AUTOMATIC IMAGE REGISTRATION OF MULTI-ANGLE IMAGERY FOR CHRIS/PROBA

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1 AUTOMATIC IMAGE REGISTRATION OF MULTI-ANGLE IMAGERY FOR CHRIS/PROBA J. Ma *, J.C.-W. Chan, F. Canters Cartography and GIS Research Group, Department of Geography, Vrje Unverstet Brussel, Plenlaan, 050 Brussels, Belgum (Jangln.Ma, Cheung.Wa.Chan, KEY WORDS: CHRIS/Proba, Automatc Regstraton, SIFT, Normalzed Cross-correlaton, Thn Plate Splne ABSTRACT: Subpxel mage regstraton s the key to successful mult-angle remote sensng mage applcatons such as mage fuson, superresoluton and classfcaton. However, mult-angle remote sensng mages pose some dffcultes for automatc mage regstraton, namely, ) precsely locatng control ponts (CPs) s dffcult as large vew angle mages are susceptble to resoluton change and blurrng; and ) local geometrc dstorton caused by varatons n platform stablty makes rgd transform models such as the projectve model unrelable. In ths paper we propose a two-stage automatc regstraton scheme for mult-angle remote sensng magery. In the frst step, CPs are gathered va the scale nvarant feature transform (SIFT). However, CPs collected by SIFT may be too few or unevenly dstrbuted. Therefore, another CPs collectng procedure based on normalzed cross-correlaton follows. In order to elmnate outlers n the CPs a geometrc constrant s utlzed; after outler elmnaton n order to get CPs of hgh accuracy for the estmaton of the thn plate splne model, whch s used to solve the local geometrc dstorton problem, a pre-fttng procedure s adopted. The methodology developed n ths paper s appled to three Compact Hgh Resoluton Imagng Spectrometer onboard the Project for On-board Autonomy (CHRIS/Proba) mages. Expermental results demonstrate the effcency and accuracy of the proposed method.. INTRODUCTION Many recently avalable remote sensng magng systems are equpped wth mult-angle functons for a better understandng of the earth s surface character. These nclude the Multspectral Thermal Imager (MTI), the Mult-angle Imagng Spectro- Radometer (MISR), the Along Track Scannng Radometers (ATSR-, ATSR-, AATSR), and the Compact Hgh Resoluton Imagng Spectrometer onboard the Project for Onboard Autonomy (CHRIS/Proba). The number of captured mages and vew angles vares from platform to platform. For example, the MTI can capture two mages at 0 and 50 n a sngle pass, and the AATSR can observe the same target at vew zenth angles of 0 and 55. However, MISR can produce mage stacks for nne camera angles (+70, +60, +45.6, + 6., 0, -6., -45.6, -60, -70 ) and CHRIS/Proba provdes multple observatons of the same scene at fve dfferent angles (+55, +36, 0, -36, -55 ). These functonaltes open up new applcatons n the areas of change detecton, mage fuson for classfcaton and thematc map producton, resoluton enhancement and so on (Chan 008a, Chan 008b). However, for any successful applcaton, accurate regstraton of these mult-angle mages s a prerequste. Automatc regstraton n these cases faces two man challenges: ) mages captured at large vew angles are susceptble to resoluton change and blurrng, whch makes precsely locatng control ponts (CPs) dffcult; ) local geometrc dstorton caused by varatons n platform stablty s serous, whch makes rgd transform models such as the projectve model unrelable. A manual regstraton approach s not mpossble n stuatons where a large number of mages need to be regstered. Also the accuracy of the manual approach can not be consstent as t strongly depends on decsons made by the operator. Thus, there s a pressng need for automatc mage regstraton methods for mult-angle magery. A typcal regstraton method can be dvded nto four steps: ) feature detecton; ) feature matchng; 3) transform model estmaton; and 4) mage resamplng (Ztova and Flusser, 003). There are many regstraton approaches, but dependng on whether feature detecton (step ) s nvolved, regstraton methods can broadly be classfed nto two categores: areabased and feature-based methods (Ztova and Flusser, 003). Generally speakng, area-based methods have hgher accuracy than feature-based methods, and they are partcularly wellsuted for mages acqured from the same sensor (Eastman et al., 007). However, there are dffcultes n applyng area-based methods drectly on mult-angle magery. Geometrc dstorton s usually hgh for mages acqured at hgh vew angles, makng t mpossble to use a rgd transform model such as the affne or projectve transform. A vable alternatve s to use a nonrgd regstraton model by searchng for an adequate number of qualty CPs and then estmatng the model s parameters. However, f these CPs are gathered by frst defnng a wndow n the nput mage and then searchng for a wndow match n the reference mage, we face the problem of havng to deal wth a very large searchng space (n the reference mage). In order to solve ths problem usually a coarse-to-fne herarchcal strategy s adopted. The template frst fnds canddate locatons n the reference mage at a coarse resoluton, whch can be obtaned by way of the pyramd approach; then the postonal accuracy s gradually mproved by movng up to fner resolutons. However, for mult-angle magery such as CHRIS/Proba, ths method s not applcable as the gray level smlarty between mult-angle magery s weak due to * Correspondng author

2 resoluton dsparty and severe blurrng. Comparatvely, feature-based methods that work on mage features are more robust to varatons n vew angle and are therefore more suted for mult-angle magery regstraton (Capel and Zsserman, 003). However ther dsadvantage s that the number of detected CPs s sometmes few and therefore wll cause problems for the estmaton of a complex non-rgd transform model such as the thn plate splne (TPS) or pecewse lnear (PL) model. To tackle the above-mentoned problems, we outlne n ths paper an automatc regstraton method that ntegrates the merts of both area-based and feature-based methods. It nvolves a two-stage CPs detecton scheme where canddate CPs are collected frst wth the scale nvarant feature transform (SIFT) method and then wth a template matchng method usng normalzed cross-correlaton (NCC) as a crteron. It also ncorporates a herarchcal approach to refne the collected CPs. The outlers n the SIFT CPs are dscarded by the ambguty crteron and a robust estmaton of the projectve transform model wth m-estmator sample consensus (MSAC) (Torr and Zsserman, 000); the outlers n the NCC CPs are elmnated by a spatal constrant nstead of a threshold on the NCC coeffcents. In order to make sure CPs are as accurate as possble, a fnal teratve refnng procedure based on the statstcal nature of CPs s performed to remove CPs of low accuracy. The TPS model s fnally estmated va the selected CPs. The non-rgd TPS model s adopted to surmount the serous local dstorton problem n mult-angle magery. We tested our approach on three sets of CHRIS/Proba mages and accurate regstraton results are attaned. In the followng secton the methodology s descrbed (secton ). Expermental results are presented n secton 3. Secton 4 focuses on major conclusons and drectons for future research.. METHODOLOGY The proposed regstraton method s composed of four stages (Fgure. ). Each stage s descrbed n detal below.. SIFT Control Ponts Selecton Among varous local feature detecton methods, SIFT s a promsng approach because t mproves detecton stablty n stuatons of nosy nput (Lowe, 004). The method s also preferable when changes occur n scale, llumnaton, and to a certan extent n the 3D camera vewpont. It acheves almost real-tme performance and the detected features are hghly dstnctve. An extensve evaluaton of varous local descrptors robustness n terms of vewng condtons and blurrng effects s found n Mkolajczyk and Schmd (005), and SIFT-based descrptors are descrbed as the best performers. SIFT not only defnes the poston of detected ponts but also descrbes pont detecton qualty. The detected SIFT ponts, also referred to as keyponts, are canddate CPs for feature matchng. A keypont descrptor s a qualty measurement descrbng the regon around the keypont. The SIFT algorthm can be dvded nto four steps: scale-space extrema detecton, keypont localzaton, orentaton assgnment and keypont descrptor assgnment. As space s lmted we refer the nterested reader to Lowe (004) for more detals. Once the keypont descrptor has been calculated, keyponts can be matched by usng the mnmum dstance method. However, not every par of matched keyponts can be thought of as SIFT CPs as the keypont descrptor only contans lmted context and hence feature matchng wll often be ambguous. Two crtera are used to flter out the outlers, or the bad pars. The frst crteron st th, an ndcator of the ambguty of each matched keypont. T d th = () d where d s the dstance to the nearest matched keypont and d s the dstance to the second nearest. If T th s close to, t means d s close to d. That s to say, for a certan keypont n the nput mage, SIFT detected two possble matchng keyponts n the reference mage. Ths s an ambguous stuaton and the matched par wll be deleted f T > th The second crteron s a spatal constrant based on the MSAC algorthm. Although local geometrc dstorton exsts n multangle mages, the man geometrc relatonshp can stll be represented by a projectve transform model (Capel and Zsserman, 003). MSAC utlzes ths spatal relatonshp to elmnate falsely matched keyponts. MSAC, or m-estmator sample consensus, s an mproved verson of the RANDom Sample Consensus (RANSAC) algorthm, whch has been wdely used for rejectng outlers n matchng ponts (Km and Im, 003). Both algorthms frst estmate a projectve model wth four randomly selected ponts. After that the transform model s evaluated wth regard to a fttng cost functon: Fgure. Flow chart showng the processng chan C= ρ () ( e )

3 where refers to the th matched keypont par andρ s the error term defned as: respectvely. g and g T S are the correspondng mean grey values respectvely. R and C are the numbers of rows and columns of the mage chps. ρ ( e ) L f e < T m = T f e T m m In equaton (3) T m s the threshold beyond whch the matched keypont pars are consdered outlers for the transform model, and e = ( x x ) + ( y y ) + ( x x ) + ( y y ) s defned as the observed error functon for a matched keypont par ( x, y ) ( x, y ), wth ( x, y ), ( x, y ) pont postons calculated va the estmated projectve transform model. L s a varable that determnes the dfference between RANSAC and MSAC. For RANSAC L=0, whch means every nler has the same effect on the estmated transform model; for MSAC L= e, whch means every nler has a dfferent nfluence on the fttng of the estmated transform model. Thus, t permts more flexblty n settng T m. In case T m s set too hgh some outlers can be regarded as nlers. MSAC mtgates ths by treatng all nlers dfferently. The default settng for T m s 64. In ths study, the above procedures were repeated 500 tmes and the transform model wth the lowest fttng cost functon value C was selected. Fnally, the projectve transform s re-estmated usng all the keypont pars whose observed error functon e values are lower than T m. These keypont pars consttute the frst set of CPs, whch are referred to as SIFT CPs n the paper.. NCC Control Ponts Selecton The problem of usng only feature-based SIFT to generate CPs s that the number of CPs may be too small and CPs may be unevenly dstrbuted. To remedate ths problem, an area-based CP selecton procedure s ntated. Frst, an ntermedate regstered mage s generated by applyng the projectve transform descrbed n Stage. We wll call ths mage the ntermedate nput mage and use t for a template matchng procedure. The ntermedate nput mage s separated nto mage chps of pxels, and each chp s matched wth a correspondng chp n the reference mage va NCC, or normalzed cross-correlaton. The matched center ponts of the chp pars are then used as canddate CPs. Ths way the number of CPs for the fnal non-rgd transform model estmaton s ncreased. The NCC coeffcent r s calculated as: r R ( gt j gt)( gs j g S) = j= = / R C = j= C (, ) (, ) ) ( gt j gt) ( gs j g S) (, ) (, ) ) where g (, j) and g (, j) represent the grey values of the T S mage chps n the nput mage and n the reference mage (3) (4) NCC has the followng advantages that make t well-suted for CP searchng: ) the NCC coeffcent s brghtness nvarant, that s to say, n the case of changes n external llumnaton the NCC coeffcent wll not change. Ths s especally mportant for mult-angle magery as objects captured at dfferent vew angles may have dfferent llumnaton and reflectance characterstcs; ) NCC CPs are robust to blurrng. Whle the NCC coeffcent wll vary wth the blurrng of the template, the poston of ts maxma wll not change. Ths s also mportant for mages acqured at hgh vew angles as these usually suffer from serous blurrng; 3) NCC s comparatvely fast to calculate (Lews, 995); and 4) NCC CPs obtaned from mage chps wll be evenly dstrbuted across the whole mage. There are three mportant ssues wth regard to collectng NCC CPs. The frst has to do wth template sze. The larger the template s, the more unque the matchng entty wll be. However, the calculaton load wll ncrease as well. In our mplementaton the template wndow sze was set at as a compromse between calculaton load and accuracy. In most cases, ths wndow sze enables us to fnd enough salent mage features lke lnes and rdges. The second ssue s related to the sze of the searchng space. We defne a searchng space whch s about one and a half tmes the sze of the template n the ntermedate nput mage, that means the searchng space has a sze of As the ntermedate nput mage almost overlaps wth the reference mage, such a large space can make sure the template can fnd the matchng chp wthn the constraned space. The last ssue s about how to arrve at subpxel accuraces. In many applcatons, such as superresoluton mage reconstructon, subpxel accuracy s requred. However, NCC can only determne an nteger value for the CP poston. In order to obtan subpxel accuracy, a nd order polynomal around the poston of the NCC coeffcent maxmum s establshed. Nne ponts are used to determne the nd order polynomal applyng the least-squares method. The CP poston wth sub-pxel accuracy corresponds to the locaton where partal dfferentaton of the polynomal reaches a zero value. After NCC CPs have been gathered, they need to be screened for outlers. Homogeneous areas such as sand and water, whch show repettve patterns or low contrast may lead to false matches. NCC may also fal when movng objects such as clouds and shadows occur n the magery. The tradtonal way to elmnate outlers n NCC CPs s by thresholdng the NCC coeffcents. However, ntal experments showed that ths method s not effectve. It s dffcult to defne a proper threshold for all the mages, and t often occurs that the matchng s successful n spte of coeffcent values smaller than the threshold, and vce versa. We therefore use a geometrc constrant to detect outlers n NCC CPs. If the dstance between a par of CPs s larger than a threshold value T d, t s regarded as an outler. The default settng for T d s 6..3 Control Ponts Refnng At ths stage n the process two groups of CPs have been obtaned: one SIFT and one NCC set. Both sets have gone

4 through outler detecton procedures, and together they consttute the potental set of CPs for TPS model estmaton. As TPS s based on nterpolaton, t s mportant to make sure that each par of CPs s as accurate as possble. At ths stage, the objectve s to obtan the most accurate CPs possble by prunng ponts wth large random errors. Ths can be done by utlzng the statstcal characterstcs of the CPs. Gven a true, nose-free CP ( x, y) n the reference mage, the probablty densty of the correspondng observed CP locaton ( x, y) can be thought of as a normal dstrbuton (Capel and Zsserman, 003): ( x x) + ( y y) Pr(( x, y) ( x, y)) = exp πσ σ (5) so on (Chu, 000). The thn-plate splne nterpolaton functon can be expressed as: N f ( x, y) h h x h F r Inr 3 = x = + + N g( x, y) h h h y = 3 G y r Inr = where ( x, y) (6) s the coordnate n the nput mage, and ( f ( x, y), g( x, y)) s the coordnate n the reference mage. ( x, y ) s the detected CP poston n the nput mage. r = ( x x ) + ( y y ) represents the dstance between ( x, y) and ( x, y ), and h,..., h defne an affne transform 3 matrx. F and G are the weghts of the non-lnear radal nterpolaton functon. The observed nosy pont ( x, y ) s the detected CP n the reference mage, and the true, nose-free pont ( x, y ) comes from the calculated CP va the CP n the nput mage and an estmated 3rd polynomal transform model. The polynomal transform of the 3rd order has often been used when geometrcal dstorton s substantal, and the resdual stochastc characterstcs of the 3rd order polynomal transform have been well studed (Buten and van Putten, 997). Whle t s not the recommended transform model for mult-angle magery, t has the followng characterstcs whch make t sutable for prefttng: ) the polynomal functon s an approxmaton functon, whch means that a CP par wth a comparatvely large random error wll not dramatcally degrade the polynomal functon parameter estmaton; ) the approxmately evenly dstrbuted NCC CPs contrbute to an unbased estmaton of parameters. For a normal dstrbuton, about 99.7% of the values are wthn plus and mnus three standard devatons from the mean. Therefore, f the measured error of a CP par s larger than three standard devatons, t s consdered as a large random error and the CP par wll be dscarded. The CPs refnng stage can be summarzed as follows: () Defne a 3rd polynomal transform model usng the leastsquares method wth all the CPs. () Calculate the nose-free pont poston, and obtan the model resdual dx and dy n the horzontal and vertcal drecton. Compute the mean and standard devaton of dx and dy, and elmnate the ponts whose dx or dy value s beyond three standard devatons. () Repeat the above procedures untl the followng condton s fulflled: the resduals n both drectons are wthn three standard dervatons..4 Image Warpng TPS s an nterpolaton functon wth the CPs havng a one-toone mappng relatonshp. TPS s also the only splne model that can be cleanly decomposed nto a global affne and a local non-affne warpng component, and thus t can account for the local deformaton caused by optcal effects, relef change and To solve equaton (6) wth N pars of CPs, the followng equlbrum constrants are mposed: N N N F = F x = F y = 0 = = = N N N G = G x = G y = 0 = = = Wth N pars of CPs and the sx equatons n equaton (7), we can solve the N+6 unknown parameters n the TPS model. A more compact calculaton for the unknown parameters s expressed as: h3 h u u... un h h 0 0 = v v... v h h 0 0 n u v 0 r Inr... r Inr F G x y n n u v r Inr 0... r Inr F G x y n n u v r Inr n n r Inr n n... 0 Fn G x n n yn n n After the parameters of the TPS model have been estmated, the warpng of the nput mage can be performed usng the TPS model and the blnear resamplng functon. 3. EXPERIMENTS The proposed method was tested on mult-angle CHRIS/Proba magery. All the mages were acqured n mode 3 at fve dfferent vew angles. The result of the 8th band ( nm) wll be used as a demonstrator. The reference mage s the nadr mage. Detals of the data sets are descrbed n Table I. Ste Country Tme Kalmthout Belgum st July, 008 Djle Valley Belgum 0th May, 008 Gnkelse Hede TheNetherlands nd Oct., 007 Table. Study area (7) (8)

5 The pre-processng of CHRIS/Proba was done wth the opensource software BEAM CHRIS-Box. It ncludes two mportant procedures: ) nose reducton: replace mssng data and destrpng; ) atmospherc correcton: retreve the surface reflectance from remotely sensed magery by removng the atmospherc effect (Guanter, 005). dsparty between mult-angle magery, at least 4 pars of true SIFT CPs can be detected for the ntermedate projectve transform model estmaton. The outlers n the set of canddate SIFT CPs can be successfully dentfed va the ambguty (a) (b) (c) (d) (e) Fgure. Mult-angle CHRIS/Proba magery for the Kalmthout ste: (a) Nadr (the squares correspond to the zones shown n detal n Fgure ), (b) +36, (c) -36, (d) +55, (e) -55. Fgure shows the 8th band for the fve mult-angle mages (+/-55, +/-36, and 0 ) for the Kalmthout ste. It s clear from Fgure that blurrng for large vew angles (+/- 55 ) s serous and that llumnaton condtons vary wth dfferent vew angles. All the off-nadr mages were co-regstered to the nadr mage usng the proposed method. Twenty sets of manually selected CPs were used as ground truth. The regstraton accuracy, represented by the root mean square error (RMSE), was calculated for each regstered mage as shown n Table. Ste Angle RMSE Kalmthout Djle Valley Gnkelse Hede Average Table. Regstraton accuracy for dfferent mages The average regstraton accuracy assessed by means of a set of manually selected CPs s about 0.77 pxels, whch s very hgh. The results also show that on average the regstraton error for larger vew angles at +/-55 s hgher than for smaller vew angles at +/-36, whch s normal. Results of vsual evaluaton of the proposed algorthm are shown n Fgure. Three zooms are provded showng that the regstered mage fts well wth the reference mage across the whole scene. 4. CONCLUSION In ths paper a two-stage regstraton scheme s proposed. Salent SIFT CPs are detected frst and then used for the estmaton of the projectve transform n stage. SIFT s shown to be a good feature detecton method for mult-angle magery. Even n the case of severe blurrng and large resoluton crteron and MASC. Outler detecton s not only mportant for dentfyng true SIFT CPs but also vtal for detectng subsequent NCC CPs. Our experments also testfy that wthout ths outler procedure the ntermedate nput mage wll not overlap well wth the reference mage, whch wll make subsequent NCC CP detecton fal. NCC has also been proven to be a good area-based CP detecton method for obtanng evenly dstrbuted CPs n stage. After pre-regstraton the ntermedate nput mage has geometrc characterstcs smlar to the reference mage. Ths ntermedate step not only makes template matchng much easer because the searchng space s more constraned but also makes the NCC matchng crteron hold. For example, f two mage chps are of a dfferent spatal resoluton, whch s the case for mult-angle magery, NCC wll fal. Also NCC s robustness to llumnaton change and blurrng makes t partcularly suted for CP detecton, startng from the ntermedate nput mage. The teratve CP refnng procedure n stage 3 s based on two assumptons: () the densty of observed CPs s Gaussan, and () a 3rd order polynomal functon s an emprcally more approprate global transform model for mult-angle magery. Actually before the automatc regstraton method for CHRIS/Proba was proposed, dfferent transform models were tested wth CPs selected by hand. The 3rd order polynomal model proved to be a better transform model than other global transform models. Durng the refnng procedure bad CPs wth large random errors are successfully dentfed, however, a small part of the good CPs wth hgh accuracy are elmnated as well. Indeed, whle the 3rd order polynomal model resdual can be thought of as an ndctor of a bad par of CPs, the large model resdual can not guarantee t really s (Buten and Van Putten, 997). Vsual nspecton of the fnal CPs demonstrates that after CP refnng only CPs wth hgh accuracy are left. The TPS model n stage 4 not only helps to deal wth local dstorton n mult-angle magery but also helps to reach subpxel regstraton accuracy. Another key component to reach sub-pxel accuracy s that the CPs themselves are detected wth sub-pxel accuracy by way of nterpolaton. The overall results obtaned wth three mult-angle CHRIS/Proba mage sets are encouragng. The proposed

6 method can also be appled on other mult-angle magery from systems such as MTI and MISR. ACKNOWLEDGEMENTS The authors would lke to express ther grateful thanks to Lus Guanter who helped to generate the reflectance mages used n ths study. The research presented n ths paper s funded by the Belgan Scence Polcy Offce (BELSPO) n the frame of the STEREO II programme - project HABISTAT (SR/00/03) Fgure. Regstraton results for the Kalmthout ste. The frst, second and thrd row correspond to the upper left corner, the center and the bottom rght corner of Fgure respectvely. In each row the centers of the cross correspond to the same pont at dfferent vew angles after regstraton. REFERENCES Buten, H.J. and van Putten, B., 997. Qualty assessment of remote sensng mage regstraton analyss and testng of control pont resduals. ISPRS Journal of Photogrammetry and Remote Sensng, vol. 5, pp Capel, D., and Zsserman, A., 003. A Computer vson appled to super resoluton. IEEE Sgnal Process., 0(3), pp Chan, J.C.-W., Ma, J. and Canters, F., 008a. A comparson of superresoluton reconstructon methods for mult-angle Chrs/Proba mages. Proceedngs of SPIE Image and Sgnal Processng of Remote Sensng XIV, pp -. Chan, J.C.-W., Ma, J., Kempeneers, P., Canters, F., Vandenborre, J. and Paelnckx, D., 008b. An evaluaton of ecotope classfcaton usng superresoluton mages derved from Chrs/Proba data. Proceedngs of IGARSS, Vol. III, pp Chu, H. and Rangarajan, A., 000. A new algorthm for nonrgd pont matchng. IEEE Conference on Computer Vson and Pattern Recognton (CVPR), vol., pp Eastman, R.D, Mogne, J.L, Netanyahu, N.S., 007. Research ssues n mage regstraton for remote sensng. IEEE Conference on Computer Vson and Pattern Recognton, 007, pp. -8. Guanter, L., Alonso, L. and Moreno, J., 005. A method for the surface reflectance retreval from PROBA/CHRIS data over land: applcaton to ESA SPARC campagns. IEEE Trans. Geosc. and Remote Sensng, 43(), pp Km, T. and Im, Y.J., 003. Automatc satellte mage regstraton by combnaton of matchng and random sample consensus. IEEE Trans.Geosc. and Remote Sensng, 4(5), pp. -7. Lews, J.P., 995. Fast template matchng. Proc. Vson Interface, pp.0-3. Lowe, D.G., 004. Dstnctve mage features from scalenvarant keyponts. Internatonal Journal of Computer Vson, 60(), pp Mkolajczyk, K. and Schmd, C., 005. A performance evaluaton of local descrptors. IEEE Trans. Pattern Anal. Mach. Intell., 7(0), pp Torr, P.H.S. and Zsserman, A., 000. MLESAC: A new robust estmator wth applcaton to estmatng mage geometry. Computer Vson and Image Understandng, 78(), pp Ztova, B and Flusser, J, 003. Image regstraton methods: a survey. Image and Vson Computng, (). pp