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30[11] A. Chakraborthy, L. H. Staib, and J. S. Duncan, “Deformable boundary finding in medical images by integrating gradientand region information,” IEEE Transactions on Medical Imaging, vol. 15, no. 6, pp. 859–870, 1996.[12] V. Casselles, R. Kimmel, and G. Sapiro, “Geodesic active contours,” in ICCV, 1995.[13] R. Malladi, J. A. Sethian, and B. C. Vemuri, “Shape modeling with front propagation: A level set approach,” IEEETransactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 2, pp. 158–174, 1995.[14] G. Sapiro, Geometric partial differential equations and image analysis, Cambridge University Press, 2001.[15] N. Paragios and R. Deriche, “Unifying boundary and region-based information for geodesic active tracking,” in IEEEComputer Vision and Pattern Recognition, Forth Collins, Colarado, 1999.[16] O. Amadieu, E. Debreuve, M. Barlaud, and G. Aubert, “Simeltaneous inward and outward curve evolution,” in ICIP,Kobe, Japan, 1999.[17] M. Flickner, H. Sawhney, D. Pryor, and J. Lotspiech, “Intelligent interactive image outlining using spline snakes,” in 28thAsilomar Conference on Signals, Systems and Computers, 1994.[18] T.F. Cootes, A. Hill, C.J. Taylor, and J. Haslam, “Use of active shape models for locating structures in medical images,”Image and Vision computing, vol. 12, pp. 355–365, 1994.[19] M. Jacob, T. Blu, and M. Unser, “A unifying approach and interface for spline-based snakes,” SPIE InternationalSymposium on Medical Imaging: Image Processing, vol. 4322, pp. 340–347, 2001.[20] C. Davatzikos, X. Tao, and D. Shen, “Hierarchical active shape models, using the wavelet transform,” IEEE Transactionson Medical Imaging, 2003.[21] M. E. Leventon, W. E. L. Grimson, and O. Faugeras, “Statistical shape influence in geodesic active contours,” in ComputerVision and Pattern Recognition, 2000.[22] H. Park, T. Schoepflin, and Y. Kim, “Active contour model with gradient directional information: Directional snake,” IEEETransactions on Circuits and systems for video technology, vol. 11, no. 2, pp. 252–256, 2001.[23] M. Figueiredo and J. Leitao, “Bayesian estimation of ventricular contours in angiographic images,” IEEE Transactionson Medical Imaging, vol. 11, no. 3, pp. 416–429, 1992.[24] C. Chesnaud, P. Refregier, and V. Boulet, “Statistical region snake-based segmentation adapted to different physical noisemodels,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 11, pp. 1145–1157, 1999.[25] T. F. Chan and L. A. Vese, “Active contours without edges,” IEEE Transactions on Image Processing, vol. 10, no. 2, pp.266276, 2001.[26] S. C. Zhu and A. Yuille, “Region competition: Unifying snakes, region growing and Bayes/MDL for multiband imagesegmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 8, pp. 884–900, 1996.[27] S. Jehan-Besson, M. Barlaud, and G. Aubert, “A 3-step algorithm using region based active contours for video objectsdetection,” EURASIP Journal on Applied Signal Processing, vol. 6, pp. 572–581, 2002.[28] G. Strang and T. Nguyen, Wavelets and Filter Banks, Wellesley-Cambridge Press, 1996.[29] F. Candocia and J. C. Prince, “Comments on sinc interpolation of discrete periodic signals,” IEEE Transactions on SignalProcessing, vol. 11, pp. 2044–2047, 1998.[30] M. Jacob and M. Unser, “Design of steerable filters for feature detection using a Canny-like criterion,” Submitted to IEEETransactions on Pattern Analysis and Machine Intelligence.[31] N. Paragios and R. Deriche, “Geodesic active regions for supervised texture segmentation,” in International Conferenceon Image Processing, Corfou, Greece, 1999, pp. 688–694.Submitted to IEEE Trans. Image Processing, January 7, 2004.DRAFT
31[32] L.D. Cohen and I. Cohen, “Finite-element methods for active contour models and baloons for 2-D and 3-D images,” IEEETransactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 1131–1147, 1993.[33] F. Precioso and M. Barlaud, “Regular B-spline active contours for fast video segmentation,” in IEEE InternationalConference on Image Processing, Rochester, USA, 2003.[34] I.J. Schoenberg, “Spline functions and the problem of graduation,” Proc. Nat. Acad. Sci., vol. 52, pp. 947–950, 1964.[35] M. Unser, “Splines: A perfect fit for signal and image processing,” IEEE Signal Processing Magazine, vol. 16, no. 6, pp.22–38, 1999.[36] T. Blu and M. Unser, “Quantitative Fourier analysis of approximation techniques: Part II—Wavelets,” IEEE Transactionson Signal Processing, vol. 47, no. 10, pp. 2783–2795, 1999.[37] G. Aubert, M. Barlaud, O. Faugeras, and S.J. Besson, “Image segmentation using active contours: calculus of variationsor shape gradients,” SIAM Journal of Applied Mathematics, 2003.[38] M. Jacob, T. Blu, and M. Unser, “An exact method for computing the area moments of wavelet and spline curves,” IEEETransactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 6, pp. 633–642, 2001.[39] E. Weisstein, “Eric Weisstein’s world of mathematics,” http://mathworld.wolfram.com.[40] W. Rasband, “ImageJ web site,” http://rsb.info.nih.gov/ij/.Submitted to IEEE Trans. Image Processing, January 7, 2004.DRAFT
Page 1 and 2: 1Efficient energies and algorithms
Page 3 and 4: 3• Parametric snakes, where the c
Page 5 and 6: 5(6,8); t=2C¡¡¡¡¡¡¡¡¡¡¡
Page 7 and 8: 71) Gradient magnitude energy: Many
Page 9 and 10: 9(a) Initialization(b) Magnitude-ba
Page 11 and 12: 11The use of this energy assumes tw
Page 14 and 15: 14second term reduces to∫ Mprovid
Page 16 and 17: 16V. EXTERNAL CONSTRAINT ENERGY.As
Page 18 and 19: 18computed as the curve integral∫
Page 20 and 21: 20whereh 1 (l, m, n) =h 2 (l) =∫
Page 22: d22d sOCFig. 6.Computation of the e
Page 25 and 26: 25(a)(b)(c)(d)Fig. 8. Segmentation
Page 27 and 28: 27In the second step, we make use o
Page 29: 29APPENDIX - D: INTEGRAL OF THE TAN

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