Avances in Machine Learning and Data Analysis

241 Pages·4.366 MB·English

by  Ao S.-I., Rieger B.B., Amouzegar M. (eds.)

Advances in Machine Learning and Data Analysis
Lecture Notes inElectrical Engineering
Volume48
Forothertitlespublishedinthisseries,goto
http://www.springer.com/series/7818
Sio-Iong Ao (cid:129) Burghard B. Rieger
Mahyar Amouzegar
Editors
Advances in
Machine Learning
and Data Analysis
123
Editors
Sio-IongAo MahyarAmouzegar
HarvardSchoolofEngineering CollegeofEngineering
andAppliedSciences CaliforniaStateUniversity
HarvardUniversity LongBeach
Room403,60OxfordStreet
CambridgeMA02138,USA
[email protected]
BurghardB.Rieger
UniversitätTrier
FBIILinguistische
Datenverarbeitung
Computerlinguistik
Universitätsring15
54286Trier
Germany
[email protected]
ISSN1876-1100 e-ISSN1876-1119
ISBN978-90-481-3176-1 e-ISBN978-90-481-3177-8
DOI10.1007/978-90-481-3177-8
SpringerDordrechtHeidelbergLondonNewYork
LibraryofCongressControlNumber:2009930421
(cid:2)c SpringerScience+BusinessMediaB.V.2010
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Preface
A large international conference on Advances in Machine Learning and Data
Analysis was held in UC Berkeley, CA, USA, October 22–24, 2008, under the
auspices of the World Congress on Engineering and Computer Science (WCECS
2008). The WCECS is organized by the International Association of Engineers
(IAENG).IAENGisanon-profitinternationalassociationfortheengineersandthe
computerscientists,whichwasfoundedin1968andhasbeenundergoingrapidex-
pansionsinrecentyears.TheWCECSconferenceshaveservedasexcellentvenues
for the engineering community to meet with each other and to exchange ideas.
Moreover, WCECS continues to strike a balance between theoretical and appli-
cation development.The conferencecommittees have been formed with over two
hundredmemberswhoaremainlyresearchcenterheads,deans,departmentheads
(chairs),professors,andresearchscientistsfromoverthirtycountries.Theconfer-
enceparticipantsarealsotrulyinternationalwithahighlevelofrepresentationfrom
manycountries.Theresponsesforthecongresshavebeenexcellent.In2008,were-
ceivedmorethansixhundredmanuscripts,andafterathoroughpeerreviewprocess
56.71%ofthepaperswereaccepted.
This volume contains sixteen revised and extended research articles written
by prominent researchers participating in the conference. Topics covered include
Expertsystem,Intelligentdecisionmaking,Knowledge-basedsystems,Knowledge
extraction, Data analysis tools, Computational biology, Optimization algorithms,
Experimentdesigns,Complexsystemidentification,Computationalmodeling,and
industrialapplications.Thebookoffersthestateoftheartoftremendousadvancesin
machinelearninganddataanalysisandalsoservesasanexcellentreferencetextfor
researchersandgraduatestudents,workingonmachinelearninganddataanalysis.
HarvardUniversity,USA Sio-IongAo
UniversityofTrier,Germany BurghardB.Rieger
CaliforniaStateUniversity,LongBeach,USA MahyarAmouzegar
v
Contents
1 2D/3DImageDataAnalysisforObjectTrackingand
Classification.................................................................. 1
Seyed Eghbal Ghobadi, Omar Edmond Loepprich,
Oliver Lottner, Klaus Hartmann, Wolfgang Weihs,
andOtmarLoffeld
2 Robot Competence Development by Constructive
Learning....................................................................... 15
Q.Meng,M.H.Lee,andC.J.Hinde
3 UsingDigitalWatermarkingforSecuringNextGeneration
MediaBroadcasts............................................................. 27
DominikBirkandSea´nGaines
4 AReduced-DimensionProcessorModel................................... 43
AzamBeg
5 Hybrid Machine Learning Model for Continuous
MicroarrayTimeSeries...................................................... 57
Sio-IongAo
6 An Asymptotic Methodto a FinancialOptimization
Problem........................................................................ 79
DejunXie,DavidEdwards,andGibertoSchleiniger
7 AnalyticalDesignofRobust Multi-loopPIController
forMulti-timeDelayProcesses.............................................. 95
TruongNguyenLuanVuandMoonyongLee
8 AutomaticandSemi-automaticMethodsfortheDetection
ofQuasarsinSkySurveys...................................................109
Sio-IongAo
vii
viii Contents
9 ImprovingLow-CostSailSimulatorResultsbyArtificial
NeuralNetworksModels ....................................................139
V.D´ıazCasa´s,P.PorcaBel´ıo,F.Lo´pezPen˜a,andR.J.Duro
10 RoughSetApproachestoUnsupervisedNeuralNetwork
BasedPatternClassifier......................................................151
AshwinKothariandAvinashKeskar
11 A NewRobust CombinedMethodforAutoExposure
andAutoWhite-Balance.....................................................165
QuocKienVuong,Se-HwanYun,andSukiKim
12 A Mathematical Analysis Around Capacitive
Characteristicsof the Current of CSCT: Optimum
UtilizationofCapacitorsofHarmonicFilters.............................179
MohammadGolkhahandMohammadTavakoliBina
13 Harmonic Analysis and Optimum Allocation
ofFiltersinCSCT ............................................................191
MohammadGolkhahandMohammadTavakoliBina
14 Digital Pen and Paper Technology as a Means
ofClassroomAdministrationRelief........................................203
JanBroer,TimWendisch,andNinaWillms
15 AConceptualModelforaNetwork-BasedAssessment
SecuritySystem...............................................................217
NathanPercival,JenniferPercival,andClemensMartin
16 Incorrect Weighting of Absolute Performance
inSelf-Assessment............................................................231
ScottA.JeffreyandBrianCozzarin
Chapter 1
2D/3D Image Data Analysis for Object
Tracking and Classification
SeyedEghbalGhobadi,OmarEdmondLoepprich,OliverLottner,
KlausHartmann,WolfgangWeihs,andOtmarLoffeld
Abstract Object tracking and classification is of utmost importance for different
kindsofapplicationsincomputervision.Inthischapter,weanalyze2D/3Dimage
data to addresssolutionsto someaspects ofobjecttrackingandclassification. We
concludeourworkwitharealtimehandbasedrobotcontrolwithpromisingresults
inarealtimeapplication,evenunderchallengingvaryinglightingconditions.
Keywords 2D/3Dimagedata(cid:2)Registration(cid:2)Fusion(cid:2)Featureextraction(cid:2)Tracking
(cid:2)Classification(cid:2)Hand-basedrobotcontrol
1.1 Introduction
Object tracking and classification are the main tasks in different kinds of appli-
cations such as safety, surveillance, man–machine interaction, driving assistance
systemandtrafficmonitoring.Ineachoftheseapplications,theaimistodetectand
findthepositionofthedesiredobjectateachpointintime.Whileinthesafetyap-
plication, the personnel as the desired objects should be tracked in the hazardous
environmentstokeepthemsafefromthemachinery,inthesurveillanceapplication
theyaretrackedtoanalyzetheirmotionbehaviorforconformitytoadesirednorm
forsocialcontrolandsecurity.Man-Machine-Interaction,ontheotherhandhasbe-
comeanimportanttopicfortheroboticcommunity.Apowerfulintuitiveinteraction
betweenmanandmachinerequirestherobottodetectthepresenceoftheuserand
interprethisgesturemotion.Adrivingassistancesystemdetectsandtrackstheob-
stacles,vehiclesandpedestriansinordertoavoidanycollisioninthemovingpath.
Thegoaloftrafficmonitoringinanintelligenttransportationsystemistoimprove
theefficiencyandreliabilityofthetransportsystemtomakeitsafeandconvenient
S.E.Ghobadi((cid:2)),O.E.Loepprich,O.Lottner,K.Hartmann,W.Weihs,andO.Loffeld
CenterforSensorSystems(ZESS),UniversityofSiegen,Paul-Bonatz-Str.9-11,
D57068Siegen,Germany
e-mail:[email protected];[email protected];[email protected];
[email protected];[email protected];[email protected]
S.-I.Aoetal.(eds.),AdvancesinMachineLearningandDataAnalysis, 1
LectureNotesinElectricalEngineering48,DOI10.1007/978-90-481-3177-8 1,
(cid:2)c SpringerScience+BusinessMediaB.V.2010
2 S.E.Ghobadietal.
for the people. There are still so many significant applications in our daily life in
whichobjecttrackingandclassificationplaysanimportantrole.Nowadays,the3D
visionsystemsbasedonTimeofFlight(TOF)whichdeliverrangeinformationhave
the main advantage to observe the objects three-dimensionallyand therefore they
havebecomeveryattractivetobeusedintheaforementionedapplications.However,
the current TOF sensors have low lateral resolution which makes them inefficient
foraccurateprocessingtasksintherealworldproblems.Inthiswork,wefirstpro-
poseasolutiontothisproblembyintroducingournovelmonocular2D/3Dcamera
system and then we will study some aspects of object tracking and classification
using2D/3Dimagedata.
1.2 2D/3DVisionSystem
AlthoughthecurrentopticalTOFsensors [13–16]canprovideintensityimagesin
additiontotherangedata,theysufferfromalowlateralresolution.Thisdrawback
can be obviated by combininga TOF camera with a conventionalone. This com-
binationisatendencyintherecentresearchworksbecauseevenwithregardtothe
emerging new generation of TOF sensors with high resolution,1 an additional2D
sensorstillresultsinahigherresolutionandprovidesadditionalcolorinformation.
With regardto the measurementrange,however,the problemof parallaxdoesnot
allowtosimplypositiontwocamerasnexttoeachotherandoverlaythegenerated
images.
The multimodal data acquisition device used in this work is a recently devel-
oped monocular 2D/3D imaging system, named MultiCam. This camera, which
is depicted in Fig.1.1, consists of two imaging sensors: A conventional 10-bit
CMOSgrayscalesensorwithVGAresolutionandaPhotonicMixerDevice(PMD)
with a resolution of 64(cid:3)48 pixels. The PMD is an implementationof an optical
Time of Flight (TOF) sensor, able to deliver range data at quite high frame rates.
VIS -NIR beam splitter
F-M(oRuenatr C Aapmeretruar eL)ens VISA-NR@I RC t7Bd6.e 0faonmrm Ns,pIRlitter3D PMD Sensor forC-M(oRuenatr CAapmereturare L)ens AR@ C t7d6. 0fonrm N,IR3D PMD Sensor for
Camera Body Lens Infrared Lighting modulated NIR modulated NIR
glass
Sensor Window glass Sensor Window
AR Ctd. for NIR AR Ctd. for NIR
NIR Edge Filter NIR Edge Filter
@ 1000nm @ 1000nm
2D CMOS Sensor IR Cut @ 730nm 2D CMOS Sensor IR Cut @ 730nm
for VIS (optional) for VIS (optional)
Fig. 1.1 Left: 2D/3D vision system (MultiCam) developed at ZESS. Middle: F-mount optical
setup.Right:C-mountopticalsetup
1ForexamplePMD-40K (200(cid:3)200pixels), Swissranger 4000(176(cid:3)144pixels) andZCam-
prototype(320(cid:3)480pixels).