IMAGE STITCHING WITH PERSPECTIVE-PRESERVING WARPING

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1 ISPRS Annals of the Photogrammetry, Remote Sensng and Spatal Informaton Scences, Volume III-3, 2016 IMAGE STITCHING WITH PERSPECTIVE-PRESERVING WARPING Tanzhu Xang, Gu-Song Xa, Langpe Zhang State Key Laboratory of Informaton Engneerng n Surveyng, Mappng, and Remote Sensng, Wuhan Unversty, Wuhan, Chna Emal: {tzxang, gusong.xa, zlp62}@whu.edu.cn Commsson III, WG III/3 KEY WORDS: Image sttchng, Image algnment, Perspectve-preservng warpng, Smlarty transform, Projectve transform ABSTRACT: Image sttchng algorthms often adopt the global transform, such as homography, and work well for planar scenes or parallax free camera motons. However, these condtons are easly volated n practce. Wth casual camera motons, varable taken vews, large depth change, or complex structures, t s a challengng task for sttchng these mages. The global transform model often provdes dreadful sttchng results, such as msalgnments or projectve dstortons, especally perspectve dstorton. To ths end, we suggest a perspectve-preservng warpng for mage sttchng, whch spatally combnes local projectve transforms and smlarty transform. By weghted combnaton scheme, our approach gradually extrapolates the local projectve transforms of the overlappng regons nto the non-overlappng regons, and thus the fnal warpng can smoothly change from projectve to smlarty. The proposed method can provde satsfactory algnment accuracy as well as reduce the projectve dstortons and mantan the mult-perspectve vew. Expermental analyss on a varety of challengng mages confrms the effcency of the approach. 1. INTRODUCTION Lmted by the narrow feld of vew (FOV) of optcal cameras, t s dffcult to fully capture the entre scene wth a sngle mage. Image sttchng s to combne multple mages wth overlappng FOV nto a sngle larger and seamless mosac, whch can extend the effectve FOV of cameras. Image sttchng has been wdely studed n photogrammetry, remote sensng and computer vson (Brown and Lowe, 2007). The key of mage sttchng s how to model the geometrc transformatons between multple mages wth overlappng FOVs. Tradtonal mage-sttchng methods (Szelsk, 2005) try to estmate a global 2-dmensonal transform, typcally homography, by correspondng ponts to algn the nput mages. However, t s usually lack of flexblty to handle all types of scenes and motons. Generally speakng, the global transform s accurate as long as some restrctve condtons are met, such as planar scenes or parallax free camera moton, for nstance, the shootng locaton s fxed or only rotatonal moton s allowed. Whle n realty, especally n the days when smart phones and unmanned aeral vehcles have become more and more popular, the magng condtons are beyond control, mages are taken casually and often full of notceable depth changes or complex scenes. Thus mage-sttchng algorthms wth a sngle global transform can no longer ft these data well, and often lead to unsatsfed vsual artefacts, e.g. ghostng effects (msalgnments), projectve dstortons (shape and perspectve dstortons). There are often two knds of methods (Zaragoza et al., 2013) to overcome the prevously mentoned msalgnment: (1) by estmatng a more sutable mage algnment model, or (2) by usng advanced post-processng technologes after algnment such as compostng, blendng. For second method, seam cuttng (Chon et al., 2010) or mult-band blendng (Brown and Lowe, 2007) s supposed to be a good choce, and some commercal sttchng software, e.g. Mcrosoft Image Composte Edtor (ICE) (Mcrosoft research, 2015) and Autosttch (Brown and Lowe, 2007), can provde advanced de-ghostng algorthms to remove the undesred artefacts. However, for sttchng mages taken under the uncontrolled magng condtons, merely relyng on the post-processng algorthms s nadequate, the obvous msalgnments are hard to be elmnated by these methods. It has been reported that the precse model of algnment s vtal mportant for mage sttchng (Ln et al., 2011). Then for the projectve dstorton problem, there are few methods at present. Most methods neglect the projectve dstortons, so some sttched area may be stretched or nconsstently enlarged, the perspectve of the sttched mage may be undesrably changed. Ths serously affects the mage sttchng performance. Ths paper addresses the mage algnment problem for mage sttchng, especally the projectve dstortons n mage sttchng. Recently, some algorthms based on local warpng models (Zhang and Lu, 2014) for accurate algnment have been proposed. Instead of estmatng a sngle global homography, (Gao et al., 2011) proposed a dual-homography warpng (DHW) model to sttch mages. It frst dvdes the scene nto a ground plane and a dstant plane, then estmates two transforms respectvely, fnally smoothly ntegrates the two by weght average. Ths method mproves the vsual effect of mage sttchng and works well for many smple scenes, but t s worth notcng that t s dffcult to decde the number of the requred planes for arbtrary scene. Alternatvely, (Ln et al., 2011) ntroduced the smoothly varyng affne (SVA) method. Ths algorthm replaces a global affne transform wth a smoothly varyng affne sttchng feld, so that t allows local geometrc deformatons. In contrast wth other approaches, SVA s more flexble and tolerant to parallax, but t only has sx degrees of freedom and thus t s not suffcent to acheve global projectvty. do: /sprsannals-iii

2 ISPRS Annals of the Photogrammetry, Remote Sensng and Spatal Informaton Scences, Volume III-3, 2016 In order to mtgate the drawbacks of SVA, (Zaragoza et al., 2013) presented the as-projectve-as-possble warpng (APAP) algorthm, amng to be globally projectve. APAP uses a smoothly local projectve transform estmated by movng drect lnear transform (DLT) and allows local non-projectve devatons. It has been reported to acheve outstandng performances on mage algnment (Zaragoza et al., 2014). However, APAP may ntroduce shape and perspectve dstortons n non-overlappng regons. For nstance, buldngs may be stretched to parallelogram, the perspectve of sttched mage may be undesrably changed, for the fact that t just smoothly extrapolates the projectve transform nto these regons. More recently, (Chang et al., 2014) proposed a shapepreservng half-projectve (SPHP) warp for mage sttchng. From the overlappng regons to the non-overlappng regons, t apples three dfferent contnuous warps to acheve the smoothly change from projectve to smlarty. Ths algorthm can allevate the shape dstorton n the non-overlappng regons. However, t s hard to handle parallax. The combnaton of SPHP and APAP s an mprovement verson to handle parallax, but t s senstve to parameter selecton and t may ntroduce unsatsfactory local deformatons. Nevertheless, most methods only focus on the accuracy of algnment wthout consderng dstortons caused by projectve transform, especally perspectve dstorton. As argued n (Chang et al., 2014), usng projectve transform only obtans a sngle-perspectve sttched mage and t wll nevtably ntroduce perspectve dstortons. To solve ths problem, the smlarty transform s ntroduced to tweak projectve transform to mtgate perspectve dstortons n ths paper, because smlarty transform just conssts of translaton, rotaton and unform scalng and thus t doesn t brng about perspectve dstorton. In ths paper, we present a novel perspectve-preservng warpng, whch ntegrates the multple local homographes wth global smlarty transform. More precsely, the proposed algorthm frst dvdes the nput mage nto grd meshes, and then estmates the local homographes by movng drect lnear transform for each mesh. By ths way, an accurate algnment can be acheved n overlappng regon. Subsequently, a global smlarty transform s ntroduced to compensate the perspectve dstortons n non-overlappng regon by weghted ntegraton wth the local homographes. In addton, we also present a method to smoothly calculate the weght coeffcents based on the analyss of the projectve transformaton. Experments demonstrate that the proposed warpng can not only be flexble to handle parallax, but also preserve the perspectve well wth less dstortons. The remander of the paper s organzed as follows. Secton 2 detals the proposed approach for mage sttchng. Secton 3 then analyses and dscusses the expermental results. Secton 4 fnally ends the paper wth some concluded remarks. 2. THE PROPOSED ALGORITHM Ths part detals a complete presentaton of the proposed algorthm. Frst we depct the estmaton method of local warpng by movng DLT. Then we descrbe the weghted combnaton of global smlarty transform and local warpng. 2.1 Local warpng Gven a par of matchng ponts Y x y 1 T and Y x y 1 T n the target mage I and the reference mage I respectvely, a projectve transform H for mappng Y to Y can be obtaned by: x x h1 h2 h h7 h8 1 x y H y h h h y The transform matrx H can be estmated by a group of correspondng ponts between I and I usng DLT. So the Eq. (1) can be rewrtten by a cross product: 0 Y HY, that s 31 (1) T T 03 1 Y y Y h1 T T 03 1 Y 03 1 xy (2) T T y Y x Y 0 h 31 9 where the transform H s denoted n a 9 1 vector. In the other 3 9 matrx, n fact there are only two rows whch are lnearly N ndependent. Gven N matchng ponts Y 1, and N Y, H 1 can be estmated by N 2 2 (3) 1 h h arg mn a h arg mn Ah, s. t. h 1 h where a s the two lnearly ndependent rows, and A s a 2N 9 matrx. The soluton s the the least sgnfcant rght sngular vector of A. In APAP (Zaragoza et al., 2013), t frst parttons the target mage nto grd meshes. For each mesh, t adopts a locaton dependent homography. So the local homography s estmated from the weghted problem N 2 h arg mn w* ah, s. t. h 1 (4) h 1 where w * denotes the nfluence of each par of pont conspondences on the th grd. Assumng x denotes the center of the th grd, the weghts are calculated as 2 2 w max exp x x /, (5) where s the scale parameter, and 0 1 s used to avod the numercal ssues n estmaton. From Eq. (5), the weght s hgh when the center pont s closer to the matchng ponts, and approxmately equal when the center pont s far from the matchng ponts. So the estmated H can ft the local structures. As can be seen n Fgure 4 (a) and (b), the overlappng regons are well algned, but the sttched result suffers from the obvous shape and perspectve dstortons n the non-overlappng regons. In our algorthm, the smlarty transform s then employed to compensate the dstortons, as detaled below. 2.2 Optmal smlarty transform Smlarty transform can be regarded as a combnaton of pannng, zoomng and n-plane rotaton of a camera, whch do: /sprsannals-iii

3 ISPRS Annals of the Photogrammetry, Remote Sensng and Spatal Informaton Scences, Volume III-3, 2016 keeps the vewng drecton unchanged, and thus t can preserve the perspectve. If we can fnd a global smlarty transform that approxmates the camera n-plane moton between the reference and target mages, t can offset the nfluence of camera n-plane moton to some extent. Whle usng all pont correspondences for the estmaton of smlarty transform lke (Chang et al., 2014) s not reasonable because the overlappng regons may contans varous mage planes. From (Ln et al., 2015), the plane that s most parallel to the mage projectve plane at the focus length of the camera can be used to estmate the optmal smlarty transform by ts pont correspondences. To fnd out pont correspondences on ths plane, RANSAC (Chn et al., 2012) s adopted to segment the matchng ponts teratvely. Gven the threshold d (n our experments, 0.01 s a good choce), RANSAC s used to fnd a group of ponts wth largest nlers, and remove nlers, then repeat these steps on the left outlers untl nlers are less than, e.g. 50 nlers. For each group of nlers, a smlarty transform can be estmated, and the correspondng rotaton angle can be calculated. The smlarty transform wth smallest rotaton s the one we preferred. The parameters mentoned above are selected by experence and experments. Fgure 1 shows an example of calculatng the optmal global smlarty transform. Dfferent colours denote dfferent groups of pont correspondences. Here the blue pont group s selected to estmate the optmal smlarty transform whose rotaton angle s the least. Fgure 1. The optmal smlarty transform estmaton 2.3 Weghted combnaton of smlarty transform where T s the local warpng for the reference mage n the After obtanng the global smlarty transform, t can be used to adjust the local warpng by weghted combnaton. For the smoothly transton, the whole mage s taken nto consderaton to estmate the fnal warpng. The weghted ntegraton can be calculated as H H S (6) th grd. Because dstortons n the non-overlappng regons do not gradually change along the x - axs, the weght ntegraton scheme presented n (Ln et al., 2015) s not an optmal soluton. If the drecton where the dstorton just occurs along exsts, the weght coeffcents can be better estmated. where H s the local warpng n the th grd. H s the fnal local warpng, and S s the smlarty transform. and are weght coeffcents wth the constrant 1. Fgure 2 shows the change of weghts of local warpng and smlarty transform for Temple mages n Fgure 3 (a). The pxel value denotes the weght coeffcent. The greater the pxel value s, the hgher the weght s. From Fgure 2, the area s far from the overlappng regons, especally the dstorted non-overlappng regons, t assgns a hgh weght for smlarty transform so that t can mtgate the dstortons as much as possble, whle for the area near the overlappng regons, t assgns a hgh weght for the local warpng so as to ensure the accuracy of algnment. Because the local warpng n the overlappng regons s tweaked, the reference mage should also take a correspondng warpng to guarantee the algnment between the target mage and the reference mage. The warpng for reference mage s T H H 1 (7) Fgure 2. Weght map for local warpng (top) and global smlarty transform (bottom) do: /sprsannals-iii

4 ISPRS Annals of the Photogrammetry, Remote Sensng and Spatal Informaton Scences, Volume III-3, Weght calculaton Early n (Chum et al., 2005), t apples the change of coordnaton to estmate the geometrc error for the homography, and t can also help to reveal the dstorton characterstcs of projectve transform. (11) Accordng to (Chum et al., 2005), the coordnaton x, y of the target mage s rotated to a new coordnaton system u, v. The relatonshp between them can be descrbed as: where Qa s affne transform and Qp s projectve transform. x cos sn 0 u y sn cos 0 v (8) Accordng to (Kmmel et al., 2011), the local scale change at pont u, v under the projectve transform s defned as the scale change of the frst order matrx of Q (.e. Jacobn matrx of Q ) at pont u, v, so n terms of Qa and Qp, the local scale change s calculated as below: det J u, v det J a (u, v) det J p u, v ka So a new projectve transform Q that transforms u, v to x, y s acqured whch meets the formula below x q1 q2 q3 u h1 h2 h3 cos sn 0 u y q4 q5 q6 v h4 h5 h6 sn cos 0 v q7 q8 1 h7 h8 1 0 (9) Supposng arctan h8 / h7, and combnng wth the above fomula, we can get q8 h7 sn h8 cos 0. So Q can be rewrtten as q1 q2 q3 Q q4 q5 q6 c 0 1 (10) where c h72 h82. And then Q can be decomposed as below: 1 1 cu 3 (12) where det denotes the determnant, and ka s ndependent of u and v. From the Eq. (12), the local scale change derved from Q only reles on u, that s, the dstorton of projectve transform Q only occurs along the u - axs. (Chang et al., 2014) also expounds ths character of projectve transform. Obtanng u - axs, the mage can be transformed to u, v coordnaton system and the projectve dstorton can be compensated along u - axs. For smplcty, the centre of mage s used as the orgn of coordnaton, and the rotaton transform s appled on the reference mage and the warped target mage. Supposng o s the orgn pont, the unt vector on u -axs. For every grd mesh pont P (the th centre denotes of grd), d s the projected length of vector op on vector ou. And the maxmum length among set d correspondng projectve pont Pmax and the mnmum length among set d (a) Temple mages (b) Raltracks mages Fgure 3. Orgnal mages for experments do: /sprsannals-iii

5 ISPRS Annals of the Photogrammetry, Remote Sensng and Spatal Informaton Scences, Volume III-3, 2016 (b) (a) (c) (d) Fgure 4. (a)-(b) APAP warpng. (c)-(d) our warpng. correspondng projectve pont Pmn can be obtaned. So the weghtng coeffcents can be calculated as: Pmn P Pmn Pmax / Pmn Pmax 1 (13) (14) where Pmn P Pmax Pmn denotes the projectve length of Pmn P algnment as well as gracefully allevate dstortons and mantan the mult-perspectve vew. Fgure 5 and 6 depct the sttchng results on the Temple and Raltracks mages. The results of each experment are n the followng order: ICE, APAP, SPHP, SPHP+APAP, and our approach. And we also hghlght some specal areas of each result. Red boxes show parallax errors or dstortons, and green boxes show the satsfactory sttchng result. on Pmax Pmn. 3. EXPERIMENTS In ths part, a set of experments are conducted on a range of challengng mages captured casually. In order to evaluate the effcency of our approach, we also compare t wth the other algorthms, ncludng ICE (Mcrosoft research, 2015), APAP (Zaragoza et al., 2013), SPHP wth global homography (Chang et al., 2014), and SPHP wth APAP (SPHP+APAP) (Chang et al., 2014). To better compare the methods and reduce the nterference, post-processng methods lke blendng or seam cuttng detaled n (Szelsk, 2005) are avoded. The algned mages are smply blended by ntensty average so that any msalgnments reman obvous. In our experments, the key ponts n the reference mage and target mage are detected and matched by SIFT (Lowe, 2004). RANSAC (Chn et al., 2012) s used to remove msmatches. The parameters for other methods are set as the same suggested n each paper. The parameters of our method are a few, whch are set as 8.5, 0.01 n all our experments. Fgure 3 llustrates the orgnal mages for experments. Fgure 3 (a) shows a par of Temple mages taken from dfferent vews. The scene contans large depth changes, and dstnct multple planes. Fgure 3 (b) dsplays a par of Raltracks mages taken at dfferent locatons. The scene s complex and full of fnely structures, such as tracks and steel structures. These taken condtons are great challenge for mage sttchng. Fgure 4 shows the comparson of APAP warpng and the proposed warpng. We can see that APAP n Fgure 4 (a) and (b) uses a smoothly local projectve warpng that can allow local devatons to acheve a good algnment performance n the overlappng regons, whle some severe shape dstorton and perspectve dstorton occur n the non-overlappng regons, such as the ground brcks are stretched non-unformly, the temple s undesrably enlarged. Whereas the results of our warpng n Fgure 4 (c) and (d) can guarantee the accurate The ICE acheves a good vsually performance to some extent. It mantans the perspectve well and has less dstortons n the non-overlappng regons. For the Temple mages, the perspectve and the shape of buldngs are preserved. For Raltracks mages, the palm tree s uprght wthout tlt, the automobles are wthout stretch. Whle to one s regret, ICE s not able to algn the mages satsfactorly, and thus t suffers from the obvous msalgnments and ghostng effects, such as broken ground brcks n Temple result n Fgure 5, and broken tracks n Raltracks result n Fgure 6. Ths s because ICE apples the global homography for mage sttchng, ts transform model maybe nadequacy for the data. Adoptng local varyng warpng, APAP method can avod ICE s drawbacks, and acheve mpressve algnment performance n the overlappng regons wth few artefacts. There are few msalgnments for these mages, whether the ground nearby or the temple n the dstance n Fgure 5, whether the complex ral tracks or the dstant constructon ste n Fgure 6. However, APAP cannot handle dstortons well. It suffers from shape and perspectve dstortons n non-overlappng regons. In Fgure 5, t s obvously that the buldng s shape n Temple result s no longer rectangular, and becomes the tlted quadrlateral. In Fgure 6, the same defects can be seen, the palm tree s tlted and the automobles are undesrably stretched and enlarged. The SPHP and SPHP+APAP can mtgate the shape dstorton and preserve the shape well, for nstance, the buldngs n Temple result reman rectangular, and the automobles n Raltracks result keep the orgnal appearance, because SPHP ntroduces the smlarty transform to offset these geometrc dstortons. Because SPHP adopts a global projectve transform, t also undergoes notceable algnment errors, especally the roof of the temple n Fgure 5, and the tower crane n Fgure 6. Wth the help of APAP, SPHP+APAP method can greatly mprove the algnment ssue, and thus the roof of the temple and the tower crane are algned accurately. However, SPHP+APAP method has two problems. Frst, some structures exst undesrable deformatons, e.g. the temple behnd the trees n Fgure 5 and the buldng ste n the dstant n Fgure 6. Then ths method stll exst perspectve dstorton and thus t can t provde the fne perspectve. In Fgure 5, the second mage s sttched wth a global clockwse rotaton, and the Raltracks do: /sprsannals-iii

6 ISPRS Annals of the Photogrammetry, Remote Sensng and Spatal Informaton Scences, Volume III-3, 2016 result n Fgure 6 suffers from the same trouble. That s because the smlarty transform adopted n SPHP and SPHP+APAP s estmated by all pont correspondences, t s not optmal to compensate dstortons. Ths s partcularly true f the scene s full of depth change and contans multple dstnct planes, just lke Fgure 3. mage well. From the comparson, our method acheves the best vsual effects, and provdes an mpressve mage sttchng results. The last row of each experment s the results of our method. The proposed method gracefully combnes local warpng and global smlarty transform. What s more, the estmated global smlarty transform s better for compensatng the dstortons than SPHP and SPHP+APAP. From the results of Temple n Fgure 5 and Raltracks n Fgure 6, the proposed method can successfully handle parallax, thus to attan the satsfactory algnment accuracy n the overlappng regons. Moreover, t can allevate the projectve dstortons, especally the perspectve dstorton, so t can preserve the shape and perspectve of each Ths paper presents a perspectve-preservng warpng for mage sttchng. We observed that f the vews of mages do not dffer purely by rotaton or are not of a planar scene, these mages are often dffcult to algn accurately n the overlappng regons, and are often troubled by dstortons n the non-overlappng regons. The proposed approach gracefully combnes the local warpng wth the global smlarty transform, whch can handle parallax and dstortons effectvely. It weakly reles on the choce of parameters, and the suggested parameters can obtan a pleasant result for most stuatons. 4. CONCLUSION Fgure 5. Comparson wth the other mage sttchng algorthms on Temple mages. From frst row to last row, the results are: ICE, APAP, SPHP, SPHP+APAP, and our approach. do: /sprsannals-iii

7 ISPRS Annals of the Photogrammetry, Remote Sensng and Spatal Informaton Scences, Volume III-3, 2016 Fgure 6. Comparson wth the other mage sttchng algorthms on the Raltracks mages. From frst row to last row, the results are: ICE, APAP, SPHP, SPHP+APAP, and our approach. The expermental analyss shows that the proposed method can acheve accurate algnment n the overlappng regons as well as reduce dstortons and preserve perspectve. It acheves the best sttchng performance compared wth the other methods. For the future work, t s of nterest to nvestgate the use of more comprehensve local geometrc transform, such as that affne transformaton nduced by usng local geometres (Xa et al., 2014). ACKNOWLEDGEMENT Ths research was supported by the Natonal Natural Scence Foundaton of Chna under contract No and No , and was partally funded by the Wuhan Muncpal Scence and Technology Bureau, wth Chen-Guang Grant REFERENCES Brown, M. and Lowe, D.G., Automatc panoramc mage sttchng usng nvarant features. Internatonal Journal of Computer Vson, 74(1): Chang, C., Sato, Y. and Chuang, Y., Shape-Preservng Half-Projectve Warps for Image Sttchng. In: IEEE Conference on Computer Vson and Pattern Recognton (CVPR), Columbus, USA. pp Chon, J., Km, H. and Ln, C., Seam-lne determnaton for mage mosackng: A technque mnmzng the maxmum local msmatch and the global cost. ISPRS Journal of Photogrammetry and Remote Sensng, 65(1): Chum, O., Pajdla, T. and Sturm, P., The geometrc error for homographes. Computer Vson and Image Understandng, 97(1): do: /sprsannals-iii

8 ISPRS Annals of the Photogrammetry, Remote Sensng and Spatal Informaton Scences, Volume III-3, 2016 Gao, J., Km, S.J. and Brown, M.S., Constructng Image Panoramas usng Dual-Homography Warpng. In: IEEE Conference on Computer Vson and Pattern Recognton (CVPR), Colorado Sprngs, USA. pp Kmmel, R., Klette, R., Sugmoto, A., Chum, O.E. and Matas, J.Í., Planar Affne Rectfcaton from Change of Scale. In: 10th Asan Conference on Computer Vson, Queenstown, New Zealand. pp Ln, C., Pankant, S.U., Ramamurthy, K.N. and Aravkn, A.Y., Adaptve As-Natural-As-Possble Image Sttchng. In: IEEE Conference on Computer Vson and Pattern Recognton (CVPR), Boston, USA. pp Ln, W., Lu, S., Matsushta, Y., Ng, T. and Cheong, L., Smoothly Varyng Affne Sttchng. In: IEEE Conference on Computer Vson and Pattern Recognton (CVPR), Colorado Sprngs, USA. pp Lowe, D. G., Dstnctve mage features from scalenvarant keyponts. Internatonal Journal of Computer Vson, 60(2): Mcrosoft research, Image composte edtor. Szelsk, R., Image Algnment and Sttchng: A Tutoral. In Handbook of Mathematcal Models n Computer Vson. Sprnger, pp Chn T.J., Yu J. and Suter D., Accelerated Hypothess Generaton for Mult-Structure Data va Preference Analyss. IEEE Transactons on Pattern Analyss and Machne Intellgence, 34(4): Zaragoza, J., Chn, T., Brown, M.S. and Suter, D., As- Projectve-As-Possble Image Sttchng wth Movng DLT. IEEE Conference on Computer Vson and Pattern Recognton (CVPR), Portland, USA. pp Zaragoza, J., Chn, T.J., Tran, Q.H., Brown, M.S. and Suter, D., As-Projectve-As-Possble Image Sttchng wth Movng DLT. IEEE Transactons on Pattern Analyss and Machne Intellgence, 36(7): Zhang, F. and Lu, F., Parallax-tolerant mage sttchng. IEEE Conference on Computer Vson and Pattern Recognton (CVPR), Columbus, USA. pp Xa, G.-S., Delon, J. and Gousseau Y., 2014, Accurate Juncton Detecton and Characterzaton n Natural Images, Internatonal Journal of Computer Vson, 106 (1): do: /sprsannals-iii