Object extraction in a quantum inspired soft computing environment
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Abstract
Differentkindsofnoiseremovalintelligenttechniquesarebeingusedtoex-
newlinetract objectsbymeansofremovalofnoiseartifacts.Amongtheintelligenttools
newlinein vogue,theMulti-LayerSelfOrganizingNeuralNetworkarchitecture(ML-
newlineSONN) architectureisefficientinextractingobjectsfromblurredandnoisybi-
newlinenary images.Grayscaleimagesaresegmentedusingthenetworkbyresorting
newlineto amultilevelsigmoidalactivationfunctionsoastoelicitnetworkresponses
newlineto allpossiblegrayscales.AparallelMulti-LayerSelfOrganizingNeuralNet-
newlinework architecture(PSONN)comprisingthreeparallelMulti-LayerSelfOrganiz-
newlineing NeuralNetworkarchitectureswith/withoutmultilevelsigmoidalactivation
newlinefunctions canbeusedtoextractpureandtruecolornoisyimages.
newlineIn thisthesis,aquantumversionoftheMLSONNarchitectureisproposedtoex-
newlinetract andsegmentnoisyimages.TheproposedQMLSONNnetworkarchitecture
newlinecomprises threedifferentlayersviz.inputlayer,hiddenlayerandoutputlayer
newlineenvisaged by qubit neurons,whereeach qubit neuronineachlayercorresponds
newlineto eachpixeloftheinputimage.Theinterconnectionweightsbetweenthediffer-
newlineent layersarerepresentedasquantumrotationgates.The qubit neuronhasthe
newlineability toextracttheobjectsbyresortingtotheprincipleofquantumsuperposi-
newlinetion.
newlineThe qubit neuronsofthethreenetworklayersprocesstheimageinformationus-
newlineing quantumrotationgatesasinterconnectionweightsguidedbyaquantum
newlinebackpropagationalgorithm.Attheoutputlayer,aquantummeasurementis
newlineused todestroythequantumstatestoyieldnetworkoutputswhiletheinter-
newlineconnection weightsareadjustedusingaquantumbackpropagationalgorithm.
newlineA parallelversionoftheproposedarchitecturecomprisingthreeindependent
newlineQMLSONN architectures(referredtoasQPSONN)witheacharchitectureen-
newlinetrustedforprocessingthethreeindividualprimarycolorcomponents(R,G,B),
newlineis alsoproposedtoextractcolorobjectsfrompurecolornoisyimages.
newlineA functionalmodificationoftheproposedQMLSONNarchitectureisachieved
newlineby incorporatingaMultilevelSigmoidal(MUSIG)activationfunctionsothatthe
newlinenetwork isabletosegmentmultilevelgrayscaleimages.Onsimilarlines,the
newlineQPSONN architectureisalsomodifiedbyincorporatingmultilevelcharacte