli
8.1 FromflattostructuralPatternRecognition ....................179
8.2 Graphprocessingbyneuralnetworks.........................183
8.2.1 Notation .........................................183
8.2.2 Ageneralframeworkforgraphprocessing.............184
8.2.3 RecursiveNeuralNetworks .........................186
8.2.4 GraphNeuralNetworks ............................187
8.2.5 Othermodels .....................................188
8.3 Graph–basedrepresentationofimages........................189
8.3.1 Imagesegmentation ...............................189
8.3.2 RegionAdjacencyGraphs ..........................191
8.3.3 Multi–resolutiontrees..............................194