Camera Calibration Using Two Concentric Circles

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1 Camera Calibration Using Two Concentric Circles Francisco Abad, Emilio Camahort, and Roberto Vivó Universidad Politécnica de Valencia, Camino de Vera s/n, Valencia 4601, Spain {jabad, camahort, WWW home page: Abstract. We present a simple calibration method or computing the extrinsic parameters (pose) and intrinsic parameters (ocal length and principal point) o a camera by imaging a pattern o known geometry. Usually, the patterns used in calibration algorithms are complex to build (three orthogonal planes) or need a lot o eatures (checkerboard-like pattern). We propose using just two concentric circles that, when projected onto the image, become two ellipses. With a simple mark close to the outer circle, our algorithm can recover the ull pose o the camera. Under the perect pinhole camera assumption, the pose and the ocal length can be recovered rom just one image. I the principal point o the camera has to be computed as well, two images are required. We present several results, using both synthetic and real images, that show the robustness o our method. 1 Introduction In the past two decades, several methods have been proposed or calibrating a camera by taking images o a pattern with known geometry. First in photogrammetry and then in computer vision, researchers have developed methods to recover a camera s extrinsic parameters (position and orientation) and intrinsic parameters (ocal length and principal point). Those methods usually require expensive laboratory settings, or use complex iducials [1]. In order to take the computer vision rom the laboratory to the home user, robust, inexpensive and eective techniques are needed. In this paper, we present an algorithm that easily recovers the pose and the ocal length o a camera by taking a single photo o a simple calibration pattern. We use a pattern made o two concentric circles o known radii, usually printed on a sheet o paper. We show how this pattern can be used in a simple setup to recover the camera parameters. Our method can be applied to camera tracking and related problems like robotics, entertainment and augmented reality. This paper is organized as ollows. The next section presents previous work in the ield o camera calibration with circular markers. Section 3 presents the This work was partially unded by the Programa de Incentivo a la Investigación o the Polytechnic University o Valencia, and by project TIC C03-01 o Spanish Ministry o Science and Technology

2 theoretical model and mathematical oundations o our work. In the ollowing section we present some results o our method, and discuss the tests we run with both synthetic and real data. Our paper inishes with some conclusions and directions or uture work. Previous Work Early work that used conics or computer vision applications was reported in [ 4]. Circular markers have been extensively used in tracking applications due to their robustness properties [6, 7]. Kim et al. [8, 9] proposed a calibration method using two concentric circles. Their algorithm requires some initial inormation about the camera to get an initial value or the intrinsic matrix. They deine a cost unction on the calibration parameters and minimize it. This method only recovers the normal o the marker s supporting plane. Another method that recovers the supporting plane o the circles was proposed in [10]. The method computes the plane s normal and a point on it expressed in camera coordinates. The method assumes that the principal point is at the center o the image. Unlike the previous methods, our algorithm does not require any a priori inormation about the camera parameters to calibrate it. Furthermore, we recover the pose (the ull rotation matrix and the translation vector) using a simple marker. Finally, we also compute the position o the principal point. 3 Calibrating the Camera 3.1 Detecting the Marker Our marker is composed o two concentric circles or radii r 1 and r, and an exterior mark that intersects with a circle o radius r 3 (see Fig. 1). The ellipses can be automatically recovered rom an image by applying standard methods in Computer Vision. Pixel chains are extracted rom the image and ellipses are itted with, e.g., Fitzgibbon s algorithm [5]. See or example [7] or an explanation o an automatic extraction algorithm. To ind the X axis mark, a circle o radius r 3 has to be projected using the same (unknown) camera as the other two. In an Appendix we explain how to project a circle o arbitrary radius concentric to two circles whose projections are known. 3. Pinhole Projection o a Circle The pinhole camera coniguration (assuming zero skew and square pixels) is usually described using an intrinsic parameter matrix (A), that describes the ocal length and principal point (see Fig. ), and an extrinsic parameter matrix (M), that establishes the camera pose (position and orientation) rom a given global coordinate system:

3 Y Yw Xw Zw v u xc u 0 xm r 1 X y x ym yc v 0 r r 3 T Zc Xc Yc Fig. 1. Design o our iducial Fig.. Pinhole camera in the scene 0 u 0 R 11 R 1 R 13 Tx A = 0 v 0 M = R 1 R R 3 Ty. (1) R 31 R 3 R 33 Tz In Fig., the world coordinate system (WCS) has its origin at the center o the concentric circles. Those circles are in the XwYw plane o the WCS, so the Zw axis is perpendicular to them. The projection operator (P), that computes the image pixel coordinates that corresponds to a 3D point in WCS is P = AM. Given a point X in WCS, equation λx = PX computes its homogeneous coordinates in the image coordinate system. The two circles are located on the plane Zw = 0, so we can write: λu p 11 p 1 p 13 p 14 λv = p 1 p p 3 p 4 λ p 31 p 3 p 33 p 34 Xw Yw 0 1 = p 11 Xw + p 1 Yw + p 14 p 1 Xw + p Yw + p 4. p 31 Xw + p 3 Yw + p 34 I we assume that the image coordinate system is centered at the principal point o the image, then u 0 = 0 and v 0 = 0 in (1) and we can write (see [10]): Xw = xt (R T) x t R 3 and Yw = xt (T R 1 ) x t R 3, () where x = [ u v ] t, and M = [ R1 R R 3 T ]. In the WCS, the exterior circle o radius r has coordinates C(Xw,Yw) = X w+y w r = 0. Substituting () in this equation and actoring, we can express the exterior circle in terms o the image coordinate system as ollows: C (x,y) = Ax + Bxy + Cy + Dx + Ey + F = 0. (3)

4 3.3 Recovering the Circles Projected Center Under perspective projection any conic is transormed into another conic. Speciically, circles are transormed into elipses when imaged by a camera. The projected center o the original circle, however, does not generally coincide with the center o the ellipse in the image. The projected center o the circles has to be computed in order to recover the normal to the supporting plane. The direction o the Zw axis in the camera coordinate system (or R 3 ) can be computed as ollows [, 3]: R 13 xc R 3 = ±N Q yc, (4) R 33 where (xc,yc) are the coordinates o the projected circle center in the image coordinate system (see Fig. ), N represents the normalization to a unit vector, and Q is the matrix that describes the ellipse, as deined in [3]: A B/ D/ Q = B/ C E/. (5) D/ E/ F/ Parameters A to F are those deined in (3) and is the ocal length. Two methods to recover the projected center o two concentric circles can be ound in [8] and [9]. In the Appendix we present our own original method. 3.4 Recovering the Pose Each parameter o the ellipse in (3) can be expressed in terms o, /T z and a constant term by substituting (5) in (4) [10]. This derivation uses the properties o the rotation matrices and the ollowing relations derived rom the pinhole camera model in Fig. : The result is Tx = T zxc and Ty = T zyc. (6) α1 α1r (α1 + α)y c + α α 3yc + α3 α 1α α 1α r (α α 3xc + (α1 + α)xcyc + α 1α 3yc) Q α α = r (α1 + α)x c + α 1α 3xc + α 3 α 1(α 1xc + α yc) α 1α 3r α 3(α 3xc + α xcyc α 1yc) (7) α (α 1xc + α yc) α α 3r α 3( α x c + α 1xcyc + α 3yc) (α 1xc + α yc) α3r α3(x c + yc) where: α 1 = Ax c + Byc + D α = Bx c + Cyc + E α 3 = Dx c + Eyc + F.

5 Thereore, (3) can be expressed as: t C (x,y) = Q /Tz G = 0, (8) 1 where G = [ x xy y x y 1 ] t. The unknowns to be computed are and /T z, so we rearrange (8) to leave the constant terms on the right-hand side o the expresion: ([ q11 q 1 q 31 q 41 q 51 q 61 q 1 q q 3 q 4 q 5 q 6 ] ) [ ] t G = [ ] q 13 q 3 q 33 q 43 q 53 q 63 G, (9) Tz where q ij is the element o row i, column j o matrix Q in (7). Given N points o the ellipse in the image we can build an N-degree over-determined system WX = B: W 11 W 1 [ ] B 1 W 1 W B.. =. Tz., (10) W N1 W N B N where W i1, W i and B i are computed using (9) with (x,y) replaced by the coordinates (x i,y i ) o the i-th point on the ellipse. This system can be solved using the least square pseudo-inverse technique: [ ] T z = ( W t W ) 1 W t B. Solving the system leads to and Tz. The components o R 3 can be computed by replacing in (4). Tx and Ty can be recovered rom (6). Following the previous steps we recover the normal to the plane that contains the circles (R 3 ) and the position o the origin o the WCS in camera coordinates (T) (see Fig. ). Fremont [10] proposed a calibration pattern that uses three orthogonal planes to recover the other two axes (Xw and Yw). Instead, we use a single mark on the exterior circle that deines the Xw direction, an idea that has been used beore in marker detection [7]. Given the pixel coordinates o the Xw axis mark in the image, we reproject it onto the plane o the concentric circles. That plane is completely deined by its normal (R 3 ) and a point on it (T). Let the X axis mark position be (r 3,0,0) in WCS, (xm,ym) in image coordinates, and Xm in camera coordinates. Then Xm = µ [ xm ym ] t where µ = D R 13 xm + R 3 ym + R 33,

6 and D = R 3 t T. Having the 3D coordinates o the Xw axis mark given in camera coordinates, and the 3D coordinates o the origin o the WCS, given in camera coordinates as well, the Xw axis (or R 1 ) is deined by Xw = N {Xm T } where N is a normalization operator. Obviously, in a right-handed coordinate system, Yw = Zw Xw, or R = R 3 R Recovering the Principal Point So ar we have assumed that the optical axis o the camera is perectly centered at the image (i.e., the principal point is the center o the image). In this section we remove this assumption and compute the principal point using the results o the previous sections. Due to the error in the estimation o the principal point, reprojecting the original circle using the parameters computed in the previous sections does not produce the ellipses in the image. This misalignment is proportional to the error incurred in the estimation o the position o the principal point. By minizating that error, the principal point can be recovered. When processing a video stream with multiple rames, the principal point can be recovered once and kept ixed or the remaining rames. This is true as long as the internal camera settings are not changed. Once the parameters that deine the projection have been recovered, we can reproject the circle o radius r onto an ellipse in the image. By minimizing the error in the reprojection, a good approximation to the principal point can be computed. We have ound that the error o reprojection can be deined as the distance between the center o the ellipse used or the calibration and the center o the reprojected ellipse. Alternatively, we can deine the error in terms o the angle between the principal axes o those two ellipses. The algorithm would be: 1. Start with an initial guess o the principal point (i.e., the center o the image).. Deine the ellipses and the X axis marker o the image with respect to that principal point. 3. Calibrate the camera. 4. Reproject the original circle (o radius r ) using the parameters obtained in the previous step. 5. Compute the reprojection error and update the working principal point accordingly. Optimization methods like Levenberg-Marquardt [11] (implemented in MIN- PACK) can eiciently ind the D position o the principal point that minimizes the error o reprojection. 4 Validating our Method We have validated our method using both synthetic and real data. We use synthetic data to determine how robust is our method in the presence o noise.

7 14 1 T R 3 Relative error (%) Pixels Fig. 3. Relative errors in the estimations o T and R 3 (Zw) 4.1 Robustness To check the robustness o the algorithm, we project two concentric circles using a known synthetic camera coniguration.then, we perturb the points o the projected circles by adding random noise to their coordinates. We it an ellipse to each set o perturbed points using Fitzgibbon s algorithm [5]. Finally, we compute the camera parameters using these two ellipses. Figure 3 shows the errors that the added noise produces in the recovered normal o the supporting plane (R 3 ) and the translation vector (T). Note that the error incurred is relatively small. We have ound that the system is very robust in the presence o systematic errors, i.e., when both ellipses are aected by the same error (or instance, with a non-centered optical axis). On the other hand, i the parameters o the ellipses are perturbed beyond a certain limit, the accuracy o the results decreases dramatically. 4. Experimental Results In order to validate the computed calibration with real images, we have applied our algorithm to several images taken with a camera. Figure 4 shows an example o the process. First, the ellipses were recovered rom the image and the camera parameters were computed. By using those parameters, we can draw the WCS axes on the image. Furthermore, the marker has been reprojected using the same parameters. The marker seen in the image has the ollowing properties r 1 =.6 cm, r = 5 cm and r 3 = 6.5 cm. 5 Conclusions and Future Work In this paper we introduce a camera calibration technique that uses a very simple pattern made o two circles. The algorithm obtains accurate intrinsic and extrinsic camera parameters. We show that our method behaves in a robust manner in the presence o dierent types o input errors. We also show that the algorithm

8 Fig. 4. Reprojecting the marker and the coordinate system in the images works well with real world images as long as good ellipse extraction and itting algorithms are used. Our work has a lot o applications, particularly in camera tracking and related ields. Our marker is easy to build and use. This makes it particularly well suited or augmented reality and entertainment applications. We are currently working on applications in these two areas. We are also trying to extend our camera model to take into account skew and lense distortion, in order to better approximate the behavior o a real camera. We are exploring the working limits o our algorithm and we are studying techniques to make the results more stable in the presence o noise. Reerences 1. Zhang, Z.: A Flexible New Technique or Camera Calibration. IEEE Trans. Patt. Anal. Machine Intell., vol., no. 11, (000) Forsyth, D., Mundy, et al.: Invariant Descriptors or 3-D Object Recognition and Pose. IEEE Trans. Patt. Anal. Machine Intell., vol. 13, no. 10, (1991) Kanatani, K., Liu, W.: 3D Interpretation o Conics and Orthogonality. CVGIP: Image Undestanding, Vol. 58, no. 3, (1993) Rothwell, C.A., Zisserman, A., et al.: Relative Motion and Pose rom Arbitrary Plane Curves. Image and Vision Computing, vol. 10, no. 4, May (199) Fitzgibbon, A.W., Pilu, M., Fisher, R.B.: Direct Least Squares Fitting o Ellipses. IEEE Trans. Patt. Anal. Machine Intell., vol. 1, no. 5, (1999) Ahn, S.J., Rauh, W., Kim, S.I.: Circular Coded Target or Automation o Optical 3D-Measurement and Camera Calibration. Int. Jour. Patt. Recog. Artiicial Intell., vol. 15, no. 6, (001) Lo pez de Ipin a, D., Mendonc a, P.R.S., Hopper, A.: TRIP: a Low-Cost Vision-Based Location System or Ubiquitous Computing. Personal and Ubiquitous Computing Journal, Springer, Vol. 6, no. 3, (May 00) Kim, J.S., Kweon, I.S.: A New Camera Calibration Method or Robotic Applications. Int. Con. Intelligent Robots and Systems, Hawaii, (Oct 001) Kim, J.S., Kim, H.W., Kweon, I.S.: A Camera Calibration Method using Concentric Circles or Vision Applications. 5th Asian Con. Computer Vision (00) 10. Fremont, V., Chellali, R.: Direct Camera Calibration using Two Concentric Circles rom a Single View. 1th Int. Con. Artiicial Reality and Telexistence (00) 11. More, J.J.: The Levenberg Marquardt algorithm: implementation and theory. Numerical Analysis, G.A. Watson ed., Springer-Verlag (1977)

9 Appendix Given two concentric circles whose projections are known, we show how to project a third circle o known radius using the same projection. A circle C o radius r centered at the origin and located in the plane Z = 0 is deined by: X t CX = [ X Y 1 ] X Y. 0 0 r 1 A projection matrix P projects a circle C onto an ellipse Q by λq = P t CP 1. We compute the dierence between the projection o a circle o radius r + α and the projection o a circle o radius r: λ 1 Q r+α λ Q r = P t (C r+α C r )P 1 = α(α + r)m, (11) where M = q t q and q is the third row o matrix P 1. Thereore, we can write: { Q3 = Q 1 α 1 (α 1 + r 1 )M Q 3 = Q α (α + r )M, where α 1 = r 3 r 1 and α = r 3 r. Using these two equations we can express Q 3 as: Q 3 = kq Q 1 r 3 r r 3 r 1, (1) where k is a scale correcting actor o Q 1 and Q. That actor can be computed applying the rank 1 condition to the ellipses (note that as C r+α C r in equation (11) has rank 1, λ 1 Q r+α λ Q r should have rank 1, too) [9]. Solving or k in Q 1 kq to have a rank 1 matrix leads to a scale correcting actor. Thereore, equation (1) allows us to project a circle o any radius given the projection o two circles, all o them concentric. This process can be used to ind the projected center o the concentric circles as well. As the projected center o a circle is always enclosed in its projected ellipse, i we project circles o smaller and smaller radii, we will be reducing the space where the projected center can be. In the limit, a circle o radius zero should project onto the projected center o the circles. Applying equation (1) to a circle o radius r 3 = 0 results in an ellipse (o radius zero) whose center is at the projected center o the concentric circles (xc,yc). The center o an ellipse in matrix orm is given by [8]: xc = Q (,)Q (1,3) Q (1,) Q (,3) ( Q(1,) ) Q(1,1) Q (,) and yc = Q (,3)Q (1,1) Q (1,) Q (1,3) ( Q(1,) ) Q(1,1) Q (,).

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