digital image processing matlab gonzalez pdf download

... Digital Image Processing – Part II 54 Morphological Image Processing (a) (c) (d) (b) Digital Image Processing – Part II 87 Image Segmentation (a) (b) (c) (a) (b) Digital Image ... Digital Image Processing – Part II 13 Colour Image Processing (b) Figure 5 Illustration of intensity slicing and colour assignment. Digital Image Processing – Part II 10 Colour Image ... (a) (b) (c) (a) (b) (c) Digital Image Processing – Part II 59 Morphological Image Processing Figure 43 Example of morphological reconstruction.(a) A binary image of the blobs as the mask image; (b)...

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... xix 1 Introduction 15 1.1 What Is Digital Image Processing? 15 1.2 The Origins of Digital Image Processing 17 1.3 Examples of Fields that Use Digital Image Processing 21 1.3.1 Gamma-Ray Imaging ... Used 34 1.4 Fundamental Steps in Digital Image Processing 39 1.5 Components of an Image Processing System 42 Summary 44 References and Further Reading 45 2 Digital Image Fundamentals 34 2.1 Elements ... Cataloging-in-Pubblication Data Gonzalez, Rafael C. Digital Image Processing / Richard E. Woods p. cm. Includes bibliographical references ISBN 0-201-18075-8 1. Digital Imaging. 2. Digital Techniques. I....

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... image processing is intimately tied to the development of the digital computer. In fact, digital images require so much storage and computational power that progress in the field of digital image ... Publishing, NY. Baxes, G.A. [1994]. Digital Image Processing: Principles and Applications, John Wiley & Sons, NY. Gonzalez, R. C. and Woods, R. E. [1992]. Digital Image Processing, Addison-Wesley, Reading, ... overlap be- tween image processing and image analysis is the area of recognition of indi- vidual regions or objects in an image. Thus, what we call in this book digital image processing encompasses...

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... unknown at this stage of processing in practice [7]. There are several non-optimized temporal DOF reduction methods available, such as the Doppler-domain (joint domain) localized processing (DDL/JDL) ... Press LLC Space-TimeAdaptiveProcessing forAirborneSurveillanceRadar HongWang SyracuseUniversity 70.1MainReceiveApertureandAnalogBeamforming 70.2DatatobeProcessed 70.3TheProcessingNeedsandMajorIssues 70.4TemporalDOFReduction 70.5AdaptiveFilteringwithNeededandSample-Supportable DOFandEmbeddedCFARProcessing 70.6Scan-To-ScanTrack-Before-DetectProcessing 70.7Real-TimeNonhomogeneityDetectionandSample ConditioningandSelection 70.8SpaceorSpace-RangeAdaptivePre-SuppressionofJammers 70.9ASTAPExamplewithaRevisittoAnalogBeamforming 70.10Summary References Space-TimeAdaptiveProcessing(STAP)isamulti-dimensionalfilteringtechniquedevelopedfor minimizingtheeffectsofvariouskindsofinterferenceontargetdetectionwithapulsedairborne surveillanceradar.Themostcommondimensions,orfilteringdomains,generallyincludetheaz- imuthangle,elevationangle,polarizationangle,dopplerfrequency,etc.inwhichtherelativelyweak targetsignaltobedetectedandtheinterferencehavecertaindifferences.Inthefollowing,theSTAP principlewillbeillustratedforfilteringinthejointazimuthangle(space)anddopplerfrequency (time)domainonly. STAPhasbeenaveryactiveresearchanddevelopmentareasincethepublicationofReedetal.’s seminalpaper[1].WiththerecentlycompletedMultichannelAirborneRadarMeasurementproject (MCARM)[2]–[5],STAPhasbeenestablishedasavaluablealternativetothetraditionalapproaches, suchasultra-lowsidelobebeamformingandDisplacedPhaseCenterAntenna(DPCA)[6].Muchof STAPresearchanddevelopmenteffortshavebeendrivenbytheneedstomakethesystemaffordable, tosimplifyitsfront-hardwarecalibration,andtominimizethesystem’sperformancelossinseverely nonhomogeneousenvironments.Figure70.1isageneralconfigurationofSTAPfunctionalblocks[5, 7]whoseprincipleswillbediscussedinthefollowingsections. c  1999byCRCPressLLC ... Space-TimeAdaptiveProcessing forAirborneSurveillanceRadar HongWang SyracuseUniversity 70.1MainReceiveApertureandAnalogBeamforming 70.2DatatobeProcessed 70.3TheProcessingNeedsandMajorIssues 70.4TemporalDOFReduction 70.5AdaptiveFilteringwithNeededandSample-Supportable DOFandEmbeddedCFARProcessing 70.6Scan-To-ScanTrack-Before-DetectProcessing 70.7Real-TimeNonhomogeneityDetectionandSample ConditioningandSelection 70.8SpaceorSpace-RangeAdaptivePre-SuppressionofJammers 70.9ASTAPExamplewithaRevisittoAnalogBeamforming 70.10Summary References Space-TimeAdaptiveProcessing(STAP)isamulti-dimensionalfilteringtechniquedevelopedfor minimizingtheeffectsofvariouskindsofinterferenceontargetdetectionwithapulsedairborne surveillanceradar.Themostcommondimensions,orfilteringdomains,generallyincludetheaz- imuthangle,elevationangle,polarizationangle,dopplerfrequency,etc.inwhichtherelativelyweak targetsignaltobedetectedandtheinterferencehavecertaindifferences.Inthefollowing,theSTAP principlewillbeillustratedforfilteringinthejointazimuthangle(space)anddopplerfrequency (time)domainonly. STAPhasbeenaveryactiveresearchanddevelopmentareasincethepublicationofReedetal.’s seminalpaper[1].WiththerecentlycompletedMultichannelAirborneRadarMeasurementproject (MCARM)[2]–[5],STAPhasbeenestablishedasavaluablealternativetothetraditionalapproaches, suchasultra-lowsidelobebeamformingandDisplacedPhaseCenterAntenna(DPCA)[6].Muchof STAPresearchanddevelopmenteffortshavebeendrivenbytheneedstomakethesystemaffordable, tosimplifyitsfront-hardwarecalibration,andtominimizethesystem’sperformancelossinseverely nonhomogeneousenvironments.Figure70.1isageneralconfigurationofSTAPfunctionalblocks[5, 7]whoseprincipleswillbediscussedinthefollowingsections. c  1999byCRCPressLLC ...

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... IntroductionandMotivation Thischapterreviewstheapplicationsofantennaarraysignalprocessingtomobilenetworks.Cellular networksarerapidlygrowingaroundtheworldandanumberofemergingtechnologiesareseentobe criticaltotheirimprovedeconomicsandperformance.Amongtheseistheuseofmultipleantennas andspatialsignalprocessingatthebasestation.ThistechnologyisreferredtoasSmartAntennasor, moreaccurately,asSpace-TimeProcessing(STP).STPreferstoprocessingtheantennaoutputsin bothspaceandtimetomaximizesignalquality. Acellulararchitectureisusedinanumberofmobile/portablecommunicationsapplications.Cell sizesmayrangefromlargemacrocells,whichservehighspeedmobiles,tosmallermicrocellsorvery smallpicocells,whicharedesignedforoutdoorandindoorapplications.Eachoftheseoffersdifferent channelcharacteristicsand,therefore,posesdifferentchallengesforSTP.Likewise,differentservice deliverygoalssuchasgradeofserviceandtypeofservice:voice,data,orvideo,alsoneedspecic STPsolutions.STPprovidesthreeprocessingleverages.Therstisarraygain.Multipleantennas capturemoresignalenergy,whichcanbecombinedtoimprovethesignal-to-noiseratio(SNR).Next isspatialdiversitytocombatspace-selectivefading.Finally,STPcanreduceco-channel,adjacent channel,andinter-symbolinterference. Theorganizationofthischapterisasfollows.InSection68.2,wedescribethevectorchannelmodel forabasestationantennaarray.InSection68.3wediscussthealgorithmsforSTP.Section68.4outlines theapplicationsofSTPincellularnetworks.Finally,weconcludewithasummaryinSection68.5. c 1999byCRCPressLLC ... York, 1974. [23] Krim, H. and Viberg, M., Two decades of array signal processing research: the parametric approach, IEEE Signal Processing Magazine, 13(4), 67–94, July 1996. [24] Lee, W.C., Mobile ... blind identification of multichannel FIR filters, IEEE Trans. on Signal Processing, 1995. [29] Orfanidis, S.J., Optimal Signal Processing – An Introduction, Macmillan Publishing Co., New York, 1985. [30]...

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... 1995. [35] Reddy, V.U., Mathew, G. and Paulraj, A., Some algorithms for eigensubspaceestimation, Digital Signal Processing, 5, 97–115, 1995. [36] Regalia, P.A. and Loubaton, P., Rational subspaceestimation ... data matrix where the kth column corresponds to the kth snapshot vector, x k ∈ C n . With block processing, the correlation matrix for a zero mean, stationary, ergodic vector process is typically ... sensor noise usually dominates numerical errors, this choice may not be critical in most signal processing applications. 66.2.2 Short Memory Windows for Time Varying Estimation Ultimately, weareinterestedin...

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... lim n→∞ E{W(n)} . (23.14) c  1999 by CRC Press LLC Williamson, G.A. “Adaptive IIR Filters” Digital Signal Processing Handbook Ed. Vijay K. Madisetti and Douglas B. Williams Boca Raton: CRC Press ... Signal Processing, 41(2), 617–628, 1993. [14] Lin, J N. and Unbehauen, R., Bias-remedy least mean square equation error algorithm for IIR parameter recursive estimation, IEEE Trans. Signal Processing, 40(1), ... Acoustics, Speech, Signal Processing, 38(7), 1222–1227, 1990. [17] Regalia, P.A., Stable and efficient latticealgorithms for adaptive IIR filtering, IEEE Trans.Signal Processing, 40(2), 375–388,...

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... Ling, F., and Nikias, C.L., Advanced Digital Signal Processing , Macmillan Publishing, New York, 1992. [3] Widrow, B. and Stearns, S.D., Adaptive Signal Processing , Prentice-Hall, Englewood ... diverges. c  1999 by CRC Press LLC Sayed, A.H. & Rupp, M. “Robustness Issues in Adaptive Filtering” Digital Signal Processing Handbook Ed. Vijay K. Madisetti and Douglas B. Williams Boca Raton: CRC Press ... algorithms, IEEE Trans. Signal Processing , 44(8), 1982–1989, Aug. 1996. [19] Sayed, A.H. and Kailath, T., A state-space approach to adaptive RLS filtering, IEEE Signal Processing Magazine , 11(3),...

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... (19.2) whereW(n)=[w 0 (n)w 1 (n)ÃÃÃw L1 (n)] T isthecoefcientvector,X(n)=[x(n)x(n 1)ÃÃÃx(nL+1)] T istheinputsignalvector,d(n)isthedesiredsignal,e(n)istheerrorsignal, andà(n)isthestepsize. TherearethreemainreasonswhytheLMSadaptivelterissopopular.First,itisrelativelyeasyto implementinsoftwareandhardwareduetoitscomputationalsimplicityandefcientuseofmemory. Second,itperformsrobustlyinthepresenceofnumericalerrorscausedbynite-precisionarithmetic. Third,itsbehaviorhasbeenanalyticallycharacterizedtothepointwhereausercaneasilysetupthe systemtoobtainadequateperformancewithonlylimitedknowledgeabouttheinputanddesired responsesignals. c 1999byCRCPressLLC [22] Farden,D.C., Tracking properties of adaptivesignal processingalgorithms, IEEETrans.Acoust., Speech, Signal Processing, ASSP-29(3), 439–446, June 1981. [23] Bitmead, R.R. and Anderson, ... algorithms, IEEE Trans. Signal Processing, 41(9), 2811–2825, Sept. 1993. [25] Douglas, S.C. and Meng, T.H Y., Normalized data nonlinearities for LMS adaptation, IEEE Trans. Signal Processing, 42(6), 1352–1365, ... Signal Processing, 4(3), 219–216, May-June 1990. [28] Harris, R.W., Chabries, D.M., and Bishop, F.A., A variable step (VS) adaptive filter algorithm, IEEE Trans. Acoust., Speech, Signal Processing, ASSP-34(2),...

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... 1995. [8] Hayes, M.S., Statistical Digital Signal Processing and Modeling, John Wiley & Sons, New York, 1996. [9] Haykin, S., Advances in Spectrum Analysis and Array Processing, Prentice Hall, Englewood Cliffs, ... which plays a major role in many applied sciences such as radar, speech processing, underwater acoustics, biomedical signal processing, sonar, seismology, vibration analysis, control theory, and ... n      2 (14.70) c  1999 by CRC Press LLC Djuric, P.M. & Kay S.M. “Spectrum Estimation and Modeling” Digital Signal Processing Handbook Ed. Vijay K. Madisetti and Douglas B. Williams Boca Raton: CRC Press...

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