a) Change Detection Using Joint Intensity Histogram Yasuyo KITA a) 2 (0 255) (I 1 (x),i 2 (x)) I 2 = CI 1 (C>0) (I 1,I 2 ) (I 1,I 2 ) 2 1. [1] 2 [2] [3] [5] [6] [8] Intelligent Systems Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), AIST Tsukuba Central 2, 1 1 1 Umezono, Tsukuba-shi, 305 8568 Japan a) E-mail: y.kita@aist.go.jp [9] [11] 2 [12] 2 [6] [9] 1 D Vol. J90 D No. 8 pp. 1957 1965 c 2007 1957
2007/8 Vol. J90 D No. 8 Stauffer [7] 2 2 I 1 I 2 2 (I 1(x),I 2(x)) 2 [13] I 2 = CI 1 (C >0) (I 1,I 2) (I 1,I 2) 3. 2. 2. 1 1 1 Field Monitoring Server FMS [14], [15] (e) (f) (g) (h) 1 Fig. 1 Relation between subtraction processes and combinatorial intensity levels. 7 6 11 59 1 2 I 1(x) ai 2(x) a =( n I1(x)/n)/( n I2(x)/n) i=1 i=1 I k x n k (0 255) 1 1958
30 2 1(e) 1 1(f) I 1I 2 2 I 1(x) (ai 2(x) 30) (ai 2(x) + 30) I 1I 2 1(g) (I 1,I 2) (I 1,I 2) 1(h) 1(e) 2 I 2 = I 1 C I 2 = CI 1 C C m I 1 I 2 N(C m,σ) I 1 I 2 = C mi 1 N(a, b) a b 2 1 I 2/I 1 I 2/I 1 =0.96 I 2 =0.96I 1 (I 1,I 2) 2 (I 1,I 2) 2 2 Fig. 2 Example of changed area based on combinatorial intensity levels. 2. 2 I 1 I 2 N(C m,σ) I 1 1 I 2 =0.6I 1 C 3 I 2 = CI 1 (C >0) 2 2 1959
2007/8 Vol. J90 D No. 8 3 Fig. 3 Diagram of distribution of various changes. 1 2 3 4 3 3 1 2 3 4 I II III (I 1,I 2) i 4 σ =3.0 1 I 1I 2 3 I 2 = CI 1 I 1I 2 (r, θ) r θ 0 90 4 r =240 4 1.0 4 Fig. 4 Changed Pixels obtained based inference of background clusters. 3. r =1 256 2 I 1I 2 4 I 1I 2 ii L1 D1 0 255 (0, 0) (255, 255) iii (I 1,I 2) Sig(I 1,I 2) Sig(I 1,I 2)=0 if(i 1,I 2) is included in selected clusters. Sig(I 1,I 2)=1 else 1960
(I 1(x),I 2(x)) σ L1 D1 σ L1 D1 I 2 = C mi 1 4 Sig(I 1,I 2)=0 4 2. 3 4 2 N1 N1 N1 = 300 x y S e = min(e x,e y) n i=1 E x = (e1,x(x) µe 1,x)(e 2,x(x) µ e2,x ) n(max(σ e1,x,σ e2,x )) 2 n i=1 E y = (e1,y(x) µe 1,y )(e 2,y(x) µ e2,y ) n(max(σ e1,y,σ e2,y )) 2 e i,k µ ei,k σ ei,k i k S e 0.0 S e 0.1 5 Fig. 5 Discrimination using gradient correlation. 0.1 < S e 0.3 0.3 < S e 1.0 2 S e T 1 T 1 5 5 3. FMS 100 3. 1 3 σ L1 D1 3.0 5(pixels) 30 (deg) 6 1961
2007/8 Vol. J90 D No. 8 6 Fig. 6 Results examples under various conditions. 1 1 2 2 3 2 I 2/I 1 2 0.5 <I 2/I 1 < 1.4 SIG(I 1,I 2) 10 100 50 80 1962
2 I 2/I 1 2 3 3 1 3 4 6 7 Sig(I 1,I 2)=0 1.0 ±3σ 7(e) ±3σ 3. 2 3. 1 2. 3 8 N1 300 8 5 (e) (f) 7 Fig. 7 Example of failure. (e) (f) 8 1 Fig. 8 Experimental results 1. 1963
2007/8 Vol. J90 D No. 8 4. 9 2 Fig. 9 Experimental results 2. 8 1 30 2 8 8(e) Sig(I 1,I 2) 9 14 32 14 42 T 1=0 9 9 Intel/Xeon 2.4GHz 100 ms 200 1000 ms Bromiley [16] (I 1,I 2) (I 1,I 2) / 3 3 σ L1 D1 2 N1 S e 3 1964
[1] R.J. Radke, S. Andra, O. Al-Kofahi, and B. Roysam, Image change detection algorithms: A systematic survey, IEEE Trans. Image Process., vol.14, no.3, pp.294 307, 2005. [2] B. Zitová and J. Flusser, Image registration methods: A survey, Image Vis. Comput., vol.21, no.11, pp.977 1000, 2003. [3] K. Toyama, J. Krumm, B. Brumit, and B. Meyers, Wallflower: Principles and practice of background maintenance, Proc. International Conference on Computer Vision, pp.255 261, 1999. [4] D-II vol.j86-d-ii, no.6, pp.796 806, June 2003. [5] M. Pic, L. Berthouze, and T. KuritaB, Adaptive background estimation: Computing a pixel-wise learning rate from local confidence and global correlation values, IEICE Trans. Inf. & Syst., vol.e87-d, no.1, pp.50 57, Jan. 2004. [6] D-II vol.j79-d-ii, no.4, pp.568 576, April 1996. [7] C. Stauffer and E. Grimson, Adaptive background mixture models for real-time tracking, Proc. of Compurt Vision and Pattern Recognition 99, pp.246 252, 1999. [8] watershed D-II vol.j84-d-ii, no.12, pp.2541 2555, Dec. 2001. [9] D-II vol.j84-d-ii, no.10, pp.2201 2211, Oct. 2001. [10] vol.44, no.sig 5 (CVIMn 6), pp.54 63, 2003. [11] D-II vol.j87-d-ii, no.5, pp.1062 1070, May 2004. [12] Radial Reach Filter RRF D- II vol.j86-d-ii, no.5, pp.616 624, May 2003. [13] F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, and P. Suetens, Multimodality image registration by maximization of mutual information, IEEE Trans. Med. Imaging, vol.16, no.2, pp.187 198, 1997. [14] http://model.job.affrc.go.jp/fieldserver/ FieldServerEn/default.htm [15] T. Fukatsu and M. Hirafuji, Field monitoring using sensor-nodes with a Web server, J. Robotics and Mechatronics, vol.17, no.2, pp.164 172, 2005. [16] P.A. Bromiley, N.A. Thacker, and P. Courtney, Nonparametric image subtraction using grey level scattergrams, Image Vis. Comput., vol.20, pp.609 617, 2002. 18 10 12 19 1 19 1982 1997 1998 Oxford 2005 IEEE CS 1965