Automatic Detection of Circular Objects by Ellipse Growing Mitsuo OKABE, Kenichi KANATANI, and Naoya OHTA 1. [4], [5], [18], [19] [14], [17] [28], [32] [6], [21], [27] Fujitsu Cadtech, Ltd., Oyama-shi, 323-0807 Japan Department of Information Technology, Okayama University, Okayama-shi, 700-8530 Japan Department of Computer Science, Gunma University, Kiryushi, 376-8515 Japan 5 5 5 3 2 3 2 [2], [3], [7] [10], [22] [26], [31], [33] [39] D II Vol. J85 D II No. 12 pp. 1823 1831 2002 12 1823
2002/12 Vol. J85 D II No. 12 edge detection voting for the center of the circle voting for the radius of the circle ellipse growth outlier detection ellipse fitting Does the edge segment mostly cover the ellipse? no yes search for the other segment end Fig. 1 1 Flowchart of the procedure. [2] 2 [30] 3 2 1 LMedS [29] 1 2. 2. 1 Sobel 1 8 8 Fig. 2 2 The evolute of an ellipse and its singularities. 61 2. 2 [11] 4 2 [1] 2 4 3 [16] P k 2 P C r 3(a) C γr k k = 30, γ = 1/10 1/ r 1 4 5 3. 2. 3 C 1 3 1824
3 Fig. 3 (a) (b) (c) (a) P k 2. (b) C R e R2 /2s 2 cos φ R (c) δ (a) Voting around the center of the circle passing through P and the two points away from it by k pixels on both sides. (b) A pixel away from the estimated center C by distance R votes the value R with weight e R2 /2s 2 cos φ. (c) Fitting an ellipse to the longest edge segment inside the region within distance δ from the estimated ellipse. C P R [11] R 1 e R2 /2s 2 cos φ 2 φ P Sobel CP 3(b) s C R s 1/4 R ±1 e 1/2 R 1 ) e R2 /2s 2 C s cos φ 0 C 0 6 3. 2. 4 δ 2 e x2 3(c) [12], [13], [15] 3 [18] Ax 2 +2Bxy+Cy 2 +2f(Dx+Ey)+f 2 F =0 (1) f AC B 2 > 0 (2) x Q x/f A B D x = y/f, Q = B C E (3) 1 D E F (1) [14], [16] (x, Qx) = 0 (4) (a, b) a, b 3 http://www.ail.cs.gunma-u.ac.jp/labo/program.html 1825
2002/12 Vol. J85 D II No. 12 δ Q (+), Q ( ) 1 S c c ( A S = B B C ), c = c = (c, S 1 c) F (5) 2 S λ 1, λ 2 ( D E u 1, u 2 3 1, 2 1 = 2 = c ( c/λ 1 ± δ/f) 2 c (6) ( c/λ 2 ± δ/f) 2 4 S (+), S ( ) ( ) S (±) = U 1 2 ) U (7) U u 1, u 2 2 2 5 Q (+), Q ( ) Q (±) = ( S (±) S (±) S 1 c (S (±) S 1 c) F +(c, S 1 (S (±) S)S 1 c) (8) (4) δ (x, Q (+) x)(x, Q ( ) x) < 0 (9) (1) 2 δ = 4 (2) δ 1 δ = 10 2. 5 LMedS [29] ) 4 Fig. 4 Judging if the edge segment covers more than half of the ellipse. 4 (3) {x α }, α = 1,..., N Q m = O, S m = 1 {x α } 5 2 5 Q (1) A, B,..., F (2) 3 S = med N (x α, Qx α ) 2 α=1 (10) P k Qx α 2 P k = diag(1, 1, 0) 1, 1, 0 Q x α [15] 4 S < S m Q m Q, S m S 10 S > = S m x α (x α, Qx α) 2 < 10Sm (11) P k Qx α 2 [29] σ σ 1.4826 S m Q 2.13σ e 2. 6 4 1826
(a) (b) (c) 5 (a) (b) (c) Fig. 5 Estimation of the center of the initial circle. (a) The detected circle and the fitted ellipse. (b) Voting for the center. (c) Simple Hough transform. 0 100 200 300 400 R (a) 0 100 200 300 400 R 6 R (a) (b) Fig. 6 The number of votes (ordinate) for the radius R of the initial circle (abscissa). (a) Proposed method. (b) Simple voting. (b) e 4 1 + γ (6) 1 = λ 1 (1 ± γ) 2, λ(±) 2 = λ 2 (1 ± γ) 2 (12) 1 ± γ γ = 1.2 20 e e 4, e 1 e, e (10) 100 4 e, e (11) 3. 5(a) 5(b) 5(c) 1 [11] 6(a) R 6(b) ±1 1 [11] 6(a) 0 7(a) 1827
2002/12 Vol. J85 D II No. 12 (a) (b) 7 Fig. 7 (a) (b)(1) (2) (3) (4) (a) The edge image used. (b) (1) The initial circle, (2) the hyperbola resulting from the ellipse growing, (3) the ellipse fitted by LMedS, and (4) the ellipse fitted after detecting another segment. δ = 10 7(b) (1) (2) (3) (4) 8 1, 2 4, 5 500 333 18 CPU Pentium III 600MHz OS Linux [32] 9 768 512 700 6.45cm 59.4 27.3cm [17], [28], [32] 4. 1 2 (2) No. 13680432 [1],,, 1976. [2],,,, PRMU98-123, Nov. 1998. [3] Y. C. Cheng and S. C. Lee, A new method for quadratic curve detection using K-RANSAC with acceleration techniques, Pattern Recognit., vol.28, no.5, pp.663 682, May 1995. [4] W. Chojnacki, M. J. Brooks, and A. van den Hen- 1828
論文 楕円成長法による円形物体の自動検出 図 8 当てはめ例 上段はエッジ画像 下段は初期円と当てはめた楕円を原画像に重ねた もの Fig. 8 Examples of ellipse fitting: the edge images (upper rows); the initial circles and the fitted ellipses superimposed on the original images (lower rows). 1829
2002/12 Vol. J85 D II No. 12 Fig. 9 9 Calibration of a turntable. gel, Rationalising the renormalisation method of Kanatani, J. Math. Imaging Vision, vol.14, no.1, pp.21 38, Feb. 2001. [5] W. Chojnacki, M. J. Brooks, A. van den Hengel, and D. Gawley, On the fitting of surfaces to data with covariances, IEEE Trans. Pattern Anal. & Mach. Intell., vol.22, no.11, pp.1294 1303, Nov. 2000. [6] D. B. Cooper and N. Yalabik, On the computational cost of approximating and recognizing nose-perturbed straight lines and quadratic arcs in the plane, IEEE Trans. Comput., vol.25, no.10, pp.1020 1032, Oct. 1976. [7],,, θ-ρ, D-II, vol.j74-d-ii, no.9, pp.1184 1191, Sept. 1991. [8] N. Guil and E. L. Zapata, Lower order circle and ellipse Hough transform, Pattern Recognit., vol.30, no.10, pp.1729 1744, Oct. 1997. [9] C.-T. Ho and L.-H. Chen, A fast ellipse/circle detector using geometric symmetry, Pattern Recognit., vol.28, no.1, pp.117 124, Jan. 1995. [10] C. L. Huang, Elliptical feature extraction via an improved Hough transform, Pattern Recognit. Lett., vol.10, no.2, pp.93 100, Feb. 1989. [11] D. Ioannou, W. Huda, and A. F. Laine, Circle recognition through a 2D Hough Trnasform and radius histogramming, Image Vision Comput., vol.17, pp.15 26, Jan. 1999. [12],,, vol.35, no.2, pp.201 209, Feb. 1994. [13],,, 1995. [14] K. Kanatani, Geometric Computation for Machine Vision, Oxford University Press, Oxford, 1993. [15] K. Kanatani, Statistical Optimization for Geometric Computation: Theory and Practice, Elsevier, Amsterdam, 1996. [16],,, 1998. [17] K. Kanatani and W. Liu, 3-D interpretation of conics and orthogonality, CVGIP: Image Understanding, vol.58, no.3, pp.286 301, Nov. 1993. [18] Y. Kanazawa and K. Kanatani, Optimal conic fitting and reliability evaluation, IEICE Trans. Inf. & Syst., vol.e79-d, no.9, pp.1323 1328, Sept. 1996. [19] Y. Leedan and P. Meer, Heteroscedastic regression in computer vision: Problems with bilinear constraint, Int. J. Comput. Vision, vol.37, no.2, pp.127 150, June 2000. [20] B. Matei and P. Meer, Reduction of bias in maximum likelihood ellipse fitting, Proc. 15th Int. Conf. Pattern Recognit., vol.3, pp.801 806, Barcelona, Spain, Sept. 2000. [21],,,, D-II, vol.j84-d-ii, no.2, pp.287 294, Feb. 2001. [22],,, D-II, vol.j81-d-ii, no.9, pp.2074 2085, Sept. 1998. [23],,, Hough, D-II, vol.j72-d-ii, no.10, pp.1760 1764, Oct. 1989. [24] D. Pao, H. F. Li, and R. Jayakumar, A decomposable parameter space for the detection of ellipses, Pattern Recognit., vol.14, no.12, pp.951 958, Dec. 1993. [25] G. Roth and M. D. Levine, Extracting geometric primitives, CVGIP: Image Understanding, vol.58, no.1, pp.1 22, July 1993. [26] G. Roth and M. D. Levine, Geometric primitive extraction using a genetic alorithm IEEE Trans. Pattern Anal. & Mach. Intell., vol.16, no.9, pp.901 905, Sept. 1994. [27] P. L. Rosin and G. A. W. West, Nonparametric segmentation of curves into various representations, IEEE Trans. Pattern Anal. & Mach. Intell., vol.17, no.12, pp.1140 1153, Dec. 1995. [28] C. A. Rothwell, A. Zisserman, C. I. Marinos, D. A. Forsyth, and J. L. Mundy, Relative motion and pose from arbitrary plane curves, Image Vision Computing, vol.10, no.4, pp.250 262, May 1992. [29] P. J. Rousseeuw and A. M. Leroy, Robust Regression and Outlier Detection, Wiley, New York, 1987. [30], Hough,, vol.32, no.2, pp.179 187, Feb. 1991. [31] S. Tsuji and F. Matsumoto, Detection of ellipses by 1830
a modified Hough transform, IEEE Trans. Comput., vol.27, no.8, pp.777 781, Aug. 1978. [32],,,, 6, pp.291 298,, June 2000. [33],,,, D-II, vol.j82-d-ii, no.12, pp.2221 2229, Dec. 1999. [34],,,, Li-Lavin-Le Master, D-II, vol.j76-d-ii, no.12, pp.2504 2512, Dec. 1993. [35],, Hough, D-II, vol.j73-d-ii, no.2, pp.159 166, Feb. 1990. [36] W.-Y. Wu and M.-J. J. Wang, Elliptical object detection by using its geometric properties, Pattern Recognit., vol.26, no.10, pp.1449 1500, Oct. 1993. [37],,,,, D-II, vol.j72-d-ii, no.7, pp.1009 1016, July 1989. [38] H. K. Yuen, J. Illingworth, and J. Kittler, Detecting partially occluded ellpises using the Hough transform, Image Vision Comput., vol.7, no.1, pp.31 37, Feb. 1989. [39] J. H. Yoo and I. K. Seth, An ellipse detection method from the polar and pole definition of conics, Pattern Recognit., vol.26, no.2, pp.307 315, Feb. 1993. 3 (1), (3) f x y z Q r Q det Q = 1 det Q = 0 2 [14], [16] Q λ 1, λ 2, λ 3 u 1, u 2, u 3 λ 3 < 0 < λ 1 < = λ 2 [14] [ ] λ 2 λ 1 λ 1 λ 3 n = N u 2 ± u 3 (A 1) λ 2 λ 3 λ 2 λ 3 n 1 n 3 < = 0 d = λ 3 1 r. (A 2) x C = Z[Q 1 n] (A 3) Z[ ] Z 1 r C = dx C (n, x C ) (A 4) θ = cos 1 n 3 (A 5) 14 1 21 6 20 1999 2001 1972 1979 IEEE 1983 1985 1987 N[ ] 2 1831