An adaptive approach to small object segmentation

Transcription

1 An adapve approach o small ojec segmenaon Shen ngzh ang Le Dep. of Elecronc Engneerng ejng Insue of echnology ejng 8 Chna Asrac-An adapve approach o small ojec segmenaon ased on Genec Algorhms s proposed. A new parameer scale of he sujec area s percenage s nroduced n hs mehod whch can overcome he -le mehod s defec of requrng he exac percenage of an ojec area and meanwhle makes effecve use of he small ojec s characer. Genec Algorhm forms he skeleon of he new approach whch can dynamcally locae he opcal hreshold n he search space. he proposed algorhm can e exended o segmen hose mages wh ojec of arrary sze y smply changng he se of he new parameer. Expermen resuls ndcae ha he proposed algorhm performs eer segmenaon qualy and akes less compuaonal me han convenonal Osu mehod. Key words-genec Algorhm; mage segmenaon; enropc mehod; fness funcon Ⅰ. INRODUCION Image segmenaon s an old and dffcul prolem where one s neresed n denfyng he dfferen homogeneous componens. Varous approaches have een presened o hs prolem u n he cases of segmenng small ojec effecve echnques sll reman o e proposed. hs work aemps o presen an adapve segmenaon mehod o small ojec ased on Genec Algorhm. he work s organzed as follows: Secon Ⅱ conans a ref ackground of mage segmenaon and hreshold mehod. In Secon Ⅲ we refly revew some radonal segmenaon mehods referred n hs paper. A relavely new segmenaon mehod called enropc mehod s provded n Secon Ⅳ. Secon Ⅴ descres he proposed mehod n deal. In Secon Ⅵ expermenal resuls are gven o demonsrae he effecveness of he new approach. Fnally n Secon Ⅶ we presen some concludng remarks aou our work. Ⅱ. OVERVIE OF IMAGE SEGMENAION Segmenaon s a process of paronng he mage no some non-nersecng regons such ha each regon s homogeneous and he unon of no wo adjacen regons s homogeneous []. Segmenaon s he frs essenal and mporan sep of any auomaed mage undersandng process and also remans an old and dffcul prolem. All susequen nerpreaon asks ncludng ojec deecon feaure exracon ojec recognon and classfcaon rely heavly on he qualy of he segmenaon process. Despe a large numer of segmenaon echnques have een presened no sngle mehod can e consdered good for all mages nor are all mehods equally good for a parcular ype of mage []. hresholdng s a wdely used ool n mage segmenaon where one s neresed n denfyng he dfferen homogeneous componens of he mage [3]. ecause of s smplcy s wdely used especally n hardware where compuaonal me s a major consderaon. Le e he pxel numers n he hegh drecon and M x y e he pxel numers n he hegh drecon. hus e he oal numers of a dgzed gray-level mage e he spaal coordnae of he mage and G { L } e a se of posve negers represenng gray levels y convenon he gray level s he darkes and he gray level L s he lghes. hen an mage funcon can e defned as he f : G x y mappng. he rghness.e. gray level of a pxel wh coordnae s denoed as f x y.

2 Le e a hreshold and G { } e a par of nary gray level and. he resul of hresholdng an mage funcon a gray level s a nary mage funcon such ha G f f : < y x f y x f y x f In general a hresholdng mehod s one ha deermnes he opcal value ased on a ceran creron. Le he numer of pxels wh gray level e. hen he oal numer of pxels n a gven mage s m L m M he proaly of occurrence of gray level s defned as M m p Ⅲ. CONVERIONAL IMAGE SEGMENAION MEODS A. Osu Mehod A mehod presened y Osu parons he pxels of an mage no wo classes and a gray level whch correspond o he ojec and ackground. Le e he whn-class varance nerclass varance and oal varance respecvely. he segmenaon qualy s examned y he followng crerons wh respec o : C C η κ λ where L p p p

3 he scheme selecs an opmal hreshold so ha he nerclass varance eween he ojec and ackground regons s maxmzed. Arg Max G he orgnal formulaon of he prolem y Osu suggess an exhausve search whch s compuaonally expensve. hus search echnques wh hgh speed o locae he opmal hreshold are expeced. Generally he Osu mehod performs eer resul han oher mehods. u n he cases of segmenng small ojec fals o gve sasfacory resuls [4].. -le Mehod [5] -le mehod s one of he earles hresholdng mehods ased on analyss of he mage s gray level hsogram. I assumes he ojecs n an mage are darker.e. have hgher gray level han he ackground and occupy a fxed percenage of he pcure area whch s %. he hreshold s defned as he gray level ha mosly corresponds o mappng a leas % of he gray level no he ojec. hs mehod s smple and suale for all szes of ojecs. I can yeld good annose capales u ovously s no applcale f he ojec area s unknown or vared from pcure o pcure. esdes he sasc propered refleced n he hsogram he -le mehod also ulzes he ojec s percenage of he pcure area whch assures s relavely sasfacory segmenaon resuls. Ⅳ. ENROIC MEOD he frs enropy ased prncple was proposed y un [6][7]. y correcng some mahemacal errors n he mehod of un J. N. kapur e al. proposed a new maxmum enropy prncple for mage hresholdng [8]. In he enropy mehod a creron funcon y applyng nformaon heory s defned and maxmzed o choose he opmal hreshold. he creron selecs a pror proaly dsruons when very lle or no nformaon s known. Le e he hreshold and proales correspondng o he ojec and ackground are O : p p p p p pl : Class enropes are hus defned as: O ln ln L here p L p ln p p ln p L ψ : he nformaon eween he wo classes proposed y J. N. kapur e al. s defned and rewren as a funcon 3

4 ψ O ln L he opmal hreshold s derved from maxmzng ψ : Arg Maxψ G Ⅴ. A new mehod of small ojec segmenaon ased on Genec Algorhm he Genec Algorhm s classfed as an effcen random search evoluon algorhm ha uses proaly o gude s search. Genec Algorhm forms he skeleon of he new fas algorhm whch can dynamcally locae he opcal hreshold n he search space. hus he Osu mehod s drawack of eng me consumng s overcome. y nroducng he new parameer scale of he sujec area s percenage he presened algorhm only requres an approxmae scale of he ojec area s percenage hus overcomes -le mehod s defec of requrng he exac percenage of an ojec and meanwhle makes effecve use of he small ojec s characer. he els sraegy s employed n he paper o proec he es ndvdual from eng dsruped y copyng no he succeedng generaon. here are wo major consderaons n praccal applcaon of GA: one s how o encode he ndvduals so as o solve he opmzaon prolem. he oher one s how o choose proper fness funcon. A. codng of ndvduals he ojec area s percenage s chosen o e each generaon s ndvdual [9]. Snce s doman s from o he chromosomes ndvduals are encoded as 7 s srngs. Le he mnmal possle percenage e percen_mn and he maxmal possle percenage e percen_max. hus he doman of he ndvdual s [percen_mn percen_max]. In hs way a new parameer called scale of he sujec area s percenage s oaned.. fness funcon Research ndcaes ha enropc mehod proposed y J. N. kapur e al. s consdered superor o oher enropy hresholdng algorhms n mage segmenaon []. he presened mehod uses J. N. kapur s enropc creron funcon ψ as Genec Algorhm s fness funcon o acheve he segmenaon of small ojec. f ψ ln L C. GA s parameers In hs approach he populaon sze s 3 he crossover proaly s.8 he muaon proaly s. predeermned numer of generaons s 4. he ermnaon condon s ha when he opmal hreshold of each generaon remans same for generaons or he GA has een mplemened for predeermned mes of generaons n our case 4 mes. Snce expermenal resul s possly dfferen each me due o GA s convergence aly we ake he average value of hreshold and me as he fnal resuls y performng he expermen repeaedly n our case mes each me we change percen_mn and percen_max. An asrac procedure of he Genec Algorhms s gven elow where soluons o a gven prolem a generaon : k Nk generae an nal populaon. k Nk s a populaon of canddae 4

5 Compue populaon Nk o ge fness of each ndvdual. 3 erform he GA s reproducon crossover and muaon usng he aove parameer seng. 4 Fnsh he GA and ge he fnal opmal hreshold f ermnaon condon s fulflled else reurn o. o oan he sasfacory segmenaon one mus make he es use of known nformaon. Small ojec s useful nformaon self whch ndcaes ha he ojec area s percenage s relavely small. Convenonal -le mehod requres he exac percenage of he ojec whch lms from eng appled o real small ojec s segmenaon suaons. he smaller he parameer of percen_mn s or he larger he parameer of percen_max s he less nformaon known n advance s. hen he parameer seng of percen_mn comes o e and percen_max comes o e he presened algorhm ecomes regular Genec Algorhm whou knowng any nformaon aou he small ojec. In hs case we can only smply save compuaonal me wh yeldng he same resul as Osu mehod. he algorhm proposed here has several advanages: Genec Algorhm forms he skeleon of he proposed algorhm whch provdes s capaly o locae he opcal hreshold n he search space dynamcally and quckly. Users only need o pon ou how much nformaon hey know aou he small ojec so far. y dong hs we can no only make he es use of he curren nformaon u also overcome he drawack of convenonal -le mehod of requrng he exac percenage of he small ojec. 3he proposed algorhm can e exended o segmen hose mages wh ojec of arrary sze y smply changng he seng of he nroduced parameer. 4Anoher advanage of he presened algorhm s he flexly of he nroduced parameer scale of he sujec area s percenage whch can e se o dfferen values so as o yeld dfferen resuls when lle nformaon aou he small ojec s known. hose resuls can e furher analyzed y daa fuson echnque. On he oher hand he proposed algorhm can e exended o segmen hose mages wh ojec of arrary sze y smply changng he seng of he nroduced parameer. Ⅵ. EXERIMENS AND RESULS A. Expermenal Resuls a 5

6 c d e f FIG.. gray-level mage of OA : a orgnal mage; Osu mehod hreshold4; c AM percen_mn percen_max5 hreshold97.36; d AM percen_mn percen_max3 hreshold95.98; e AM percen_mn percen_max hreshold9.36; f AM percen_mn percen_max5 hreshold9.64; ALE COMARISON OF DIFFEREN ARAMEERS FOR OA IMAGE percen_mn percen_max hreshold me Osu Mehod AM AM AM AM Analyses o expermenal resuls In he aove expermens percen_mn s whch means he mnmum proporon of he ojec s percens. he oher parameer percen_max s 5 3 and 5 n each expermen whch means he maxmum proporon of he ojec s 5 3 and 5 percens. he resuls show ha he smaller percen_max s he smaller he hreshold s and he less compuaonal me s. In fac he value of percen_max means he pror knowledge aou he ojec s sze. he smaller percen_max 6

7 s he more we know aou he small ojec s sze and hus make a eer use of small ojec s nformaon so as o save compuaona7l me. Ⅶ. CONCLUSION In hs paper we have developed a mehod ha employs Genec Algorhm and enropy mehod. y nroducng a new parameer scale of he sujec area s percenage he proposed mehod overcomes -le mehod s drawack of requrng he exac percenage of an ojec area. ased on expermenal resuls he scheme works eer han Osu mehod oh n erms of speed and segmenaon capales. REFERENCES [] N. R. al S. K. al. A Revew on Image Segmenaon echnques. aern Recognon :77~94 []. hanu S. Lee and J. Mng. Adapve mage segmenaon usng a Genec Algorhm. IEEE ransacons On Sysems Man And Cyernecs 995 5: 543~565 [3] K. S. Fu and J. K. Mu A survey on mage segmenaon aern Recognon 98 3: 3~6 [4] S. U. Lee S. Y. Chung and R.. ark. A Comparave performance sudy of several gloal hresholdng echnques for segmenaon. Compuer Vson Graphcs and Image rocessng 99 5:7~9 [5].Doyle Operaon useful for smlary-nvaran paern recognon J. Assoc. Compu. Mach 96 9: 59~67 [6]. un. A new mehod for gray-level pcure hresholdng usng he enropy of he hsgram. Sgnal rocess 98 :3~37 [7]. un. Enropc hresholdng: A new approach. Compu. Vson Graphcs Image rocess 98 6: ~39 [8] J. N. kapur. K. Sahoo and A. K. C. ong. A new mehod for grey-level pcure hresholdng usng he enropy of he hsogram. Compu. Vson Graphcs Image rocess 985 9: [9] ou Gexan u Chengke. An adapve ojec segmenaon mehod ased on genec algorhms and mnmum error : 7~3 [] J. N. Kapur. Maxmum enropy models n scence and engneerng. ley Easern New Delh 989 7