Augmented Vision Perception in Infrared: Algorithms and Applied Systems

469 Pages·2009·70.42 MB·English

by  Nguyen Trung Thà nh

Advances in Pattern Recognition
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Dr. Riad I. Hammoud
(Ed.)
Augmented Vision Perception
in Infrared
Algorithms and Applied Systems
ABC
Editor
Dr.RiadI.Hammoud
DelphiElectronics&Safety
Kokomo,Indiana,USA
[email protected]
http://sites.google.com/site/riadhammoud/
Serieseditor
ProfessorSameerSingh,PhD
ResearchSchoolofInformatics
LoughboroughUniversity
Loughborough,UK
ISBN978-1-84800-276-0 e-ISBN978-1-84800-277-7
DOI10.1007/978-1-84800-277-7
AdvancesinPatternRecognitionSeriesISSN1617-7916
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Preface
Throughoutmuchofmachinevision’searlyyearstheinfraredimageryhassuffered
fromreturnoninvestmentdespiteitsadvantagesovervisualcounterparts.Recently,
thefiscalmomentumhasswitchedinfavorofbothmanufacturersandpractitioners
ofinfraredtechnologyasaresultoftoday’srisingsecurityandsafetychallengesand
advancesinthermographicsensorsandtheircontinuousdropincosts.Thisyielded
agreatimpetusinachievingeverbetterperformanceinremotesurveillance,object
recognition,guidance,noncontactmedicalmeasurements,andmore.Thepurposeof
thisbookistodrawattentiontorecentsuccessfuleffortsmadeonmergingcomputer
visionapplications(nonmilitaryonly)andnonvisualimagery,aswellastofillinthe
needintheliteratureforanup-to-dateconvenientreferenceonmachinevisionand
infraredtechnologies.
Augmented Perception in Infrared provides a comprehensive review of recent
deployment of infrared sensors in modern applications of computer vision, along
within-depthdescriptionoftheworld’sbestmachinevisionalgorithmsandintelli-
gentanalytics.Itstopicsencompassmanydisciplinesofmachinevision,including
remote sensing, automatic target detection and recognition, background modeling
andimagesegmentation,objecttracking,faceandfacialexpressionrecognition,in-
variant shape characterization, disparate sensors fusion, noncontact physiological
measurements,nightvision,andtargetclassification.Itsapplicationscopeincludes
homelandsecurity,publictransportation,surveillance,medical,andmilitary.More-
over,thisbookemphasizesthemergingoftheaforementionedmachineperception
applications and nonvisual imaging in intensified, near infrared, thermal infrared,
laser,polarimetric,andhyperspectralbands.
Thisbookcontainseighteenchaptersorganizedintosevendistinctiveparts.PartI
presents advanced techniques for identifying unique infrared signatures and clas-
sifying small-resolution objects above and under the soil. Chapter 1 addresses the
challengeofmetallicandnonmetalliclandminedetectionusinginfraredthermogra-
phyinsteadofpopularmetaldetectors.Itdescribesanovelapproachforclassifying
shallowly buried objects in terms of geometric and thermal signatures. Chapters 2
vii
viii Preface
and 3 address the classification problem of vehicles including M35 trucks vs.T72
tanks,usinguncooled-infraredandpassivepolarimetricsensorswithinadistanceup
to12km.
Part II focuses on describing successful noncontact thermal video analysis
methodologies to compute vital sign measurements on the human face, neck, and
breast. Chapter 4 presents a wavelet-based signal analysis approach for accurate
measurement of arterial pulse based on the thermal infrared imaging of arterial
pulse propagation along the superficial arteries of the human body. Chapter 5 de-
scribes a coalitional tracking algorithm used in free-contact deception, lie, and
breathing-during-sleepmonitoringsystems.Thelastchapterofthispart(Chapter6)
provides a survey of recent research on thermal infrared imaging in early breast
cancerdetection.
PartIIIhighlightstherecentadvancesinsensortechnologydevelopmentthrough
combining conventional imaging and spectroscopy (hyperspectral imaging) and
theirdeploymentinmedicaldiagnostics,remotesensing,andmilitarytargetrecog-
nition. Two examples of hyperspectral bands are explored in acquiring critical
spatialinformationfromanobject:visiblefluorescenceandnear-infraredspectrum.
Chapter7presentsanoninvasiveskintumordetectiontechniquebasedontheanal-
ysisofspectralsignaturesmeasuredfromhyperspectralvisiblefluorescenceimag-
ing at a fixed ten-nm spectra resolution. The last chapter of this part (Chapter 8)
proposes a methodology to automatically select relevant spectral bands (spectral
screening)alongwithanovelclassoftargetdetectionmethods.
Part IV highlights the use of intensified and thermal sensors for facial recogni-
tion. Chapter 9 tackles the problem of face recognition in low-light environment
by matching probe and gallery recorded with disparate I2 and thermal sensors.
Chapter10 proposes a novel technique to recognize facial expressions in thermal
imagery.
PartVdealswithautomaticmovingobjectdetectioninairbornelow-resolution
near-infraredandthermalvideos.Chapter11addressestheissueofsafenavigation
ofbothmannedandunmannedaircraftsandpresentsanenhancedvisionsystemto
locate a runway and detect obstacles on it during approaching and landing opera-
tions using an infrared sensor mounted on an aircraft nose. Chapter 12 presents a
fastertechniquethanmultiscaleopticalflowforpreciselocalizationofboundariesof
movingobjectsinlow-resolutionthermalvideos.Thebasicprincipleiscombining
forwardandbackwardmotionhistoryimages.
Part VI focuses on fusion and registration techniques of multiple disparate
multichannel visible and thermal sensors for accurate image segmentation and
pedestrian contour detection (Chapter 13), tracking occluded objects using mu-
tual information (Chapter 14), and pedestrian tracking using up to four cameras
(Chapters15 and 16). Chapter 16 presents a pedestrian detection system based on
thesimultaneoususeoftwoopticalandthermalstereosystems.
The last part of this book (Part VII) focuses on multitarget tracking using laser
and infrared sensors. Chapter 17 presents a successful algorithm to track multiple
peopleinacrowdedsceneusinglaserimagery.Thelastchapter(Chapter18)ofthis
Preface ix
book reports the experimental results of a comparative study of both boosted and
adaptiveparticlefiltersforaffine-invarianttargettrackingininfraredimagery.
This practical reference offers a thorough understanding of theory and experi-
mentalfieldoperationalcharacteristicsofkeyalgorithmicbuildingblocksofcom-
putervisionsystemsusingnonvisualinfraredimagery.Itcontainsenoughmaterial
tofillatwo-semesterupper-divisionoradvancedgraduatecourseontopicsrelated
tomachinevisionanditsapplications,augmentedvision,infraredimagery,pattern
recognition, remote sensing, and information fusion. Scientists and teachers will
findin-depthcoverageofrecentstate-of-the-artworkonaugmentedmachinevision
in infrared imagery. Moreover, the book helps readers of all levels understand the
motivations,activities,trends,anddirectionsofresearchersandengineersinthema-
chinevisionindustryintoday’smarketandoffersthemaviewofthefutureofthis
rapidlyevolvingtechnologicalarea.Itshouldbenotedthatwhilethisbookprovides
a brief background review of computer vision and infrared spectrum, it is highly
recommended for nonexperts in this area to first read through some popular intro-
ductory references on image understanding, remote sensing, and electromagnetic
spectrum.
Thiseffortcouldnothavebeenachievedwithoutthevaluablesupportofmycol-
leaguesfromseveralactivecommunities,includingIEEEOTCBVSworkshopand
SPIEDefenseandSecuritySymposiumseries.Iamsogratefulfortheirpermission
to include their excellent work here; their expertise, contributions, feedback, and
reviewaddedsignificantvaluetothisgroundbreakingresource.
IwouldliketoextendthankstoallfolksatSpringer-Verlag,andinparticularto
CatherineBrettforhervaluablesupport.
Kokomo,Indiana,USA RiadIbrahimHammoud
February12,2008
Contents
Preface............................................................ vii
PartI InfraredSignaturesandClassification
1 InfraredThermographyforLandMineDetection ................ 3
1.1 Introduction.............................................. 4
1.2 ThermalModelingoftheSoil,IncludingShallowly
BuriedObjects ........................................... 7
1.2.1 MathematicalFormulation.......................... 7
1.2.2 EstimationoftheSoilThermalDiffusivity............. 12
1.2.3 EstimationoftheSoil-SurfaceBoundaryCondition ..... 13
1.3 InverseProblemSettingforBuriedObjectDetection............ 14
1.3.1 MathematicalFormulation.......................... 14
1.3.2 SimplificationoftheInverseProblem................. 15
1.3.3 A Two-Step Method for Solving the Simplified
InverseProblem................................... 16
1.4 ExperimentalDataandProcessingChain ..................... 18
1.4.1 DescriptionoftheMinefieldandMeasurement
System .......................................... 18
1.4.2 Preprocessing..................................... 20
1.4.3 EstimationoftheSoilThermalParameters ............ 21
1.4.4 ValidationoftheProposedThermalModel ............ 21
1.4.5 Effect of Mine Properties and Soil Type
ontheSoil-SurfaceThermalContrast................. 23
1.4.5.1 Effect of the Depth of Burial
andtheHorizontalSize ................... 23
1.4.5.2 EffectoftheMineHeight.................. 24
1.4.5.3 EffectofSoilType ....................... 26
1.4.6 AnomalyDetectionandReduction ................... 27
xi
xii Contents
1.4.7 ReconstructionoftheGeometric
andThermalProperties............................. 30
1.4.8 ClassificationoftheDetectedAnomalies.............. 32
1.5 Conclusions.............................................. 33
Chapter’sReferences ............................................ 34
2 Passive Polarimetric Information Processing for Target
Classification ............................................... 37
2.1 Introduction.............................................. 37
2.2 Theory .................................................. 38
2.2.1 Background ...................................... 39
2.2.1.1 Polarization ............................. 40
2.2.1.2 Refraction .............................. 42
2.2.2 SurfaceNormalfromGeometry ..................... 43
2.2.2.1 SpecialCase............................. 44
2.2.2.2 GeneralCase ............................ 45
2.2.3 InvariantsofPolarizationTransformations............. 47
2.2.3.1 ProbabilisticRepresentation ............... 48
2.2.3.2 InvariantAlgebra......................... 48
2.3 SimulationandExperimentalResults......................... 49
2.3.1 SurfacePropertiesandGeometry .................... 50
2.3.1.1 Single-SensorExample ................... 50
2.3.1.2 Dual-SensorExample..................... 51
2.3.1.3 LaboratoryExperiments................... 52
2.3.2 PolarimetricInvariants ............................. 55
2.4 SummaryandConclusions ................................. 56
Chapter’sReferences ............................................ 61
3 Vehicle Classification in Infrared Video Using the Sequential
ProbabilityRatioTest........................................ 63
3.1 IntroductionandProblem .................................. 63
3.2 One-ClassClassification ................................... 64
3.3 ObjectClassification....................................... 65
3.3.1 Shape-BasedClassification ......................... 66
3.3.1.1 GlobalFeatures .......................... 66
3.3.1.2 LocalFeatures........................... 66
3.3.2 Motion-BasedClassification ........................ 68
3.3.3 Shape-and-Motion-BasedClassification............... 68
3.4 OverallApproach ......................................... 68
3.5 Single-LookVehicleClassifier .............................. 70
3.5.1 ScaleSpaceFeatureExtraction ...................... 70
3.5.2 SignatureExtraction ............................... 71
3.5.3 SignatureMatching................................ 71
3.6 MultilookSequentialClassifier.............................. 74
3.7 DataandResults.......................................... 76
3.7.1 InfraredVideoData................................ 76