A multi-level thresholding approach using a hybrid optimal estimation algorithm

Transcription

1 Pattern Recognton Letters 28 (2007) A mult-level thresholdng approach usng a hybrd optmal estmaton algorthm Shu-Ka S. Fan *, Yen Ln Department of Industral Engneerng and Management, Yuan Ze Unversty, No. 135, Yuandong Road, Jhongl Cty, Taoyuan County 320, Tawan, ROC Receved 13 January 2006; receved n revsed form 25 August 2006 Avalable onlne 22 December 2006 Communcated by L. Younes Abstract Ths paper presented a hybrd optmal estmaton algorthm for solvng mult-level thresholdng problems n mage segmentaton. The dstrbuton of mage ntensty s modeled as a random varable, whch s approxmated by a mxture Gaussan model. The Gaussan s parameter estmates are teratvely computed by usng the proposed PSO + EM algorthm, whch conssts of two man components: () global search by usng partcle swarm optmzaton (PSO); () the best partcle s updated through expectaton maxmzaton (EM) whch leads the remanng partcles to seek optmal soluton n search space. In the PSO + EM algorthm, the parameter estmates fed nto EM procedure are obtaned from global search performed by PSO, expectng to provde a sutable startng pont for EM whle fttng the mxture Gaussans model. The prelmnary expermental results show that the hybrd PSO + EM algorthm could solve the mult-level thresholdng problem qute swftly, and also provde qualty thresholdng outputs for complex mages. Ó 2006 Elsever B.V. All rghts reserved. Keywords: Mult-level thresholdng; Mxture Gaussan curve fttng; Expectaton maxmzaton (EM); Partcle swarm optmzaton (PSO) 1. Introducton Image thresholdng s very useful n separatng objects from background mage, or dscrmnatng objects from objects that have dstnct gray-levels. Sezgn and Sankur (2004) have presented a thorough survey of a varety of thresholdng technques, among whch global hstogrambased algorthms, such as mnmum error thresholdng (Kttler and Illngworth, 1986), are employed to determne the threshold. In parametrc approaches (Weszka and Rosenfeld, 1979; Synder et al., 1990), the gray-level dstrbuton of each class has a probablty densty functon (PDF) that s assumed to obey a (mxture) Gaussan dstrbuton. An attempt to fnd an estmate of the parameters of the dstrbuton that wll best ft the gven hstogram data s * Correspondng author. Tel.: x2510; fax: E-mal address: smonfan@saturn.yzu.edu.tw (S.-K.S. Fan). made by usng the least-squares estmaton (LSE) method. Typcally, t leads to a nonlnear optmzaton problem, of whch the soluton s computatonally expensve and tmeconsumng. Besdes, Snyder et al. presented an alternatve method for fttng curves based on a heurstc method called tree annealng; Yn (1999) proposed a fast scheme for optmal thresholdng usng genetc algorthms, and Yen et al. (1995) proposed a new crteron for mult-level thresholdng, termed automatc thresholdng crteron (ATC) to deal wth automatc selecton of a robust, optmum threshold for mage segmentaton. More recently, Zahara et al. (2005) proposed a hybrd Nelder-Mead Partcle Swarm Optmzaton (NM-PSO) method to solve the objectve functon of Gaussan curve fttng for mult-level thresholdng. The NM-PSO method s effcent n solvng the mult-level thresholdng problem and could provde better effectveness than the other tradtonal methods n the context of vsualzaton, object sze and mage contrast. However, curve fttng s usually tme-consumng /$ - see front matter Ó 2006 Elsever B.V. All rghts reserved. do: /j.patrec

2 S.-K.S. Fan, Y. Ln / Pattern Recognton Letters 28 (2007) for mult-level thresholdng. To further enhance effcency whle mantanng qualty effectveness, an mproved method s warranted. For further detals of the NM-PSO method, see Zahara et al. (2005) and Fan and Zahara (2006). In ths paper, an mprovement upon Gaussan curve fttng s reported that the effcency of mage thresholdng methods could be enhanced n the case of mult-level thresholdng. We present a hybrd expectaton maxmzaton (EM) and partcle swarm optmzaton (PSO + EM) algorthm to solve the objectve functon of Gaussan parameter estmaton. The PSO + EM algorthm s appled to mage thresholdng wth mult-modal hstograms, and the performances of PSO + EM on Gaussan curve fttng are compared to the NM-PSO method. Secton 2 ntroduces the parametrc objectve functons that need to be solved n mage thresholdng. In Secton 3, the proposed hybrd PSO + EM algorthm s detaled. Secton 4 gves the expermental results and compares performances between two dfferent methods. Lastly, Secton 5 concludes ths research. 2. Mnmum error thresholdng by Gaussan model One of the most advanced technques n mage thresholdng s mnmum error thresholdng proposed by Kttler and Illngworth (1986), whch was remarked n the survey paper presented by Sezgn and Sankur (2004). In the begnnng, we consder an mage whose pxels assume gray level values, x, from the nterval [0, n]. It s convenent to summarze the dstrbuton of gray levels n the form of a hstogram h(x) whch gves the frequency of occurrence of each gray level n the mage (Kttler and Illngworth, 1986). Then, the dstrbuton of mage ntensty s modeled as a random varable, obeyng a mxture Gaussan model. A properly normalzed mult-modal hstogram p(x) of an mage can be ftted wth the sum of d probablty densty functons (PDFs) for fndng the optmal thresholds used n mage segmentaton (Synder et al., 1990). Wth Gaussan PDFs, the model follows the followng form: " # pðxþ ¼ Xd P pffffffffff exp ðx l Þ 2 2p r r 2 ; ð1þ where P s the a pror probablty and P d P ¼ 1, d s the levels of thresholdng, l s the mean, and r 2 s the varance of mode. A PDF model must be ftted to the hstogram data, typcally by usng the maxmum lkelhood or mean-squared error approach, n order to locate the optmal threshold. Gven the hstogram data h(x) (observed frequency of gray level, x), t can be defned below: hðxþ ¼ JðxÞ X L 1 ¼0 JðxÞ ; where J(x) denotes the occurrence of gray-level x over a gven mage range [0,L 1] where L s the total number of gray levels. It s expected to fnd a set of parameters, denoted by H, whch mnmze the fttng error: ð2þ Mnmze H ¼ X ½hðxÞ pðx; HÞŠ 2 : ð3þ x Here, H s the objectve functon (.e., Hammng Dstance) to be mnmzed wth respect to H, a set of parameters defnng the mxture Gaussan PDFs and the probabltes, as gven by H ={P,l,r ; =1,2,...,d}. Gven p(xj) and P, there exsts a gray level s for whch gray level x satsfes (for the case of two adjacent Gaussan PDFs) P 1 pðxj1þ > P 2 pðxj2þ; when x 6 s ð4þ P 1 pðxj1þ < P 2 pðxj2þ; when x > s: s s the Bayes mnmum error threshold at whch the mage should be bnarzed. Now usng the model p(xj,t), =1,2, the condtonal probablty e(x, T) of gray level x beng replaced n the mage by a correct bnary value s gven by eðx; T Þ¼pðxj; T ÞP ðt Þ=pðxÞ ¼ 1 x 6 T ð5þ 2 x > T : Takng the logarthm of the numerator n Eq. (5) and multplyng the result by 2, the followng quantty can be obtaned: eðx;t Þ¼ x l 2 ðt Þ þ 2logr ðt Þ 2logP ðt Þ ¼ 1 x 6 T r ðt Þ 2 x > T : The average performance profle for the whole mage can be characterzed by the crteron functon: JðT Þ¼ X pðxþeðx; T Þ: ð7þ x Thus, the problem of mnmum error threshold selecton can be formulated as one of mnmzng J(T),.e., JðsÞ ¼mn JðT Þ: ð8þ T After fttng the mult-modal hstogram, the optmal threshold could be determned by mnmzng Eq. (7) gven by the mxture Gaussan model, t can be found JðT Þ¼1þ2½P 1 log r 1 ðt ÞþP 2 log r 2 ðt ÞŠ 2½P 1 log P 1 ðt ÞþP 2 log P 2 ðt ÞŠ: ð9þ Eq. (9) can be computed easly and fnd ts mnmum. Lkewse, t can be extended to a mult-level thresholdng case: JðT ;...;T d 1 Þ¼1 þ 2 Xd ð6þ fp ðt Þ½logr ðt Þ logp ðt ÞŠg; ð10þ where the mage ntensty contans d modes,.e., t s a mxture of d Gaussan denstes. Here, the number of canddate thresholds to be evaluated s consderably greater 2. The number of ponts for whch the crteron functon must be computed s (L + 1)!/[(L +2 d)!(d 1)!] where L s the largest gray level startng from 0. For nstance, when d = 3 the number of ponts for functon evaluaton s 32,640. Kttler and Illngworth (1986) provded a fast

3 664 S.-K.S. Fan, Y. Ln / Pattern Recognton Letters 28 (2007) procedure for the value of threshold; the procedure wll be adopted n ths paper after the estmates of mxture model are obtaned. 3. Hybrd PSO + EM method In ths paper, the development of the hybrd algorthm ntends to mprove the effcency of the optmal thresholdng technques currently appled n practce. The goal of ntegratng expectaton maxmzaton (EM) algorthm and partcle swarm optmzaton (PSO) s to combne ther advantages and compensate for each other s dsadvantages. For nstance, the EM algorthm s effcent but excessvely senstve to the startng pont (.e., ntal estmates). A poor startng pont mght make the EM algorthm termnate prematurely or get stuck due to computatonal dffcultes. On the other hand, PSO belongs to the class of global, populaton-based search procedure but requres much computatonal effort than classcal search procedures. Smlar hybrdzaton deas have ever been dscussed n hybrd methods usng genetc algorthms and drect search technques, and they emphaszed the trade-off between soluton qualty, relablty and computaton tme n global optmzaton (Renders and Flasse, 1996; Yen et al., 1998). Ths secton starts by ntroducng the procedure of the EM algorthm and PSO, followed by a descrpton of the proposed hybrd method The EM algorthm For the PDF of mxture Gaussan functon (see Eq. (1)), the lkelhood functon s defned as follows: KðX; HÞ ¼ YN X d P px n ; l ; r 2 : ð11þ Thus, the logarthm of the lkelhood functon K(X; H) s gven by kðx; HÞ ¼ XN log Xd P px n ; l ; r 2 : In general, the parameter estmaton problem can be defned as bh arg max kðx; HÞ: ð12þ H Note that any maxmzaton algorthm could be used to fnd H, b the maxmum lkelhood estmate of parameter vector H (P,l, and r )(Tomas, 2005). Here, the EM algorthm assumes that approxmate (ntal) estmates P ðkþ ; l ðkþ,andr ðkþ are avalable for the parameters of the lkelhood functon K(X;H (k) ) or ts logarthm k(x;h (k) ). Then, better estmates P ðkþ1þ ; l ðkþ1þ, and can be computed by frst usng the prevous estmates r ðkþ1þ to construct a lower bound b(h (k) ) for the lkelhood functon, and then maxmzng the bound wth respect to H. Constructon of the bound b(h (k) ) s called the E step n that the bound s the expectaton of a logarthm. The maxmzaton of b(h (k) ) that yelds the new estmates P ðkþ1þ ; l ðkþ1þ,andr ðkþ1þ s called the M step. The EM algorthm terates the above two computatons untl convergence to a local maxmum of the lkelhood functon and obtans an estmated optmal soluton. In detal, for the case of mxture Gaussan parameters estmaton, the estmate P (k) (jn) s computed for the membershp probabltes: P ðkþ P ðkþ p x n ; l ðkþ ; r ðkþ ðjnþ ¼X K ; ð13þ p x n ; l ðkþ ; r ðkþ P ðkþ m¼1 where the prevous parameter estmates P ðkþ ; l ðkþ, and r ðkþ are requred. Ths s the actual computaton performed n the E step. The rest of the constructon of the bound b(h (k) ) uses Jensen s nequalty to bound the logarthm k(x;h (k) ) of the lkelhood functon: kðx; HÞ ¼ XN P XN log Xd X d where qðjnþ ¼P pðx n ; l ; r Þ: qð; nþ P ðkþ qð; nþ ð; nþ log P ðkþ ð; nþ ¼ b HðkÞ ; ð14þ The bound b(h (k) ) obtaned can be rewrtten as follows: X d b H ðkþ ¼ P ðkþ ðjnþlog qð; nþ XN X d P ðkþ ðjnþlog P ðkþ ðjnþ: Snce the old membershp probabltes P (k) (jn) are known, maxmzng b(h (k) ) s the same as maxmzng the frst of the two summatons: X d bh ðkþ ¼ P ðkþ ðjnþlog qð; nþ: ð15þ b(h (k) ) contans a lnear combnaton of d logarthms, and ths breaks the couplng of the equatons obtaned by settng the dervatves of b(h (k) ), wth respect to the parameters, equal to zero. The dervatve of b(h (k) ) wth respect to l s easly found to be ob k ol ¼ XN P ðkþ ðjnþ l x n : ð16þ r 2 Upon settng ths expresson to zero, the varance r can be cancelled, and the remanng equaton contans only l as the unknown as follows: l P ðkþ ðjnþ ¼ XN P ðkþ ðjnþx n : ð17þ

4 S.-K.S. Fan, Y. Ln / Pattern Recognton Letters 28 (2007) Ths equaton can be solved mmedately to yeld the new estmate of the mean: l ðkþ1þ ¼ P ðkþ ðjnþx n P ðkþ ðjnþ ; ð18þ whch s only a functon of old values (wth superscrpt k). The resultng value l ðkþ1þ s plugged nto the expresson for b(h (k) ), whch can now be dfferentated wth respect to r through a very smlar manpulaton to yeld vffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff r ðkþ1þ u1 ¼ d P ðkþ ðjnþkx n l ðkþ1þ k 2 t : ð19þ P ðkþ ðjnþ The dervatve of b(h (k) ) wth respect to P, subject to the constrant that the P 0 s add up to one, can be handled agan through the soft-max functon. Ths yelds the new estmate for the mxng probabltes as a functon of the old membershp probabltes: P ðkþ1þ ¼ 1 N P ðkþ ðjnþ: ð20þ Eventually, the new estmates (wth superscrpt k + 1) are all solved and would be fed nto next (EM) run untl the lkelhood converges. In ths paper, the termnaton condton s satsfed when the dfference between the current and pervous lkelhood value s smaller than the tolerance of The PSO algorthm Partcle swarm optmzaton (PSO), developed by Kennedy and Eberhart (1995), s one of the latest evolutonary optmzaton algorthms. PSO smulates a commonly observed socal behavor where members of a group tend to follow the leader of the group. Smlar to genetc algorthms (GA), PSO s also populaton-based and evolutonary n nature, wth one major dfference from GA that t does not mplement selecton; namely, all partcles n the populaton survve through the entre search process. The descrpton of PSO here s necessarly bref snce the complete procedure has been detaled n (Zahara et al., 2005). The partcle s velocty and poston are teratvely updated by v NEW d x NEW d ¼ w v OLD d þ C 1 r 1 x pd x OLD d þ C 2 r 2 x gd x OLD d ; ð21þ ¼ x OLD d þ v NEW d ; ð22þ where the acceleraton coeffcents C 1 and C 2 are two postve constants; w s an nerta weght, and r 1, r 2 are unformly generated random numbers from the range [0, 1] whch s ndependently generated for every teraton. For further detals regardng PSO, see Eberhart and Sh (2001) and Hu and Eberhart (2001) Hybrd PSO + EM algorthm Intalzaton. Generate a populaton of sze 3N + 1. Repeat (1).Evaluaton & Rankng. Evaluate and Rank the ftness of all partcles. From the populaton select the global best partcle and record ts locaton (parameters). (2).EM Update. Apply EM approach to the global best partcle and replace ts locaton wth the updated parameters (Note that the global best partcle of each teraton s updated only once). Excepton: If EM approach fals n update, generate a new soluton (partcle) randomly and then go back to step (1). (3).PSO Update. Apply velocty update to the remanng 3N partcles accordng to equatons (21) and (22). Untl a termnaton condton (EM algorthm converges) s met. Fg. 1. The hybrd PSO + EM algorthm procedure. Unlke the real synergy of hybrdzaton n NM-PSO, the proposed PSO + EM algorthm s more lke a twostep search procedure by two dfferent methods. The populaton sze of ths hybrd PSO + EM approach s set at 3N + 1 when solvng an N-dmensonal problem n order to make a far comparson wth the NM-PSO method. Note that, n Gaussan curve fttng we need to estmate N =3d 1 parameters. For example, n b-level thresholdng, there are fve parameters, H ={P 1,l 1,r 1,l 2,r 2 }and P 2 =1 P 1. The ntal populaton s created by usng a randomzed startng pont, that s, all partcles are randomly generated n search space. A total of 3N + 1 partcles are sorted by the ftness, and the top one partcle (.e., elte) s then fed nto the EM algorthm to update ts locaton (soluton) once n parameter space. The remanng 3N partcles are adjusted by PSO takng nto account the poston of ths elte partcle f updated successfully. Ths procedure for adjustng the 3N partcles nvolves selecton of the global best partcle and fnally velocty updates. The global best partcle of the populaton s determned accordng to the sorted ftness values. Note that f the global best partcle could not be updated through the EM algorthm, the PSO + EM algorthm wll generate a new one to replace the old one, and reselect the global best partcle n the new swarm. So, selecton (or called flterng) n PSO + EM s possble. By Eqs. (21) and (22), a velocty update for each of 3N partcles s then carred out. The 3N + 1 partcles are sorted n preparaton for repeatng the next teraton. The process termnates when a certan convergence crteron s satsfed. Here, Fg. 1 summarzes the procedure of PSO + EM and Fg. 2 shows the flowchart. 4. Expermental results In ths secton, we evaluate and compare the performances of these two methods: the NM-PSO algorthm and proposed PSO + EM algorthm whle mplementng Gaussan curve fttng for mult-level thresholdng. Both methods are mplemented on an Intel Celeron 2.8 GHz platform wth 512 MB DDR-RAM usng Matlab. The test mages are of sze pxels wth 8 bt gray-levels, taken under natural room lghtng wthout the support of any specal lght source. The stoppng crteron of NM-PSO referred to Zahara et al. (2005) s 10N teratons when solvng an N-dmensonal problem. However, the

5 666 S.-K.S. Fan, Y. Ln / Pattern Recognton Letters 28 (2007) Apply velocty update to the remanng 3N partcles No Generate PSO populaton of sze 3N+1 Evaluate the ftness of each partcle Rank partcles based on the ftness Apply EM approach to update the best partcle once Updated successfully? Yes Compute lkelhood of updated soluton Does lkelhood converge? Yes Gan the optmum soluton (estmates) No Fg. 2. Flowchart of PSO + EM algorthm. Ftness functon Generate a new soluton randomly. PSO + EM algorthm s halted whle the lkelhood functon mprovement falls wthn To farly compare these two parametrc methods effcency and effectveness, for the NM-PSO algorthm the ntal parameter estmates used n (Zahara et al., 2005) are followed. For the PSO + EM algorthm, the pror estmates, P 0 s, are set at P =1/d; remanng parameters are all generated at random. The experment starts wth oxdaton nduce stackng fault (OISF) mage taken under un-central lghtng as shown n Fg. 3(a). It ams to verfy that the PSO + EM algorthm can delver satsfactory performance. Note agan that the mnmum error thresholdng technque as shown n Eq. (10) s employed here. The CPU tmes recorded n all tables do not nclude computaton of the threshold values. As can be seen from Fg. 3(b) and (c), the NM-PSO and PSO + EM algorthms both perform 4-level mxture Gaussan curve fttng and produce qute dfferent threshold values (see Fg. 3(f) and (g)). Obvously, the PSO + EM algorthm can handle low-contrast part well n upper-rght and lower-left corners of mage but the NM-PSO algorthm cannot. Comparng computaton tme n the 4-level fttng, the PSO + EM algorthm only takes one twenty-ffth of tme as the NM-PSO algorthm. The PSO + EM algorthm returns a much smaller Hammng dstance than the NM- PSO algorthm,.e., 40% reducton n fttng error. If a hgher fttng qualty s desred, a more accurate fttng result by usng seven levels s ganed whle computaton Fg. 3. Expermental results for the OISF mage.

6 S.-K.S. Fan, Y. Ln / Pattern Recognton Letters 28 (2007) tme ncreases only to 5.84 s from 3.75 s. To be precse, the further mprovement on Hammng dstance made from 4- to 7-level fttng s about 5%. The second experment of PCB mage n Fg. 4(a) produces smlar results to those presented n the OISF example. Regardless of the complex pattern, the PSO + EM algorthm stll performs better than the NM-PSO algorthm n Hammng dstance and computaton tme for the 4-level fttng, achevng 9% and 93% mprovements, respectvely. For the thresholdng effectveness, the threshold values obtaned by usng the PSO + EM algorthm are totally dfferent from the NM+PSO algorthm (see Fg. 4(f) and (g)). Ths mght explan partly why the proposed algorthm can separate pnholes from background n the lower part of the PCB mage but the NM-PSO cannot. As the number of fttng level ncreases to 7, the fttng error n terms of Hammng dstance s reduced by 68%, whch makes the local patterns rehabltated more clearly. Based on the above two examples, t s perhaps surprsng that the PSO + EM algorthm s very quck, a fact that can be attrbuted to ts superor speed by ncorporatng EM nto the algorthm. Mnmum fttng errors acheved would be largely due to the global search capablty provded by PSO. The thrd experment s conducted on the RAM mage dsplayed n Fg. 5(a). The speed advantage by the PSO + EM algorthm stll prevals for ths mult-level thresholdng case, but the segmentaton qualty s no longer acceptable for ndustral applcatons (snce the mark on the module s not cleanly dentfed). Lookng at the orgnal hstogram, there are two hgh peaks between gray levels 50 and 100, whch complcates stuatons much where only the 4-level fttng s chosen. Thanks to PSO + EM s excellent speed, we could ncrease the fttng level to gan better results wthout sacrfcng computaton tme too much. Fortunately, full detals of the RAM mage are revealed when the fttng level s rased to 7, and Hammng dstance s further enhanced down to four dgts after decmal pont (see Fg. 5(g) and (h)). It mples that the PSO + EM algorthm can serve as a hgh-dmensonal tool for dealng wth the thresholdng stuatons where the other algorthms fal to yeld qualty outcomes wthn reasonable computaton tme. The proposed PSO + EM algorthm renders a degree of effcency n practcal applcatons, whch s unattanable wth the NM-PSO algorthm. Table 1 summarzes the estmaton results of prevous three experments, ncludng Hammng dstance, a set of parameter estmates, and CPU tme. One scenaro, 4-level fttng, s for the NM-PSO algorthm. Two scenaros, 4- and 7-level fttng, are for the PSO + EM algorthm. Wth a vew to evaluatng the algorthm s computaton robustness, the last two columns n the table report the average Hammng dstance (H) and CPU tme (T) statstcs computed over 10 ndependent runs. The number n parenthess ndcates the standard devaton of the assocated statstc. Concernng Hammng dstance statstc, the PSO + EM algorthm can yeld smaller average fttng errors and standard devatons than the NM-PSO algorthm n three test mages. These results clearly demonstrate the proposed Fg. 4. Expermental results for the PCB mage.

7 668 S.-K.S. Fan, Y. Ln / Pattern Recognton Letters 28 (2007) Fg. 5. Expermental results for the RAM mage. Table 1 Expermental results va mult-level thresholdng approaches and ts statstcs Image Method Level (d) Hammng dstance (H) Parameter set {P 1,...,P d ; l 1,...,l d ; r 1,...,r d } OISF NM-PSO e 04 {0.083, 0.020, 0.740, 0.157; , , , ; , 3.293, , } PSO + EM e 04 {0.258, 0.037, 0.106, 0.599; , , , ; , 7.876, , } e 04 {0.032, 0.001, 0.074, 0.186, 0.031, 0.000, 0.676; , , , , , , ; 9.959, , , , 5.267, 6.612, } PCB NM-PSO e 04 {0.441, 0.106, 0.100, 0.353; , , , ; 9.143, 8.371, 6.172, } PSO + EM e 04 {0.311, 0.313, 0.027, 0.349; , , , ; , , , 6.864} e 04 {0.199, 0.053, 0.103, 0.082, 0.173, 0.214, 0.176; , , , , , , ; , , , 1.559, , 4.398, } RAM NM-PSO e 03 {0.260, 0.330, 0.391, 0.019; , , , ; 7.569, 4.665, 4.696, 4.373} PSO + EM e 03 {0.521, 0.132, 0.066, 0.281; , , , ; , , , 4.311} e 04 {0.020, 0.056, 0.241, 0.103, 0.143, 0.221, 0.216; , , , , , , ; , , , 7.122, 2.099, , 3.541} a Average/standard devaton computed over 10 ndependent runs. CPU tme (T) H Statstc a T Statstc a e 03 (1.47e 03) (0.5742) e 04 (3.74e 05) (3.9456) e 04 (2.70e 05) (5.0104) e 03 (1.71e 03) (4.2062) e 04 (9.68e 05) (2.2355) e 04 (3.88e 05) (3.4487) e 03 (2.99e 03) (1.4501) e 03 (2.33e 04) (5.1679) e 04 (2.57e 05) ( ) algorthm s edge over the NM-PSO algorthm n the consstency and stablty of fttng accuracy. Relatng to CPU statstc, bascally, the PSO + EM algorthm requres far less CPU tme on average than the NM-PSO algorthm.

8 S.-K.S. Fan, Y. Ln / Pattern Recognton Letters 28 (2007) There s, however, an excepton as the 7-level fttng s appled to the RAM mage. In such a case, the average of CPU tme requred by the PSO + EM algorthm s somewhat less than that of the NM-PSO algorthm, but the standard devaton of the former s way larger than that of the latter. Yet, the effcency s apparently not the major concern n ths nstance, so the PSO + EM algorthm wth 7 levels s stll recommended for provdng qualty outputs despte ts nstablty n CPU tme. As can be seen from Table 1, the NM-PSO algorthm exhbts a better stablty of computaton tme than the PSO + EM algorthm (see the standard devatons). The reason s that, to mantan a reasonable effcency, the NM-PSO algorthm has to use a fxed teraton stoppng crteron. By contrast, for the PSO + EM algorthm a qute small tolerance of n lkelhood s used nstead to termnate the search, and all the ntal parameter estmates, except the pror probablty, are all generated randomly. A careful observaton should be placed on the thrd experment. For the nstance of RAM mage, the PSO + EM algorthm s forced to requre many tmes computaton effort than the other two examples to gan a qualty fttng result. The fundamental message s that a mxture Gaussan model appled n ths paper may not be best to ft a Laplace-dstrbuton-type hstogram, lke the one shown n Fg. 5. A smlar dffculty can also arse when the PSO + EM algorthm s appled to a unform-dstrbuton-type hstogram for the mult-level curve fttng. Therefore, we feel safe to allege that the mxture generalzed Gaussan dstrbuton (GGD) model may be a better selecton for the RAM mage where a 4-level fttng would suffce. 5. Concludng remarks The NM-PSO algorthm has been verfed that s effectve n the b-level and tr-level thresholdng cases, but ts computaton tme becomes aggravated n the case of hgher dmensonal thresholdng. In order to make the evolutonary computaton method more practcal n on-lne applcatons, a much faster searchng procedure called the PSO + EM algorthm s proposed, whch solves the objectve functon of mxture Gaussan curve fttng by combnng the EM and PSO algorthms. Expermental results n terms of three test mages show that the PSO + EM algorthm converges much faster than the NM-PSO algorthm n the mult-level Gaussan curve fttng. In addton, comparsons between the PSO + EM and NM-PSO algorthms demonstrate that the new algorthm provdes better qualty n vsualzaton, object shape and contrast of mage segmentaton, partcularly when the mage has a very complex pattern. Thus, the PSO + EM algorthm can be consdered a promsng and vable method for on-lne mult-level thresholdng due to ts superor effcency. Acknowledgements The authors wsh to thank two anonymous referees for provdng many constructve suggestons that greatly mproved the qualty on an earler verson of ths paper. References Eberhart, R.C., Sh, Y., Trackng and optmzng dynamc systems wth partcle swarms. In: Proc. of the Congress on Evolutonary Computaton. Seoul, Korea, pp Fan, S.-K.S., Zahara, E., A hybrd smplex search and partcle swarm optmzaton for unconstraned optmzaton. Eur. J. Oper. Res. do: /j.ejor Hu, X., Eberhart, R.C., Trackng dynamc systems wth PSO: Where s the cheese? In: Proc. of The Workshop on Partcle Swarm Optmzaton. Indanapols, IN, USA. Kennedy, J., Eberhart, R.C., Partcle Swarm Optmzaton. In: Proc. of the IEEE Int. Conf. on Neural Networks. Pscataway, NJ, USA, pp Kttler, J., Illngworth, J., Mnmum error thresholdng. Pattern Recognton Lett. 19, Renders, J.M., Flasse, S.P., Hybrd methods usng genetc algorthms for global optmzaton. IEEE Trans. Systems. Man Cybernet. Part B: Cybernetcs 26, Sezgn, M., Sankur, B., Survey over mage thresholdng technques and quanttatve performance evaluaton. J. Electron. Imagng 13 (1), Synder, W., Blbro, G., Logenthran, A., Rajala, S., Optmal thresholdng a new approach. Pattern Recognton Lett. 11, Tomas, C. Estmatng Gaussan Mxture Denstes wth EM A Tutoral < EM/tomasEM.pdf>. Weszka, J., Rosenfeld, A., Hstogram modfcatons for threshold selecton. IEEE Trans. Systems. Man Cybernet. 9, Yen, J.C., Chang, F.J., Chang, S., A new crteron for automatc multlevel thresholdng. IEEE Trans. Image Process. 4 (3), Yen, J., Lao, J.C., Lee, B., Randolph, D., A hybrd approach to modelng metabolc systems usng a genetc algorthm and smplex method. IEEE Trans. Systems. Man Cybernet. Part B: Cybernetcs 28, Yn, P.Y., A fast scheme for optmal thresholdng usng genetc algorthms. Sgnal Process. 72, Zahara, E., Fan, S.-K.S., Tsa, D.-M., Optmal mult-thresholdng usng a hybrd optmzaton approach. Pattern Recognton Lett. 26,