Raviteja.Bhima / International Journal of Engineering Research and Applications(IJERA) ISSN: 2248-9622 www.ijera.comVol. 3...
Raviteja.Bhima / International Journal of Engineering Research and Applications(IJERA) ISSN: 2248-9622 www.ijera.comVol. 3...
Raviteja.Bhima / International Journal of Engineering Research and Applications(IJERA) ISSN: 2248-9622 www.ijera.comVol. 3...
Raviteja.Bhima / International Journal of Engineering Research and Applications(IJERA) ISSN: 2248-9622 www.ijera.comVol. 3...
Raviteja.Bhima / International Journal of Engineering Research and Applications(IJERA) ISSN: 2248-9622 www.ijera.comVol. 3...
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International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.

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W33123127

  1. 1. Raviteja.Bhima / International Journal of Engineering Research and Applications(IJERA) ISSN: 2248-9622 www.ijera.comVol. 3, Issue 3, May-Jun 2013, pp.123-127123 | P a g ePerformance Evaluation Of Some Algorithms For AcousticImages Using Image Segmentation TechniqesRaviteja.BhimaAbstractThis paper is concerned withUnsupervised sonar image segmentation .Wepresent a new estimation and segmentationprocedure on images provided by a highresolution sonar. The sonar image is segmentedin to two kinds of regions:Shadow(corresponding to a lack of acousticreverberation behind each object lying onseabed) and Reverberation (Due the reflectionof acoustic wave on the seabed and on theobjects).The unsupervised contextual method isdefined as a two-step processExpectationMaximization Algorithm and Statistical RegionSnakeTheory[1]. The expectation maximizationalgorithm is very useful for parameter estimationproblems in finite mixtures. The stochastic EMalgorithm is a widely applicable approach forcomputing maximum likelihood estimates for themixture problem[2].During past years the activecontour models (Snakes) have been widely usedfor finding the contours of objects. Thissegmentation strategy is classically edge-based inthe sense that the snake is driven to fit themaximum of an edge map of the scene.Thistechnique has been successfully applied to realsonar images, and is compatible with anautomatic processing of massive amounts of data.IndexTerms: Active contours , Image edgedetection , Imageprocessing ,Unsupervised Imagesegmentation ,Expectation,MaximizationAlgorithm,Snake Algorithm ,IntroductionIn image analysis, image segmentationisthe partition of a digital image into multiple regions(sets of pixels) with each region associated with oneof a finite number of classes that are modeled asdistinct random fields[3]. An important problem isthat of parameter estimation since the random fieldmodels are mostly parametricmodels specified by asmall number of parameters which have to beestimated from the data. In most of the previousconsiderations, supervised approacheswhich usuallyassume training data to be available for imageclasses, the parameters can be estimated from thetraining data before segmentation is widely used.Although this supervised approach avoids thecomplexity of a combined problem of estimationand segmentation, it is rather unrealistic in manypractical situations.To automatically detect objects in side-scansonar images, supervised approaches are difficultdue to the large variability in the appearance of side-scan images. Thus, unsupervised approacheswhichalso allow analysis to be carried out while the data isbeing collected, enabling mission plans to bechanged depending on the data collected arestrongly needed[4].In sonar imagery, three kinds of regionscan be visualized: highlight, shadow, and sea bottomreverberation areas. The highlight information iscaused by the reflection of the acoustic wave on theobject, while the shadow zone corresponds to a lackof acoustic reverberation behind the object.Fig (1) The formation of object area in side scanimageSystem Model& EM Algorithm
  2. 2. Raviteja.Bhima / International Journal of Engineering Research and Applications(IJERA) ISSN: 2248-9622 www.ijera.comVol. 3, Issue 3, May-Jun 2013, pp.123-127124 | P a g eThe EM algorithm is the most general effectivealgorithm for solving in complete data problems.The definitive reference for this algorithm waswritten by Dempster, Laird & Rubin in 1977.Basically it works as follows.Let x= (yT, zT) be a set of random data of interest,where y is the observed data & z is the part of xthat is not observable. For example, in imagesegmentation y is the set of image pixel intensitiesand is observable, whereas z is the image classstatus of the pixels that is not visible. Usually theobservable y is called the incomplete data, & x iscalled the complete data.Let g(x/Ψ) be the probability density function ofthe complete data, where Ψ is a parameter vectorthat characterize the density function. Theincomplete data problem is to estimate Ψ basedonly on the observations represented by theincomplete data y.It generates from some initial approximation Ψ(0),a sequence of estimate Ψ(p), where each iterationconsist of the following two stepsExpectation-step(E=step)Determine the functionQ(Ψ|))=E[log(g(X|Ψ))|y,(p)] (1)Maximization-step(M-step)Find the maximizorΨ(p+1) )=arg max(Q(Ψ| (p)))(2)Here, p represents the pthiteration. It has been thatunder some relatively general conditions, theestimate converges to the ML estimate of theincomplete data problem, at least locally.Model of EM AlgorithmThis is the simplest case, where the data is fullycategorized. The fully categorized data can begenerally represented as{xi,i=1,……,N}={(yi,ziT)T,i=1,…..,N}(3)Where each zi=(zi,1,……,zi,k)Tis an indicator vectorof length k with 1 in the kthposition & zeroselsewhere. Then the likelihood functioncorresponding to (  i,……..,  N) can then be writtenin the form  NiNiikiii zzyx ff1 11)|,.....,,()|()|g(X )1(ˆ pk =NipikN 1ˆ1;k=1,2,…K,Snake AlgorithmThe technique discussed in this section is based onresearch conducted in [1], where snakes are usedunder a statistical framework to segment images.Whilst many region based techniques are oftencomputationally insensitive, the techniquesdescribed here is very fast, allowing much of theintensive calculations to be computed before thesegmentation commences. This is possible whenthe statistical laws present in the image can bedescribed by the exponential family, making theapproach applicable in a wide range of physicalsituations[12].Assume that the observed scene (The raw side scansonar image) y is composed of two areas: Target(Shadow or highlight) and background of, we wishto, segment the target region. We consider theimage y ={y(i,j)|(i,j)[1,Ni][1,Nj]} to becomposed of Ni Nj pixels where the gray levels ofthe Nt target pixels and the gray levels of NbBackground pixels are assumed to be uncorrelatedand independently disturbed. They are described bytheir respectively probability density functionsf(y(i,j)| t ) and f(y(i,j)| b ) where t and b Arethe parameters of the probability densityfunctions[10].We define a binary window functionW={w(i,j)|w(i,j) [1,Ni] [1,Nj]} which definesthe shape of the snake at any instant of time.Defining w(i,j) to be equal to 1 inside thewindowand 0 elsewhere, the image becomescomposed of tworegions Ωt ={(i,j)|w(i,j)=1} andΩb={(i,j)|w(i,j)=0} so that the observed image canbe viewed as the sum of two componenty(i,j)=t(i,j)w(i,j)+b(i,j)(1-w(i,j)) (4)
  3. 3. Raviteja.Bhima / International Journal of Engineering Research and Applications(IJERA) ISSN: 2248-9622 www.ijera.comVol. 3, Issue 3, May-Jun 2013, pp.123-127125 | P a g eWhere t(i,j) and b(i,j) are values drawn fromtheirrespective probability distributions.Therefore the purpose of segmentation becomes toestimate the most likely shape w for the target inthe sense. Without any a priori knowledge aboutthe shape of the target, the best w is chosen bymaximizing the likelihood function[8].g[y|w,t,b]=g( t|  bbtg |(). ) (5)The likelihood function is expressed as a product ofprobabilities as the pixels are assumed to beuncorrelated andu{y(i,j)|(i,j) u}(u=t or u=b). (6)As we can see the likelihood function in (5.2)depends on the parameters of the probabilityfunctions as well as the window shape w. theparameter vector uwhere u{t,b} are the priori unknown and are ingeneral of ni interest .However for segmentation tobe possible it is necessary to compute these values.The parameters can be calculated using a variety oftechniques such as a maximum a posterioriapproach, but for simplicity, a maximum likelihoodapproach is again utilised as no a priori knowledgeon the values of the parameters is available[8].In order to proceed one has to specify parametersexpression for the probability density functionsf(y(i,j)|t) and f(y(i,j)|bHere we considerprobability density functions which belong to theexponential families for r(i,j) and b(i,j). Taking intoaccount the sufficient statistics if we chooseMaximum likelihood estimation and insert theseestimates in the likelihood function l(y,w)[9].l(y,w)=log{ ]([ tttG }+log{ )]([ bbtG }(4)Where Guare the functions which depend on yonly via t( u).The window function w that maximizes thecriterion J(y,w)=-l(y,w) then performs a maximumlikelihood segmentation of the target in the sense.The criterion can consequently be regarded asenergy acting on the snake since its minimizationforces the contour to surround thetarget. For thispurpose, we can use an iterativealgorithm in whichwe have to calculate the criterion J(y,w) for eachdeformation of the snake. Thus, the minimizationprocedure can be time consuming. In the nextsection, we show how to obtain the optimalcriterion J(y,w).Simulation ResultsSide scan sonar Imagesclose image10 20 30 40 50 60 7010203040506070Bot-hat image10 20 30 40 50 60 7010203040506070
  4. 4. Raviteja.Bhima / International Journal of Engineering Research and Applications(IJERA) ISSN: 2248-9622 www.ijera.comVol. 3, Issue 3, May-Jun 2013, pp.123-127126 | P a g eSegmentation result of a synthetic imageProbability Density of the Three Pixel Classes(Good Conditions)Snake Algorithm:ConclusionImage processing is a rapidly growingarea of computer science. Its growth has beenfueled by technological advances in digitalxyMorphological clustering image10 20 30 40 50 60102030405060123xySegmentation of Synthetic image10 20 30 40 50 601020304050601230 10 20 30 40 50 6000.020.040.060.080.10.120.140.160.18Normalized historgram and estimated mixturepixel intensity valueprobabilityNomalized histogramSum of pdfShadowBackgroundTarget10 20 30 40 50 60 7010203040506070510152025303540455010 20 30 40 50 60 70102030405060705101520253035404550xyClustering and segmentation of Synthetic image10 20 30 40 50 60102030405060123Iteration Result: Polygon 8 Nodes (Bad HighlightContrast)EM AlgorithmIteration Result: Polygon 4 Nodes (Bad HighlightContrast)
  5. 5. Raviteja.Bhima / International Journal of Engineering Research and Applications(IJERA) ISSN: 2248-9622 www.ijera.comVol. 3, Issue 3, May-Jun 2013, pp.123-127127 | P a g eimaging, computer processors and mass storagedevices. Fields which traditionally used analogimaging are now switching to digital systems, fortheir flexibility and affordability. Importantexamples are medicine, film and video production,photography, remote sensing, and securitymonitoring.Nowadays, image segmentation represents a task ofgrowing importance in many different fields. Thistechnique is finding broad applications inunderwater acoustics, space born remote sensing,medical imaging systems, non-distractive materialanalysis, as well as spoken languageprocessing[14].This paper concludes that it is to explorethe potential growth areas of using these twoalgorithms in side scan imagery. Evaluating andcomparing their performances for both syntheticand real sonar images using the software simulationtool MATLAB.References[1] R. Adams and L. Bischof, “Seeded regiongrowing,” IEEE Trans. Pattern Anal.Machine Intell., vol. 6, June 1994.[2] A. C. Bovic, M. Clark, and W. S. Geisler,“Multichannel texture analysis usinglocalized spatial filters,” IEEE Trans.Pattern Anal. Machine Intell., vol. 12, pp.55–73, Jan. 1990.[3] J. M. H. Du Buf, “Gabor phase in texturediscrimination,” Signal Process., vol. 21,pp. 221–240, 1990.[4] J. M. H. Du Buf and P. Heitkämper,“Texture features based on Gabor phase,”Signal Process., vol. 23, pp. 225–244,1991.[5] J. Canny, “Computational approach toedge detection,” IEEE Trans. PatternAnal. Machine Intell., vol. 8, pp. 679–698,Nov. 1986.[6] L. D. Cohen, “On active contour modelsand balloons,” CVGIP: ImageUnderstand., vol. 53, pp. 211–218, Mar.1991.[7] J. G. Daugman and C. J. Downing,“Demodulation, predictive coding, andspatial vision,” J. Opt. Soc. Amer. A, vol.12, pp. 641–660, Apr. 1995.[8] R. Deriche, “Optimal edge detection usingrecursive filtering,” in Proc. IEEE Int.Conf. Computer Vision, 1987, pp. 501–505.[9] D. Dunn, W. E. Higgins, and J. Wakeley,“Texture segmentation using 2-D Gaborelementary functions,” IEEE Trans.Pattern Anal. Machine Intell., vol. 16, pp.130–149, Feb. 1994.[10] T. Meier and K. Ngan, “Automaticsegmentation of moving objects for videoobject plane generation,” IEEE Trans.Circuits Syst. Video Technol., vol. 8, pp.525–538, 1998.[11] D. Wang, “Unsupervised videosegmentation based on watersheds andtemporal tracking,” IEEE Trans. CircuitsSyst. Video Technol., vol. 8, pp. 539–546,1998.[12] C. Gu and M. Lee, “Semiautomaticsegmentation and tracking of semanticvideo objects,” IEEE Trans. Circuits Syst.Video Technol., vol. 8, pp. 572–584,1998.[13] M. Kass, A. Witkin, and D. Terzopoulos,“Snakes: Active contour models,” Int. J.Comput. Vis., vol. 1, pp. 321–331, 1988.[14] F. Leymarie and M. Levine, “Trackingdeformable objects in the plane using anactive contour model,” IEEE Trans.Pattern Anal. Machine Intell.,vol. 15, pp.617–633, 1993.

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