Augmented Vision Perception in Infrared: Algorithms and Applied Systems PDF
Preview Augmented Vision Perception in Infrared: Algorithms and Applied Systems
Advances in Pattern Recognition Forothertitlespublishedinthisseries,goto http://www.springer.com/series/4205 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 BritishLibraryCataloguinginPublicationData AcataloguerecordforthisbookisavailablefromtheBritishLibrary LibraryofCongressControlNumber:2008939138 (cid:2)c Springer-VerlagLondonLimited2009 Apartfromanyfairdealingforthepurposesofresearchorprivatestudy,orcriticismorreview,asper- mittedundertheCopyright,DesignsandPatentsAct1988,thispublicationmayonlybereproduced, storedortransmitted,inanyformorbyanymeans,withthepriorpermissioninwritingofthepublish- ers,orinthecaseofreprographicreproductioninaccordancewiththetermsoflicencesissuedbythe CopyrightLicensingAgency.Enquiriesconcerningreproductionoutsidethosetermsshouldbesentto thepublishers. 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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
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