GENERATING ORTHOIMAGES FOR CLOSE-RANGE OBJECTS BY AUTOMATICALLY DETECTING BREAKLINES

Transcription

1 GENEATING OTHOIMAGES FO CLOSE-ANGE OBJECTS BY AUTOMATICALLY DETECTING BEAKLINES Efstrtios Stylinidis 1, Lzros Sechidis 1, Petros Ptis 1, Spiros Sptls 2 Aristotle University of Thessloniki 1 Deprtment of Cdstre Photogrmmetry nd Crtogrphy 2 Deprtment of Geodesy nd Surveying Univ. Box 473, G-546, Thessloniki, Greece sstyl@topo.uth.gr, lziks@inme.com, ptis@topo.uth.gr Close-rnge ojects (industril, rchitecturl etc) re highly chrcterized y rupt chnges of their surfce continuity. Thus no DEM (nd thus orthoimge) of high qulity cn e generted unless these discontinuities re tken into ccount. Fortuntely, these reklines re mostly stright lines, nd thus their utomtic detection is not very difficult. Besides, their detection cn lso led to informtion out the orienttion of the imges, esing down the necessity for excessive control points. This pper focuses in how these useful grphicl elements (reklines) cn e detected nd extrcted in wy tht the user s intervention will e minimum. The developed lgorithm extrcts stright-line reklines from stereo model nd through mtching process etween ll reklines endpoints, 3D oject model is constructed. 1. INTODUCTION In rchitecturl photogrmmetric pplictions, lrge numer of structures re presented in the imges. Minly these structures re formed y stright-line elements nd usully re highly chrcterized y rupt chnges of their surfce continuity. The discontinuities denote edges in the imge nd therefore reklines in the oject nglyph. The development of tools for the detection of such interesting grphicl entities is surely very useful. This pper presents prts of reserch designed to meet the need for utomtic rekline detection nd DTM genertion. 2. BEAKLINES AND THEI USE Figure 1: Breklines in the surfce model Wht is rekline? How cn rekline e defined? These re two sic questions tht should e firstly nswered nd clered in order to understnd the importnce nd the usefulness of reklines. Breklines re defined s those lines or curves tht they rise y the rupt chnges of the oject surfce continuity. They cn e defined s open or closed grphicl ojects. A surfce dis-

2 continuity is set of points where the nglyph presents drmtic chnge of its form. They re commonly referred s jump edges or occlusion oundries. In Figure 1 it is shown clerly how reklines re defined with the hevy lck colored line nd how the surfce model is divided in homogeneous res ordered y the stright-line reklines. There is n incresing demnd for rpid genertion of surfce models, nd reklines is vitl prmeter for n ccurte surfce model reconstruction. Orthoimge production requires known surfce, wht is usully reported s DTM, DSM or DEM. It is very importnt to initilly locte the reklines in DTM collection ecuse of the demnd to hve n pproximte representtion of the surfce model. Especilly in the mtching process for utomtic DTM genertion, lrge mount of gross errors in point loction, occur due to the fct tht priori knowledge of reklines position does not exist. When no reklines exist, continuous model is considered nd this is d condition when trying to reconstruct n oject surfce. In such cses, the mtching results re not sfe, especilly in the res where reklines relly exist. However, when reklines re locted in the oject spce, the dditionl product of 3D wirefrme model is creted s well. This is importnt informtion especilly in cses of rchitecturl ojects where it cn e used for imge orienttion even for single imge rectifiction. The ove considertions indicte tht it is vitl in oject surfce reconstruction to detect reklines firstly in order to hve relile results. Besides, the dditionl product of 3D model tht is creted, must not e ignored s well. 3. THE STATEGY FO 3D OBJECT ECONSTUCTION In the next few lines the whole strtegy for 3D oject reconstruction, i.e. rekline extrction is presented. Ech one of the following prgrphs, refer to the steps of the process tht schemticlly presented in Figure Imge processing It is ovious tht in the originl imge, plethor of dt exists. Sometimes this sitution is not desirle. In order to reduce this dt to useful informtion n edge opertor (like the grdient opertor) is used nd thus n edge imge is creted. It is cler tht using n edge imge is n effective wy to detect edges nd therefore find the reklines (Step I). 3.2 Approximtions Certinly, the idel cse is the utomtic extrction of true reklines from imges. As it is shown in the edge imge, lot of useless informtion remins nd thus no lgorithm cn understnd wht is relly rekline, wht is useful nd wht is necessry for the rekline reconstruction. Tht is why it hs een chosen to set up n pproximtion position of rekline in the imge model. In this cse n pproximte position of every rekline is given in the left imge of the stereo pir (Step II).

3 Originl imge Edge imge 2D imge model Approximte rekline position 3D pproximte oject model TUE Epipolr line x mx x min The projected 2D model Mtching process 3D oject model Figure 2: The strtegy for 3D oject reconstruction

4 3.3 Hough Trnsform - 2D imge model All pirs of djcent pproximte points defined from the previous step, re defining res tht my contin stright line nd thus n pproximte length nd the orienttion for ech line segment re known. A tch Hough trnsform process is pplied in ll these res nd s result hough lines re extrcted from the imge. Ech pir of djcent hough lines gives n intersection point, nd the linkge of these points gives the 2D imge model (Step III) D pproximte oject model Projection to right imge The interior, the reltive nd the exterior orienttion of the stereo model, re ssumed to e known. Using inverse collinerity equtions (Eqution 1.1), XYZ ground coordintes re clculted for ech corner rekline point of the 2D imge model. Therefore, n pproximte 3D model is generted, nd using the direct collinerity equtions (Eqution 1.2), the pproximte 3D model cn e projected to the other imge (Step IV). X = X Y = Y + + ( ) ( x ) ( ) 11 + y Z Z ( x ) + ( y ) 13 ( ) ( x ) ( ) 12 + y 22 c Z Z ( x ) 13 + ( y ) 23 c c c (1.1) x y = x = y c c ( X X ) + 12 ( Y Y ) + 13( Z Z ) ( X X ) + ( Y Y ) + ( Z Z ) ( X X ) + 22 ( Y Y ) + 23( Z Z ) ( X X ) + ( Y Y ) + ( Z Z ) (1.2) The ij is the element of the 3 3 rottion mtrix, X,Y,Z define imge position in the ground coordinte system, x,y,c define the interior orienttion nd (x,y ), (x,y ) re the imge point coordintes in the left nd right imge respectively. The Z vlue is ssumed to e in the rnge of minimum (Z 1 ) nd mximum (Z 2 ) vlue in the oject spce. This condition gives two extreme pirs of (x,y) vlues, (x min,y min ) nd (x mx,y mx ) nd defines the serching re for mtching. It is ovious tht the projected 2D model in the second imge is not presented in its true position due to the fct tht the Z coordinte tken into ccount is not the rel, ut n pproximte one. 3.5 Mtching 3D oject model The y-vlues for the serching re re not tken into ccount, ecuse of the epipolrity constrin which is defined y Eqution 1.3

5 ( y y ) w + c v [( x ) w + c u ] + [( x ) v ( y ) u ] (1.3) y z = where, u v w = = = ( x ) + 12 ( y ) 13 c ( x ) + 22 ( y ) 23 c ( x ) + ( y ) c (1.4) (for ij in Eqution 1.4, ngles re considered to e retrieved ccording to the reltive orienttion) The criticl prmeter in the mtching process is the definition of serching long the epipolr line. The serching re long epipolr line is defined y x x x (1.5) min true mx The itertive mtching process for ech rekline corner, etween the left nd the right imge, leds to the true loction of ll conjugte points (Step V). In Figure 3 it is Brekline corner (Templte) Serching window long the epipolr line Epipolr line () Prt of the left imge () Prt of the right imge shown how rekline corner is defined (templte window) nd in Figure 3 how mtching process works in the serching window long the epipolr line. Once the series of rekline points re mtched, photogrmmetric intersection process is pplied to clculte the oject coordintes for ll rekline corners. Thus the 3D oject model is creted (Step VI). 5. DISCUSSION - CONCLUSIONS Figure 3: Mtching process Breklines re very useful to e extrcted from imges for lots of photogrmmetric resons nd tht is why n incresing demnd for rpid nd ccurte detection of reklines exists. Firstly, through the rekline detection, correct DTM is fesile. The existence of reklines denotes pproximtion for the surfce model nd this is useful condition for ccurte DTM clcultion. Thus n orthoimge genertion is fesile s well.

6 Secondly, y-product of the ove process, eqully importnt, is the reconstruction of the 3D oject model. A third y-product is the possiility to use the reconstructed 3D wirefrme model s source for computing imge exterior orienttion, in the cse where no control points re ville. EFEENCES Ackermn, F., Digitl Imge Correltion: Performnce nd Potentil Appliction in Photogrmmetry, Photogrmmetric ecord, 11(64), pp (1984) Admos, C. nd W. Fig, Hough Trnsform in Digitl Photogrmmetry, Interntionl Archives of Photogrmmetry nd emote Sensing, Wshington, USA, Vol. 29, Prt B3, pp (1992) Gruen, A. nd E. Bltsvis, High-Precision Imge Mtching for Digitl Terrin Model Genertion, Photogrmmetri (PS), 42, pp (1987) Stylinidis, E. nd P. Ptis, Semi-Automtic Interest Line Extrction in Close-rnge imges, Interntionl Archives of Photogrmmetry nd emote Sensing, Thessloniki, Greece, Vol. XXXII, Prt 5W11, pp (1999) Stylinidis, E. nd P. Ptis, Hough Trnsform in Line Extrction, Interntionl Archives of Photogrmmetry nd emote Sensing, Amsterdm, The Netherlnds, Vol. XXXIII, Prt B5, pp (2)