PROF. J. K. PATIL. Engineering, Kolhapur, India,

Transcription

1 DETECTION AND REMOVAL OF SHADOW USING OJECT ORIENTED TECHNIQUE MISS. SUJATA. KALE Department of Electroncs and Telecommuncaton Engneerng, harat Vdyapeeth s College of Engneerng, Kolhapur, Inda, sujatakale92@gmal.com PROF. J. K. PATIL Department of Electroncs and Telecommuncaton Engneerng, harat Vdyapeeth s College of Engneerng, Kolhapur, Inda, jayamala.p@redffmal.com ASTRACT In hgh resoluton mages, there s loss of nformaton because of tradtonal pxel level shadow detecton method. Here present object orented technque whch can automatcally detect and remove shadows from satellte mages. In ths method, for mage segmentaton mage parameters are used. Threshold values are used for the separaton of shadow regon. From ths shadow wll be detected. Some dark objects are known as false shadows. These false shadows are rules out accordng to object propertes and spatal relatonshp between objects. For shadow removal morphologcal operaton nner outer outlne profle lne (IOOPL) s used. Usng homogeneous secton over each object shadow wll be removed. Homogeneous sectons are attaned through IOOPL smlarty matchng. Ths s effectve method foe detecton and removal shadow from urban hgh resoluton remote sensng mages. KEYWORDS: Change detecton, mage segmentaton, nner-outer outlne profle lne (IOOPL), objectorented, shadow detecton, shadow removal. INTRODUCTION For the percepton of earth and ther flyng advancement, recently hgh resoluton remote sensng data opened new tme n remote sensng feld, varous remote sensng satelltes lke IKONOS, Quck-rd, GEO- Eye, RESOURCE 3 and so on. As a result t s necessary to process these remote detectng pctures. ut there s the presence of shadows whch are few downsdes of these hgh determnaton pctures. In urban terrtores lke huge surface lke brdges, buldngs, towers, etc shadows are complex. Also shadows themselves become some type of nformaton n the process of 3-D reconstructon, buldng poston recognton, and also n heght estmaton. These shadows gve undesred data and thus can create ssues for clent. So to overcome these problems shadow detecton and removal s mportant thng.[1] There are many effectve algorthms are present for detecton and removal of shadow. Exstng shadow detecton methods are classfed nto two types 1) model based and 2) feature based. Model based method uses pror nformaton lke camera poston, scene, movng targets [2][4]. Feature based method uses nformaton lke grayscale, saturaton, brghtness, and texture. Also mprovement of these two proposed methods becomes mproves calculaton [5]. Color space nformaton and automatc threshold method(otsu s technque) are some strateges to dentfy shadows [7]. To dstngush shadows and also to determne a shadow free pcture by mrrorng some presumptons the llumnant nvarance model can be utlzed [13]. For shadow dscovery for example HSV, YIQ, HCV mages wth dverse color spaces can be utlzed [7]. In the process of shadow detecton some dark objects can be consdered as shadows that s false shadow. These false shadows should be ruled out. Pxel-level technque gves nsuffcent results. So there s new technque s proposed known as object orented technque. It gves effectve and suffcent result. Here, Convexty Model (CM) based segmentaton s done. Then shadow wll detected by thresholdng and false shadow wll be elmnates. After that boundary extracton wll be done. Then usng IOOPL shadow wll be removed. PROPOSED METHODOLOGY Detecton and removal of shadow from mages s dvded nto two phases:- A. Shadow Detecton. Shadow Removal 114 P a g e

2 A. SHADOW DETECTION Shadows are made when lght source s blocked by an object. Then behnd that object 3-Dmensonal volume s formed. Fgure 1:- Prncple of Shadow Formaton Manly shadows are dvded nto two parts:- 1) Self shadow, 2) Cast shadow. The shadow on a subject on the sde that s not drectly facng the lght source s known as self-shadow. The cast shadow s defned as the shadow of a subject fallng on the source of another subject because the former subject has blocked the lght source. The cost shadow s agan dvded nto two parts:- 1)umbra, 2)penumbra. When lght has been drectly blocked by object then shadow created that shadow known as umbra, whle penumbra shadow s created when somethng partly blocks the drect lght. As shown n fg 1. In ths paper the algorthm s manly focused on cast shadow area of the remote sensng mages. The block dagram of shadow detecton s shown n the followng fg. 2 Orgnal Image Segmentaton Detecton of Suspected Shadow Image wth true shadow Elmnaton of False Shadow Fgure 2:- lock Dagram of Shadow Detecton 1) Image Segmentaton Consderng Shadow Features: Hgher resoluton mages contan hgh spatal nformaton. As they carry the large amount of data, t s hard to process on them. Pxel based methods are not suffcent for these mages as they may take excessve tme for processng these mages. Image segmentaton s needed for the use of spatal nformaton to detect shadows. Tradtonal mage segmentaton gves nsuffcent result, whch makes t dffcult to separate shadows from dark objects. Convexty Model (CM) constrants for segmentaton can mprove the stuaton to certan degree. CM based segmentaton along wth color shape and shape factor s used whch dstngush between shadows and dark objects. The parameters of each object wll be recorded lke grayscale average, varance, area and permeter. Ths segmentaton gves better result and t wll be less tme consumng result. 2) Detecton of suspected shadow area: Thresholdng methods are used for the separaton of shadow nonshadow regons. Accordng to hstogram values of mages threshold s obtaned. Threshold s obtaned by the neghborhood values of mean of two peaks. Suspected shadow can be detected by comparng grayscale value estmaton of each object and threshold by usng followng equaton (1) and (2) Gq = 2 1 (Gm + Gs) (1) h(t) = Mn {h(gq- ), h(gq+ )} (2) Where, Gm- Average grayscale value of an mage Gs - Left peak of the shadow n the hstogram 115 P a g e

3 T - Threshold, where T [Gq-, Gq+ ] - Neghborhood of T h(i)- Frequency of I, where I = 0, 1,, 255. In the wake of drectng expansve number of examnatons, t has found that to supplant the rght peak, the normal of grayscale qualtes are utlzed. To rearrange ths operaton Gs.e. left peak of the shadow n the hstogram can be supplanted by half of the grayscale normal, when the left peak s not requred. To mantan a strategc dstance from the mpact of anomalous data, a few pxels on left and rght sdes of hstogram are excluded. For a smlar queston, when n the shadow and non-shadow range, ther grayscale dstncton at the red and green wavebands s more dscernble than at the blue waveband. In ths manner, t recover a suspected shadow utlzng the threshold method at the red and green wavebands. 3) Elmnaton of False Shadow: Some dark objects for example vegetaton can be consdered as shadows, so these false shadows should be ruled out. Hence to remove these false shadows spatal nformaton s utlzed. Accordng to Raylegh scatterng, as compare to blue waveband grayscale dfference at red and green waveband s more notceable. Hence ts grayscale average at blue s more notceable as compared to red and green. Green vegetaton can become a false shadow. Accordng to varous propertes of green vegetaton Gg s greater than Gb. Hence for object, f Gb> Gg, then can be consdered as vegetaton and t can be ruled out.. SHADOW REMOVAL In order to remove shadow from mage shadow removal method s used. The block dagram of shadow removal s shown n followng Fg. 3. Image wth True Shadow oundary Extracton True Shadow Removal Recovered Image Fgure 3:- lock Dagram of Shadow Removal 1) oundary Extracton: Shadow removal method s based on IOOPL matchng. Accordng to nner and outer pxels of mage boundary the nner and outer IOOPL lnes are obtaned. The dagram of shadow boundary, nner and outer outlnes lnes s shown n followng fg. 4. Fgure 4:- Dagram of shadow boundary, nner, and outer outlne lnes. As shown n fg 4. R s the vector lne of shadow boundary obtaned by shadow detecton. Usng morphologcal operaton nner and outer profle lnes are obtaned. R1 s the outer outlne obtaned by expandng R outward. R2 s the nner outlne obtaned by contractng R nward. If R1 and R2 are close then t s probablty that they belong to same object. Therefore, outer profle lnes belong to non-shadow area and nner profle lnes belong to shadow area 116 P a g e

4 2) True Shadow Removal: For true shadow removal nner-outer outlne profle lne (IOOPL) matchng s used. When correlaton between both outlnes.e. nner and outer outlnes are nearly close, then there s large possblty that ths locaton belongs to same type of object. To obtan nner outer profle lne (IOOPL) the grayscale value of correspondng nodes along nner and outer outlne at each waveband wll be collected. To recover shadow areas n an mage IOOPL matchng s used. To rule out nonhomogeneous secton IOOPL wll be dvde nto average sectons wth same standard. Then smlarty of each lne par wll be calculated. For the calculaton of smlarty followng equaton 3 s used. In followng equaton smlarty set A and set s expressed. (A, ) = n 1 ( c A c n n A A 2 ( c c ) 1 1 A )( c c ( c ) c ) 2 (3) Where, A= Curve representng one set = Curve representng another set c = Grayscale of node on curve X x x c = Grayscale average of all nodes on curve X If correlaton coeffcent s large n IOOPL matchng the IOOPL lne par belongs to same type of object and then t wll be consdered to be matchng. If correlaton coeffcent s small n IOOPL matchng then some dfferent types of objects exsts n ths secton. So these parts wll be removed.[1] True shadows are removed usng homogeneous sectons obtaned by lne par matchng. For shadow removal there are two approaches. Frst approach s relatve radaton correcton whch calculates the radaton parameter accordng to homogeneous ponts to each object. It s good at restorng the contrast between background and objects. Second approach s polynomal fttng whch collects and analyze all homogeneous secton and t retreves all shadows drectly wth obtaned fttng parameters. It s good at restorng all of mage radant nformaton. RESULT AND DISCUSSION The results of ths methodology are shown through mages. For ths process nput mage has taken shown n fg. 5. Ths mage cab be any color mage or grayscale mage. If color mage s taken as an nput mage then t can be converted nto grayscale mage as shown n fg. 6. Fgure 5:- Input Image Fgure 6:- Input Grayscale Image 117 P a g e

5 From the segmentaton result shown n fg. 7, t can be seen that segmentaton that consders shadow and dark objects. Then shadow area s detected through threshold method shown n fg. 8. After that suspected shadow s detected and false shadow s elmnated from mage as shown n fg.9. After elmnaton false shadow, nner and outer lne generaton for shadow removal. The nner outer outlne profle lne (IOOPL) graph generaton s shown n fg 10. Wth the help of RRN (Relatve Radometrc Normalzaton) shadow s removed. The output mage.e. recovered mage s shown n fg. 11. Fgure 7:- Segmented Image Fgure 8:- Shadow detecton Image Fgure 9:- Suspected Shadow Detected and False Shadow Elmnated Image Fgure 10:- IOOPL Graph Generaton Fgure 11:- Shadow Removed Image 118 P a g e

6 CONCLUSION Shadow detecton technque s proposed for stable and accurate dentfcaton of shadows. For shadow detecton segmentaton of an mage s done. After that shadow wll be detected by thresholdng. Wth the full use of spatal nformaton of mage shadow detecton s done usng object-orented shadow detecton method. After that boundary extracton s done for shadow removal process. Next, for shadow removal IOOPL matchng s used. From ths shadow free mage wll be obtaned. REFERENCE 1) Hongya Zhang, Kamn Sun, and Wenzhuo L Object-Orented Shadow Detecton and Removal From Urban Hgh-Resoluton Remote Sensng Images, IEEE Transacton. Geoscence. Remote Sensng, vol. 52, nos. 11, Nov ) J. Yoon, C. Koch, and T. J. Ells, Shadow Flash: An approach for shadow removal n an actve llumnaton envronment, Proc.13th MVC,Cardff,U.K.,Sep.2 5, 2002, pp ) P. Saraband, F. Yamazak, M. Matsuoka Shadow detecton and radometrc restoraton n satellte hgh resoluton mages, Proc. IEEE IGARSS, Sep. 2004, vol. 6, pp ) R.. Irvn and D. M. McKeown, Jr, Methods for explotng the relatonshp between buldngs and ther shadows n aeral magery, IEEE Trans. Syst., Man, Cybern., vol. 19, no. 6, pp , Dec ) Y. L, T. Sasagawa, and P. Gong, A system of the shadow hngdetecton and shadow removal for hgh resoluton cty aeral photo, Proc. ISPRS Congr, Comm., 2004, vol. 35, pp , Part 3. 6) P.M. Dare, Shadow analyss n hgh-resoluton satellte magery of urban areas, Photogramm.Eng. Remote Sens., vol. 71, no. 2, pp , ) V. J. D. Tsa, A comparatve study on shadow compensaton of color aeral mages n nvarant color models, IEEE Trans. Geosc. Remote Sens., vol. 44, no. 6, pp , Jun ) T. Km, T. Javzandulam, and T.-Y. Lee, Semautomatc reconstructon of buldng heght and footprnts from sngle satellte mages, Proc.IGARSS, Jul. 2007, vol. 2, pp ) K.-L. Chung, Y.-R. Ln, and Y.-H. Huang, Effcent shadow detecton of color aeral mages based on successve thresholdng scheme, IEEE Trans. Geosc. Remote Sens., vol. 47, no. 2, pp , Feb ) D. Ca, M. L, Z. ao et al., Study on shadow detecton method on hgh resoluton remote sensng mage based on HIS space transformaton and NDVI ndex, Proc. 18th Int. Conf. Geonformat., Jun. 2010, pp ) A. Makarau, R. Rchter, R. Muller et al., Adaptve shadow detecton usng a blackbody radator model, IEEE Trans. Geosc. Remote Sens., vol. 49, no. 6, pp , ) E. Salvador, A. Cavallaro, and T. Ebrahm, Shadow dentfcaton and classfcaton usng nvarant color models, n Proc. IEEE Int. Conf. Acoust., Speech, Sgnal Process., 2001, vol. 3, pp P a g e

7 13) G. Fnlayson, S. Hordley, and M. Drew, Removng shadows from mages, Proc. ECCV, May 28 31, 2002, pp , Vson-Part IV. 14) R. Hghnam and M. rady, Model-based mage enhancement of far nfrared mages, IEEE Trans Pattern Anal. Mach. Intell., vol. 19, no. 4, pp , Apr P a g e