Key-Words: - Under sear Hydrothermal vent image; grey; blue chroma; OTSU; FCM

Transcription

1 A Fast and Effectve Segentaton Algorth for Undersea Hydrotheral Vent Iage FUYUAN PENG 1 QIAN XIA 1 GUOHUA XU 2 XI YU 1 LIN LUO 1 Electronc Inforaton Engneerng Departent of Huazhong Unversty of Scence and Technology, Wuhan, Chna 1 Traffc Scence and Engneerng College of Huazhong Unversty of Scence and Technology, Wuhan, Chna 2 Abstract: - Ths paper puts forward a segentaton algorth for undersea hydrrotheral vent age. Basng on the n-depth analyss on the dfference between sea-water area, roc and soe area, whch are the three nds of objects n the typcal undersea hydrotheral vent age, we eploy both OTSU thresholdng and Fuzzy C-Mean (FCM) clusterng to segent the age, and dscard all the non-nterested area lvng only soe area. Experents show that ths algorth can segent the age both qucly and precsely. Key-Words: - Under sear Hydrotheral vent age; grey; blue chroa; OTSU; FCM 1 Introducton In vew of the ense potental of ocean exploture, expecally the abundant resources whch s portant for huan s exstence, the nternatonal county has placed ncreasngly hgher expectaton for ocean econoy. Undersea hydrotheral vent s a very portant object for resource explorng. Practcal explorng experence deonstrates that t s around the hydrotheral vent that the lvng-gvng actvty s flourshng and the less-coon etal usually dstrbutes. Object search and recognton n the undersea 3 densonal space s of crucal portance for undersea ntellgent operaton. Inforaton collecton of undersea objects anly reles on the lght, sound vson. The research of undersea vson technology s stll n ts coenceent, especally the research on non-structured objects. Many technologes, whch are popular n the ground-based artfcal vson applcaton, are unsutable for undersea applcaton. The undersea envronent s coplex and changeful, and the ocean current has bg pact on object recognton. The vsblty s extreely low for water s one thousandth as transparent as ar. The lght asslaton and dsperson effect s strong, whch result n the quc attenuaton of age sgnals. All these has been bg challenges for the developent of undersea artfcal vson technology. Iage segentaton s portant for age analyss. By separatng objects as well as extractng and easurng paraeters, the orgnal age s transfored nto a ore abstract for, n favor of age analyss and understandng. Typcal undersea hydrotheral vent age coprses sea-water area, soe area, roc and other undersea propagatons, wth soe area beng our nterestng object.(see Fg 1 as exaple) (a) (b) Fg1 Undersea hydrotheral vent age As can be seen, the undersea vsblty s very low whle the hydrotheral vent s dversfed and lac of structure features and ncludes both lghts and shades n t. The roc area has a wealth of holes and s ult-colored. Besdes, there are any specle nose n sea-water area, ntroduced by lght s bac and for-scatterng. All of these features, add dffcultes to the segentaton tas, and ae the tradtonal grey-level based segentaton algorths unsutable for undersea hydrotheral vent age. There have been any researches on age segentaton. The ethod eployed nclude thresholdng, regon-detecton, oton exanaton and object tracng; and feature eployed nclude grey-level, color nforaton, reflectvty and texture. Ths paper, basng on the n-depth analyss on the dfference between sea-water area, roc and soe area, whch are the three nds of objects n the typcal undersea hydrotheral vent age, puts forward an fast end effectve segentaton algorth. we eploy both OTSU thresholdng and Fuzzy C-Mean (FCM) clusterng to segent the age, dscard all the non-nterested area leavng only the

2 soe area. Experents show that ths algorth can segent the age both qucly and precsely. 2 Algorth Illustraton Thresholdng s a nd of practcal technology n age segentaton, whch separates the age nto object and bacground area and thus realzes the bpolar processng. The ost popular thresholdng ethod ncludes OTSU, optal threshold, hstogra characterstc pea analyss.etc. These algorth are sple and have quc processng speed. However, ther cannot gve good perforance when objects have poor separablty. FCM clusterng algorth, fro the angle of statstc recognton, calculates and fuzzy ebershp related to the speces center of each pxel by teratve calculaton. The coputatonal coplexty s relatvely hgh whlee the perforance s stable. Snce the undersea hydrotheral vent s dversfed and lac of salent features, ad the dfference between the three an nds of objects s feeble, t s hard to acheve precse segentaton by only one step, whch reles hghly on the robustness of features and the segentaton algorth. Basng on the n-depth analyss on the dfference between sea-water area, roc and soe area, we use both OTSU and FCM clusterng to segent the age, wpe off the non-nterested area step by step, and thus leavng only the soe area n the vson feld. 2.1 Thresholdng segentaton basng on the analyss of hstogra characterstc pea Snce the undersea hydrotheral vent s dversfed and the llunaton condton s not good, the tradtonal grey feature based segentaton algorth s no longer sutable. Color nforaton should be ntroduced n the segentaton process. Snce the lght scatterng feature dffers aong the three objects, ther general chroa s dfferent fro each other. The sea-water area s always the bluest, followed by soe area and then roc area. Therefore, we defne blue-chroa feature by equaton (1) (Assue r,g,b are the R,G,B channel of pxel x respectvely). b Let Bs (nt)(255 ) (1) r + g + b (nt) s the nteger operator, whch transfors float nuber nto ts nearest nteger. Bs deonstrates the porton occuped by blue channel. Bs can fnely represent the dfferences between speces, and the pact of llunaton to Bs s very sall. In the blue chroa hstogra, there area usually three peas correspondng to the three speces. Aong the, the pea correspondng to sea-water area s n the hgher part, and s relatvely further fro the other two. We hereby eploy the hstogra concave analyss to allocate the concave pont of the pea and thus get the adaptve threshold for sea-water area segentaton. (a) hstogra analyss (b) orgnal pcture (c)result Fg2 sea-water area segentaton 2.2 FCM clusterng After thresholdng, the bluest area (usually corresponds to sea water area) there left soe and roc area n the age. Snce the roc area s usually ult-colored and the soe area dffers wth ts coponent and thcness, t s hard to acheve good segentaton by thresholdng. Ths paper, fro the angle of statstcal pattern recognton, puts forward a segentaton algorth basng on FCM clusterng. Fuzzy c-eans algorth (FCM) s one of the ost wdely used age fuzzy clusterng algorths. Its effectveness attrbutes not only to the ntroducton of fuzzness for belongngness of each pxel but also to explotaton of spatal contextual nforaton. It consders each pxel as a feature vector n the feature space, and the age as a set of these feature vectors. Each feature vector ay be assgned to ultple clusters wth soe degree of certanty easured by the ebershp functon. onsder the set X fored by n feature vectors fro an t-densonal Eucldean space, that X { x1, x2,..., xt}, x R 1,2,... n. The clusterng process s realzed by seeng for the local nu of the ean square devaton of equaton (2): n c 2 JUV (, ) ( u ) x v (2) 1 1

3 s a weght paraeter, and u s the ebershp of x, representng the degree of certanty t should be assgned to the fuzzy set centered by v.u can be obtaned by teratve calculaton, and the local nu can be calculated by the followng two c s the nuber of clusters n the age; [ 1, ] equatons when >1 and x u v v : 2/( 1) 1 c x v, j 1 x vj n 1 n ( u ) 1 ( u ) x (3) Feature Extracton Although the general blue chroa of soe area s hgher than that of roc, snce ths two speces gather on the lower part, t s hard to dvde the (See Fg3). In order to enhance the separablty, we stretch the hstogra fro the lower part to the[0-255]. (a) (b) Fg3 blue-chroa hstogra enhancng The hstogra stretch, on one hand, enhances the separablty between speces, and weaens the closeness wthn speces on the other. For those soe whch has slar color wth roc area, t s stll hard to acheve good segentaton only by blue-chroa. Therefore, grey nforaton s also ntroduced n FCM clusterng n order to fully utlze the color nforaton. As can be seen, the grey feature of undersea hydrotheral vent age confors a certan order wth the roc area beng the hghest followed by soe area and then sea-water area. The result of FCM clusterng represents the coprose of blue-chroa and grey level nforaton, and therefore s expected to produce better perforance Estaton of Intal Cluster Center The ntal cluster center of FCM algorth needs to be set before hand, and ts precson has portant (4) nfluence on the precson of both the fnal result and the teraton nuber as well. As the dynac range of blue chroa s extended by hstogra stretch, t s hard to set a fx cluster center. We hereby use OTSU thresholds to estate the ntal cluster center. By OTSU, We can calculate the thresholds separatng soe and roc spece. Assue the thresholds for grey and blue-chroa hstrgra are S1 and S2 respectvely. Then the ntal clusters centers n feature space can be estated as: Soe:((S2+255)/2, S1/2,); Roc:(S2/2, (S1+255)/2); (5) Fg4 deonstrate the relatonshp between the cluster centers and the thresholds: Fg4 Feature Space Snce the dstrbuton of grey and blue chroa features confor to fxed rules, the soe area wll be gathered n class 1 by FCM teraton, whle the roc area to class 2. Thus, the algorth can not only dvde the two speces, but also classfy the segentaton result at the sae te. In other words, the algorth has a pre-recognton ablty Acceleraton of Algorth As shown n Fg4, The orgnal age, no atter how large t s, s apped nto the 256*256 2-D feature space. On one hand, as ost of the pxels gather around the cluster center, the 2-D feature hstogra s actually a sparse atrx. On the other hand, those pxels on the sae pont n the feature space have exactly the sae features, and therefore should have the sae ebershp values. Basng on the above analyss, we eploy the feature space searchng polcy, and calculate the ebershp for the group of pxels on the sae pont n feature space unforly. Thus, the equaton (3)and (4) for the teratve process are transfored nto: u c x x v j 1 j v 2/( 1) 1 [1, c], [0, ] (5) v *255 0 hst( ) ( u ) x hst( ) ( u ) (6)

4 hst( ) s the statstcal 2-D feature hstogra, representng the nuber of pxels on the th pont n 2-D feature space; x represents the coordnate vector of the th pont n 2-D feature space; u represents the degree of certanty of the th pont n 2-D feature space beng assgn to the cluster centered by v. The above teratve process s exactly equvalent to the orgnal teratve process, and the coputatonal coplexty has been draatcally reduced. 3 The Experent Result and Dscusson A set of experents s carred out n order to test the effectveness and the speed of the algorth. 3.1 The Experent Result The coparson experent s carred out between our algorth and the OTSU based algorth on a group of natural under sea hydrotheral vent ages. We fuse the grey and blue chroa feature by addng the values up. The result feature s used as the nput, and the coparson experent s pleented wth a double-threshold-otsu algorth and the algorth, whch pleent OTSU thresholdng for two tes, respectvely. Fg4 shows a group of undersea hydrotheral vent ages and the correspondng results: (a1) orgnal age (a2) ethod 1 (a3) ethod 2 (a4) our algorth (b1) orgnal age (b2) ethod 1 (b3) ethod 2 (b4) our algorth (c1) orgnal age (c2) ethod 1 (c3) ethod 2 (c4) our algorth (d1) orgnal age (d2) ethod 1 (d3) ethod 2 (d4) our algorth Fg5 experent result (The soe area s labeled wth whte r n orgnal age. The double-threshold-otsu algorth s labeled as ethod 1and the algorth pleents OTSU thresholdng for two tes s labeled as ethod 2) As can be seen, on one hand, the ntegralty of soe area of our algorth s evdently better than the other two algorths. On the other hand, the area slablled as soe area of our algorth s relatvely sall. The prncple of OTSU algorth s to axze the ean square varance between speces. Basng on the nforaton of one denson hstogra, ts perforance s nfluenced by the area raton of the two speces. The ore equvalent of ther szes s, and the larger the devaton between the, the better the threshold perforance wll be. Whle n undersea hydrotheral vent age, the soe area s usually sall, the algorth basng on OTSU thresholds can not gve good perforance. However, FCM clusterng suffers slghtly by the area raton of two speces. Its effectveness s guaranteed by the ntroducton of fuzzness for belongngness of each pxel and the explotaton of spatal contextual. The followng table shows the speed of three ethods. Pcture Method Method Our Method Table 1 The processng speed (unt second) As can be seen the general speed of our algorth s better than double-threshold OTSU whle a bt slower than the algorth pleentng OTSU for two te. Both the sea-water area segentaton and ntal cluster center estaton are basng on 1-D hstogra and therefore s not te-consung. The

5 ntal cluster center, estated by OTSU thresholds, s n well accordance wth the real center, so only a few teraton s enough before the stablzaton of clusterng. Further ore, the acceleraton of teraton reduce the coputatonal coplexty draatcally. Thus, the speed of the algorth s well guaranteed. In order to obtan ore accurate segentaton for soe area, soe sple post-processng steps can be eployed: frstly, erode the result bnary age to separate the an soe area fro other sall fragents; secondly, select the bggest connected coponent and wpe off all the others; thrd, dlate the bnary age. Ths post-processng can delete the fragents n age, whle preserve the contour of the an soe area (see Fg 6). (a)orgnal bnary age (b) the result age Fg6 post-processng To test the precson, we ae a group of test pctures. We frst cut out the soe area by hand and calculate ts sze. Then, wpe off the reanng soe area by PHOTOSHOP tools. Fnally, paste the soe area cut out bac (See Fg7). (a) orgnal pcture (b)the area cut out (c)the test pcture Fg 7 How to ae a test pcture The soe area n test pcture s then equal to the soe area we cut out before hand. So, We can test the precson of the algorth by coparng the actual soe area wth the segented soe area. (see Fg 8). (a)test pcture (b)segent result (c)fnal result Fg8 post processng The experent was carred out on a group of 10 test ages. The result s shown n the followng table. Pc Actual area Segent Precson (pxels) Area (pxels) Although, the erode and dlate operaton result n slght change n the contour of the area, the fnal result s stll n well accordance wth the actual soe area. Snce we use a fxed erode the dlate as, the nfluence of orphology operaton s relatvely bgger for sall soe area. The precson s expected to be proved by changng the sze of the as accordng to that of the soe area. 4 Concluson Tang nto account the dffculty n the segentaton of undersea hydrotheral vent age, whch s dversfed and lac of notable features, ths paper, basng on the strategy of solvng the proble step by step, puts for- ward a segentaton algorth usng grey and blue chroa features. As the grey and blue chroa featu- res can fnely deonstrate the dfferences between each speces, the result of FCM clusterng s n well accordance wth the orgnal objects. Furtherore, the clusterng process classfes the objects autoat- cally and therefore can be used as a pre-recognton technology. Experents show that ths algorth gves good segentaton result and has good proce- ssng speed and stablty as well. 5 Acnowledgeent The author would le to than the Chna Natonal Scence Foundaton( ) for fnancal supportng and would also than NOAA s research progra for provdng the agery References: [1]: Songcan Chen; Daoqang Zhang Robust age segentaton usng FCM wth spatal constrants based on new ernel-nduced dstance easure Systes, Man and Cybernetcs, Part B, IEEE Transactons on, Volue: 34, Issue: 4, Aug [2]: Wang, S.; Ssnd, J.M Iage segentaton wth rato cut Pattern Analyss and Machne Intellgence, IEEE Transactons on, Volue: 25, Issue: 6, June 2003 [3]: Zeyun Yu; Bajaj, C. Iage segentaton usng gradent vector dffuson and regon ergng Pattern Recognton, Proceedngs. 16th Internatonal Conference on, Volue: 2, Aug. 2002

6 [4]:Wang Pezhen Chen wenan Iage Fast Segentaon Usng 2-D Thresholdng and FCM Journal of Iage and Graphcs.Sep 1998 Vol3. No9 [2]: Han Sq, Wang Le A Survey of Thresholdng Methods for Iage Segentaon Syste Engneerng ad Electroncs. Vol.24, No 6, [3]:Lang Guangn Iproveent of A Two-denson Adaptve Thresholdng Segentaton Algorth Coputer Applcaton Vol 21,No5,2002 [4]:Zheng Hual Characterstc Analyss for FCM Algorth n Iage Segentaton and Its Iproveent Coputer Engneerng Mar 2004 [5]:Lu Guansong A New Algorth for Connected Coponent Labelng n Bnary Iages Coputer Engneerng and Applcaton