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Region-based strategies for active contour models

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Abstract

The variational method has been introduced by Kass et al. (1987) in the field of object contour modeling, as an alternative to the more traditional edge detection-edge thinning-edge sorting sequence. since the method is based on a pre-processing of the image to yield an edge map, it shares the limitations of the edge detectors it uses. in this paper, we propose a modified variational scheme for contour modeling, which uses no edge detection step, but local computations instead—only around contour neighborhoods—as well as an “anticipating” strategy that enhances the modeling activity of deformable contour curves. many of the concepts used were originally introduced to study the local structure of discontinuity, in a theoretical and formal statement by leclerc & zucker (1987), but never in a practical situation such as this one. the first part of the paper introduces a region-based energy criterion for active contours, and gives an examination of its implications, as compared to the gradient edge map energy of snakes. then, a simplified optimization scheme is presented, accounting for internal and external energy in separate steps. this leads to a complete treatment, which is described in the last sections of the paper (4 and 5). the optimization technique used here is mostly heuristic, and is thus presented without a formal proof, but is believed to fill a gap between snakes and other useful image representations, such as split-and-merge regions or mixed line-labels image fields.

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References

  • A. A. Amini, T. E. Weymouth & R. C. Jain, Using dynamic programming for solving variational problems in vision, IEEE trans. Pattern Analysis &Machine Intelligence, vol. 12, no. 9, 1990.

  • J. M. Beaulieu & M. Goldberg, Hierarchy in picture image segmentation-a step-wise optimization approach, IEEE trans. Pattern Analysis & Machine Intelligence, vol. 11, no. 2, pp. 150–163, 1989.

    Google Scholar 

  • M. O. Berger, Snake growing, Lecture Notes in Computer Sciences, vol. 427, edited by O. Faugeras, pp. 571–572, Springer-Verlag, 1990.

  • A. Blake & G. Brelstaff, Computing lightness, Pattern Recognition Letters, vol. 5, pp. 129–138, 1987.

    Google Scholar 

  • P. Cinquin, F. Leitner, I. Marque & S. Lavallée, 2D and 3D segmentation methods based on differential equations and spline-snakes, Proc. Conference on Curves & Surfaces, Chamonix, France, 1990.

  • R. Cipolla & A. Blake, The dynamic analysis of apparent contours, 3rd International Conference on Computer Vision, Osaka, 1990.

  • Cohen, On active contour models & balloons, Computer Vision, Graphics & Image Processing, Image Understanding, vol. 53, n. 2, pp. 211–218, March 1991.

    Google Scholar 

  • P. Fua & Y. Leclerc, Model driven edge detection, Machine Vision & Applications, vol. 3, pp. 45–56, 1990.

    Google Scholar 

  • M. Gage, On an area-preserving evolution equation for plane curves, Contemporary Mathematics, vol. 51, pp. 51–62, 1986.

    Google Scholar 

  • S. Geman & D. Geman, Stochastic relaxation, Gibbs distributions and the restoration of images, IEEE trans. Pattern Analysis & Machine Intelligence, vol. 6, no. 6, 1984.

  • S. Geman, D. Geman, C. Graffigne & P. Dong: Boundary detection by constrained optimization, IEEE trans. Pattern Analysis & Machine Intelligence, vol. 12, no. 7, pp. 609–627, 1990.

    Google Scholar 

  • S. Grossberg, Cortical dynamics of 3-dimensional form, color and brightness perception—monocular theory, Perception & Psychophysics, vol. 41, no. 2, pp. 87–116, 1987.

    Google Scholar 

  • R. M. Haralick, Digital step-edges from zero-crossings of second directional derivatives, IEEE trans. Pattern Analysis & Machine Intelligence, vol. 1, pp. 58–68, 1984.

    Google Scholar 

  • M. Kass, A. Witkins & D. Terzopoulos, Snakes—active contour models, International Journal of Computer Vision, vol. 1, no. 4, pp. 321–330, 1987.

    Google Scholar 

  • B. B. Kimia, A. Tannenbaum & S. W. Zucker, Toward a computational theory of shape : an overview, Lecture Notes in Computer Sciences, vol. 427, pp. 402–407, Springer-Verlag, 1990.

  • J. J. Koenderink & A. J. van Doorn, The Structure of Two-dimensional Scalar Fields with Applications to Vision, Biological Cybernetics, vol. 33, pp. 151–158, 1979.

    Google Scholar 

  • E. H. Land, The retinex theory of color vision, Scientific American, vol. 237, no. 6, pp. 108–129, 1977.

    Google Scholar 

  • P. J. Laurent, Approximation et optimisation, Hermann, Paris, 1972.

    Google Scholar 

  • Y. G. Leclerc & S. W. Zucker, The local structure of image discontinuities in one dimension, IEEE trans. Pattern Analysis & Machine Intelligence, vol. 9, no. 3, pp. 341–355, 1987.

    Google Scholar 

  • F. Leitner, F. Berthommier, T. Coll, I. Marque, D. Francillard, Ph. Cinquin & J. Demongeot, Neural networks, differential systems and segmentation of medical images, in From pixels to features II, ed. by H. Burkhardt, Y. Neuvo & J. C. Simon, Elsevier Science Publishers, pp. 253–274, 1991.

  • Marroquin, Mitter & Poggio, Probabilistic solution of ill-posed problems in computional vision. 1987

  • S. Menet, P. Saint-Marc & G. Medioni, Active contour models: Overview, Implementation and applications, proc. IEEE Conference on Systems, Man & Cybernetics, Los Angeles, California, USA, 1990.

  • D. Mumford & J. Shah, Boundary detection by minimizing functionals, in Proc. International Conference on Computer Vision and Pattern Recognition, pp. 22–26, San Franciso, 1985.

  • D. Mumford & J. Shah, Optimal approximations by piece-wise smooth functions and associated variational problems, Communications on Pure and Applied Mathematics, vol. 42, pp. 577–685, 1989.

    Google Scholar 

  • M. Nitzberg & D. Mumford, The 3.1-D sketch, 3rd International Conference on Computer Vision, Osaka, 1990.

  • P. M. Prenter, Splines and Variational Methods, Wiley Classics Library, 1975, reprinted in 1989.

  • W. H. Press, B. P. Flannery, S. A. Teukolsky & W. T. Vetterling, Numerical Recipes in C, Cambridge University Press, 1988.

  • R. Ronfard, Contours & boundaries in color images, proc. of SPIE Conference on Visual Communication & Image Processing, Lausanne, 1990.

  • R. Ronfard, Principles for variational edge detection in multi-spectral and color images, PhD thesis, Ecole des Mines de Paris, Feb. 1991.

  • J. Shah, Parameter estimation, multi-scale representation and algorithms for energy-minimizing segmentations, in Proc. 10th International Conference on Pattern Recognition, pp. 815–819, Atlantic City, 1990.

  • B. Sharahray & D. J. Anderson, Optimal estimation of contour properties by cross-validated regularization, IEEE trans. on Pattern Analysis & Machine Intelligence, vol. 11, n. 8, Jan. 1989.

  • M. Sigelle & R. Ronfard, “Relaxation of previously classified images by a Markov Field technique and its relationship with statistical physics,” proc. of the 7th Scandinavian Conference on Image Analysis, IAPR, Aalborg, Denmark, August 1991.

  • R. Szeliski & D. Terzopoulos, From splines to fractals, Computer Graphics, vol. 23, no. 3, pp. 51–60, 1989.

    Google Scholar 

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Ronfard, R. Region-based strategies for active contour models. Int J Comput Vision 13, 229–251 (1994). https://doi.org/10.1007/BF01427153

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