COMPARITIVE ANALYSIS OF FLLT AND JSEG

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1 COMPARITIVE ANALYI OF FLLT AND JEG Ms AKILA VICTOR Asst. PROFEOR, CE VIT UNIVERITY, VELLORE TAMILNADU, INDIA. ABTRACT The man key pont of preferental mage segmentaton s to segment object of user nterest based on ntenstes, boundares and texture and gnorng the remanng portons. It explans the trees of shapes to represent mage content. In the tree of shape we use algorthms called as FLLT (Fast Level Lne Transform) and JEG (Jsegmentaton). Here both the algorthm are compared and an analyss s done to dsplay the result of objects selected from pror mages. Key Words: FLLT, Jegmentaton, Texture Classfcaton, Peer Group Flter. I. INTRODUCTION egmentaton plays a major role n dgtal mage processng. Image segmentaton algorthms are desgned to segment an mage nto several regons so that the contents of each regon represent meanngful objects. The segmentaton results can therefore be utlzed for post processng stages such as object recognton. Image segmentaton smplfes post processng stages by focusng attenton on each ndvdual segment. Edge detecton methods such as the Canny detector [1] were wdely appled for ths task. Edge detecton methods utlze ntensty gradents to detect the boundares of objects. However, edge detecton methods usually generate edges that are not closed contours, and ths causes dffcultes for later processng such as object recognton. Curve evoluton methods [2][6] have been popular for mage segmentaton snce the early 1990s. These methods evolve the ntalzed curve(s) to the boundares of objects n an mage. The above methods tend to segment the whole mage nto several regons, whch s challengng for mages wth cluttered background. On the other hand, ths s not always necessary n real applcatons. The user may be nterested n fndng only the locaton of objects of nterest. For example consder a car movng on a road then the user can preferentally segment the car mage by extractng the background. Ths s the key dea of preferental mage segmentaton, whch means to preferentally segment objects of nterests from an mage and gnore the remanng portons of the mage for ths applcaton. The dea of preferental mage segmentaton bears some smlartes to the object detecton methods from mages [3]. These methods detect the exstence and rough locaton of objects n an mage, e.g., n usng a sparse, part-based object representaton and a learnng method respectvely. The mage parsng method utlzes probablstc methods to unfy segmentaton, detecton and recognton. These methods, however, tend to classfy/parse an whole mage nstead of meanngful objects. Furthermore, these methods are usually computatonally ntense. Ths method s motvated by the utlzaton of pror nformaton n curve evoluton models. II. BACKGROUND ecton A shows how an mage s represented usng a tree of shapes. It also shows how a tree structure s ntroduced for mage representaton. ecton B descrbes the relatonshp between color and geometry n natural mages. ecton C ntroduces the technques of planar curve matchng, whch can be utlzed to compare the boundares of dfferent objects. ecton D ntroduces the technque of texture Identfcaton, whch ncludes the wavelet transformaton whch solates the bands nto many sub bands. A. Image Representaton usng trees of shapes The tree of shapes represents mages based on the technques of the contrast-nvarant mathematcal morphologes [4][7]. Ths method s based on the theory of mage representaton usng connected components of set of fnte permeters n the space of functons wth weakly bounded varatons (WBV). The representaton of an mage usng a tree of shapes utlzes the nferor or the superor of a level lne to represent an object, and takes the boundary of the nferor area as the shape of the object. Therefore, only the closed shapes are generated. Ths representaton also provdes a tree structure to represent the spatal relatonshp for the objects n an mage. They have several advantages. Frst, they represent regons nstead of curves n an mage, whch provde a way to handle the contents nsde the regons. econd, they are nvarant to the contrast changes n an mage, whch may be caused by the change of lghtng Thrd, closed boundares are acqured for each upper level set or lower level set, whch can be utlzed for shape matchng of the regons. IN :

2 The nestng of level sets provdes an ncluson tree for an mage. The ncluson tree from the famly of upper level sets and the tree from the famly of lower level sets, however, can be dfferent f the connected components are drectly utlzed. The shapes n the lower level are spatally ncluded n the shapes n the next hgher level. The tree of shapes, therefore, provdes a natural way to represent the spatal relatonshps between the shapes n the mage. It s straghtforward to fnd upper level sets and lower level sets n an mage by thresholdng. A tree of shapes can be further constructed by the nestng of level sets. However, ths method s computatonally ntense. Ths trees of shapes uses a method called as Jegmentaton(J-EG) whch s used for segmentng objects. It splts the regon based on the user nterest. doman(ma) j ma(p) _ vg We wll note Uv nsteed of Uv(ma) when there s no ambguty on the nput mage. The FLLT take an mage nput and bulds a non-redundant tree.e. a pont of nput s stored only once by the nodes of t. The herarchy follows the ncluson between the shapes. In the followng, the nput mage wll be noted nput THE FLLT ALGORITHM The Fast Level Lne Transform (FLLT) [5] constructs a contrast-nvarant representaton of an mage. Ths algorthm bulds a tree whch follows the ncluson of the shapes contaned n an mage. For an mage flter, havng the contrast-nvarant property s nterestng. For nstance, n the feld of document mage analyss, ths representaton s precous to extract characters regardless of whether ther are brghter or darker than ther surroundngs. Ths document presents how ths algorthm s ntroduced n our mage processng Lbrary and shows the results of some connected flters that can be derved from ths representaton. Fg.1 Orgnal Object Lower level set For an mage ma, we defne the lower level set of value v as the followng set of ponts: Lv(ma) = fp 2 doman(ma) j ma(p) _ vg We wll note Lv nsteed of Lv(ma) when there s no ambguty on the nput mage. Upper level set For an mage ma, we defne the upper level set of value v as the followng set of ponts: Uv(ma) = fp 2 Fg 2 egmented Anmal ALGORITHM: For each pont pmn that s a local mnmum not already n the tree, 1. Intalzaton. Here, we ntate the constructon of CC startng wth the local mnmum pmn. _ g = U(pmn) _ CC a new node wth g as value of the tree. _ A fpmng _ N fg _ CC:shape:add(pmn) 2. Growng. _ If A = fg then the tree s complete. _ Enlarge N to ts neghborhood: N N [ fx; x 2 neghborhood(a) n CC:shapeg _ Update gn: gn mn x2n U(x) 3. N and A. Three cases are dstngushed: (a) If gn > g, we meet the parent component. The value of the parent component s gn. We therefore update g. Max tree The Max tree s useful to apply flters on an mage. It allows us to select a subset of the shapes of the mage accordng to ther characterstcs. A tradtonal example s the area openng and closng of surface (whch works on the areas of the shapes.it s also used n astronomcal mage processng because all the shapes are brghter than ther neghborhood. We thus flter the mage IN :

3 to clean t and to proceed to automatc classfcaton of galaxes. The Max tree bulds a tree of components modelng ncluson of shapes n an mage. Ths tree contans only the shapes havng a darker neghborhood. Mn tree Ths tree s the dual of the Max tree. It thus contans only the shapes darker that ther neghborhood.it s only effcent on the mages havng brght background and dark shapes, therefore specallyeffectve n analyss of mages of documents. Applyng a flter on ths tree allows quckly extractng all the characters (connected), or arrayng elements n an nvoce. Buld the Mn tree and the Max tree, keepng for each node, one pont belongng to each of ts holes. Ths s the most complcated part of the FLLT. Merge the two trees, by fllng the holes of the Mn tree wth nodes of the Max tree and nversely. THE JEG METHOD The J-EG algorthm [5] provdes a faster way to construct a tree of shapes. J-EG s a pyramdal algorthm based on regon growng. At the begnnng of each teraton, a J-mage s computed. New segments are dened for groups of connected pxels presentng small values and approprate sze. Next, pxels presentng values smaller than the mean J-value of the unsegmented pxels are merged wth adjacent exstng segments. Ths step s repeated for a new J-mage wth smaller wndow sze untl a determned mnmum sze s reached. Fnally, a postprocessng segment mergng step usng hstogram dfferences s performed to reduce over-segmentaton. The essental dea of JEG s to separate the segmentaton process nto two ndependently processed stages, color quantzaton and spatal segmentaton. The 1st pass detects unform regons based on quantzed color features only, whle the 2nd pass uses the color quantzaton of the 1st pass n a mult-scale technque n order to detect regon boundares. The algorthm allows the user to manually set varous thresholds and parameters or use the default parameter values n a more automatc mode. The output of the JEG algorthm s a segmented mage wth the regon boundares hghlghted as well as a gray-mask mage wth each regon gven a dstnct label.applyng the crteron to local wndows n the class-map results n the "J-mage, n whch hgh and low values correspond to possble boundares and nterors of color-texture regons. A regon growng method s then used to segment the mage based on the mult-scale J-mages. For vdeo sequences, a regon trackng scheme s embedded nto regon growng and problems of moton estmaton are avoded. The vdeo sequences can be used to acheve consstent segmentaton and trackng results, even for scenes wth arbtrary nonrgd object moton. Color Quantzaton Colors n the mage are reduced through peer group flterng (PGF) and vector quantzaton. PGF s a nonlnear algorthm for mage smoothng and mpulsve nose removal. The result of color quantzaton s a classmap whch assocates a color class label to each pxel belongng to the class. In the frst stage, colors n the mage are quantzed to several representng classes that can be used to dfferentate regons n the mage. Ths quantzaton s performed n the color space alone wthout consderng the spatal dstrbutons. Afterwards, mage pxel colors are replaced by ther correspondng color class labels, thus formng a class-map of the mage. The man focus of ths work s on spatal segmentaton, where a crteron for "good" segmentaton usng the class-map s proposed. The JEG algorthm [5] s the prmary colortexture segmentaton tool for ths system. Its prmary usage s to segment a frame nto homogenous regons of color and texture. These homogenous regons of color/texture correspond to the nterestng objects to be tracked. JEG performs ths segmentaton n a 2-pass algorthm. Fg.3 orgnal fgure J-EG Calculatons The J value can be calculated m 1 N z Here m mean that s to be calculated for the gven mage. T z Z Here T represents the total varance of the data ponts. Z z m z 2 IN :

4 Color mage w = Where can be as follows C 1 z Z z m 2 W = Total varance of data pont belongng to same class Color space quantzaton Color class map J B W patal egmentaton Where ( ) / T W W J-Image Classfcaton Here J s the value to be evaluated.these s some of the calculatons to be performed for Jegmentaton. And the segmented object here below n fg 2 s an example of how regons are spltted. Usng the above calculaton the Jegmented values are found out. patal segmentaton The second method s the spatal segmentaton. It explans f two related objects are there n the mage. Then the spatal segmentaton s used to dentfy the mage correctly. For example f two tree mages are n the same fgure t could be easly separated and spltted nto two dfferent regons. Usng ths the J mage s classfed and the J mage s got. Now t s ready for regon growng and the desred segmentaton results are obtaned. egmentaton J-Image Results Fg 4: chematc results of J-seg Regon Growng egmentaton Results Fg 4: chematc results of J-seg Fg 5 explans the steps of J-EG algorthm, t ncludes two mportant steps the frst s color quantzaton and the next s spatal segmentaton. In the frst step color mage s gven where quantzaton s performed and the quantzaton results n fndng out the color maps. The color maps are obtaned by fndng out the mean for the related pxels. B. Color and Geometry n Mathematcal Morphologes Fg 5 JEG Fgure An mproved space HL (IHL) s utlzed for the color model. The color n every pxel s represented wth three channels (Y,,H), whch corresponds to the gray level, saturaton and hue respectvely. The IHL space [10], compared to other spaces such as HL and HV, has the property of a well-behaved saturaton coordnate. IN :

5 The IHL space always has a small numercal value for near-achromatc colors, and s completely ndependent of the brghtness functon. For a pxel wth color(r,g,b) n the(r,g,b) space, and the correspondng pxel wth color(y,,h), The transformaton from the RGB space to the IHL space s Where Y=0.2126R G B =MAX(R,G,B)-MIN(R,G,B) = { 360º-H, f B>G H, OTHERWIE H = 2) Affne flterng [11] of the extracted level lnes at several scales. Ths step s appled to smooth the curves usng affne curvature deformaton to reduce the effects of nose. 3) Local encodng of peces of level lnes after affne normalzaton. Both local encodng and affne normalzaton are desgned for local shape recognton methods. Ths step wll help to deal wth occlusons n real applcatons. 4) Comparson of the vectors of features of the mages. Eucldean dstance s utlzed to compare the feature vectors. The performance of curve matchng between two curves s calculated after affne flterng, curve normalzaton and local encodng. It tells how a boundary can be detected from the fgure. It ncludes four mportant steps for fndng out the exact boundary. The boundary matchng example s as follows Ths method uses the transformaton and the total order n to extract the shapes and buld the tree of shapes for color mages. The nverse transformaton from the IHL space to the RGB space s not utlzed. The color mage s syntheszed so that ts grey verson contans no shape nformaton. Usng the IHL color space and the total order a tree of shapes s bult for the color mage. The color matchng s as follows a b c Fg 6a,b,c shows the dentfcaton of colors. C. Planar Curve Matchng The method n defnes the shape of a curve as a conjuncton of shape elements and further defnes the shape elements as any local, contrast nvarant and affne nvarant part of the curve. These defntons are orented to provde nvarance to nose, affne dstorton, contrast changes, occluson, and background. The shape matchng between two mages are desgned as the followng steps. 1) Extracton of the level lnes for each mage. The level set representatons are utlzed here for the extracton. The level lne s defned as the boundares of the connected components as shown before. a b c Fg 7 a, b, c represents the boundary of the mages Texture Matchng Image transforms are very mportant n dgtal processng they allow to accomplsh less wth more. For example the Fourer Transform may be used to effectvely compute convolutons of mages or the Dscrete Cosne Transform may be used to sgnfcantly decrease space occuped by mages wthout notceable qualty loss. Wavelet Transform (WT) s a relatvely new concept as a whole, even t though t ncorporates some of the transforms, whch have been known for long tme. Texture analyss s mportant n many applcatons of computer mage analyss for classfcaton, detecton or segmentaton of mages based on local spatal patterns of ntensty or color. Textures are replcatons, symmetres and combnatons of varous basc patterns or local functons, usually wth some random varaton. Textures have the mplct strength that they are based on ntutve notons of vsual smlarty. Ths means that they are partcularly useful for searchng vsual databases and other human computer nteracton applcatons. However, snce the noton of texture s ted to the human semantc meanng, computatonal descrptons have been broad, vague and sometmes conflctng. The method of texture analyss chosen for feature extracton s crtcal to the success of the texture classfcaton. However, the metrc used n comparng the feature vectors s also IN :

6 clearlycrtcal. Many methods have been proposed to extract texture features ether drectly from the mage statstcs The texture dentfcaton example can be as follows a b c d Fg 8 a,b,c,d explans the wavelet dentfcaton 3. PERFORMANCE COMPARION Both the algorthms are appled for the trees of shapes and also they are compared based on the tme and the qualty of the mage. The FLLT algorthm can be used to gve a qualty mage and whereas the JEG algorthm works very well on lmted tme. They can be done n larger databases. The performance of the Jsegmentaton and Fast level lne transform algorthm are compared and the performance are plotted as follows GRAPH 1 ROC CURVE UING POITIVE MEAURE FOR FLLT AND JEG GRAPH 2 COMPARIION OF FLLT AND JEG 4. UMMARY AND FUTUREWORK The method utlzes both the ntensty and shape pror nformaton by means of the tree of shapes and boundary matchng. It s nvarant to contrast change and smlarty transformatons such as scale, rotaton and for the translaton. Future research on segmentaton methods wll be focused on the mult scale analyss s also appled to larger mage databases to examne ts performance. Ths can be done for the movng mages n the database. A systematc evaluaton of the proposed method wll be performed on large mage databases, and the results wll be provded REFERENCE [1] J. F. Canny, A computatonal approach to edge detecton, IEEETrans. Pattern Anal. Mach. Intell., vol. 8, no. 6, pp , Jun [2] J. ethan, Level et Methods and Fast Marchng Methods, 2nd ed. Cambrdge, U.K.: Cambrdge Unv. Press, 1999, Cambrdge Monograph.on Appled and Computatonal Mathematcs [3]. Agarwal, A. Awan, and D. Roth, Learnng to detect objects n mages va a sparse, part-based representaton, IEEE Trans. Pattern AnayssMach. Intell., vol. 26, no. 11, pp , Nov [4] P. Monasse, Morphologcal representaton of dgtal mages and applcaton to regstraton, Ph.D. dssertaton, Ceremade, Pars, [5] Ynng Deng and B.. Manjunath, Unsupervsed egmentaton of Color-Texture Regons n Images and Vdeo, IEEE Trans on Pattern Analyss and Machne Intellgence, vol.22, no. 6, p , 2001 [6] D. Cremers and G. Funka-Lea, Dynamcal statstcal shape prors for level set based sequence segmentaton, Varatonal and Level et Methods Comput. Vs., pp , [7] P. Monasse and F. Guchard, Fast computaton of a contrastnvarant mage representaton, IEEE Trans. Image Process., vol. 9, no. 5, pp , May 2000 [8] Y.-G. Wang, J. Yang, and Y.-C. Chang, Color-texture mage segmentaton by ntegratng drectonal operators nto jseg method, Pattern Recogn. Lett., vol. 27, no. 16, pp , [9]. Y. Yu, Y. Zhang, Y. G. Wang, and J. Yang, Unsupervsed color-texture mage segmentaton, Journal of hangha Jaotong Unversty (cence), vol. 13, no. 1, pp , February [10] A. Hanbury and J. erra, A 3d-Polar Coordnate Colour Representaton utable for Image Analyss, PRIP, T.U.Wen, Tech. Rep. PRIP-TR-77, [11] J. L. Lsan, L. Mosan, P. Monasse, and J. M. Morel, On the theory of planar shape, Multscale Model mul., vol. 1, no. 1, pp. 1 24, Author Profle Ms. Akla Vctor receved her Mc software Engneerng degree n 2008 from Anna Unversty Chenna and s also a unversty Rank holder and M.E. degree n Computer cence and Engneerng n 2010 from Anna Unversty Trunelvel, Trunelvel, Inda. Presently workng as an Asst.Prof n VIT Unversty, Vellore.Her areas of nterest are Image Processng, Cryptography and oftware IN :

7 Engneerng. he has presented papers n natonal and Internatonal Conferences n varous felds. As part of ths paper, she s workng on developng segmentaton preferentally for movng mages. Akla Vctor / (IJCE) Internatonal Journal on Computer cence and Engneerng IN :