Abstract
In this paper we undertake a systematic investigation of affine invariant object detection and image denoising. Edge detection is first presented from the point of view of the affine invariant scale-space obtained by curvature based motion of the image level-sets. In this case, affine invariant maps are derived as a weighted difference of images at different scales. We then introduce the affine gradient as an affine invariant differential function of lowest possible order with qualitative behavior similar to the Euclidean gradient magnitude. These edge detectors are the basis for the extension of the affine invariant scale-space to a complete affine flow for image denoising and simplification, and to define affine invariant active contours for object detection and edge integration. The active contours are obtained as a gradient flow in a conformally Euclidean space defined by the image on which the object is to be detected. That is, we show that objects can be segmented in an affine invariant manner by computing a path of minimal weighted affine distance, the weight being given by functions of the affine edge detectors. The gradient path is computed via an algorithm which allows to simultaneously detect any number of objects independently of the initial curve topology. Based on the same theory of affine invariant gradient flows we show that the affine geometric heat flow is minimizing, in an affine invariant form, the area enclosed by the curve.
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Alvarez, L., Guichard, F., Lions, P. L. and Morel, J. M.: Axioms and fundamental equations of image processing, Arch. Rational Mech. 123 (1993), 199–257.
Alvarez, L., Lions, P. L. and Morel, J. M.: Image selective smoothing and edge detection by nonlinear diffusion, SIAM J. Numer. Anal. 29 (1992), 845–866.
Angenent, S.: Parabolic equations for curves on surfaces, Part II. Intersections, blow-up, and generalized solutions, Ann. of Math. 133 (1991), 171–215.
Angenent, S., Sapiro, G. and Tannenbaum, A.: On the affine heat flow for non-convex curves, J. Amer. Math. Soc. 11 (1998), 601–634.
Ballester, C., Caselles, V. and Gonzalez, M.: Affine invariant segmentation by variational method, SIAM J. Appl. Math. 56 (1996), 294–325.
Blake, A. and Yuille, A.: Active Vision, MIT Press, Cambridge, 1992.
Blaschke, W.: Vorlesungen über Differentialgeometrie II, Springer, Berlin, 1923.
Bookstein, F. L.: Fitting conic sections to scattered data, Comput. Graph. Image Process. 9 (1979), 56–71.
Calabi, E., Olver, P. J. and Tannenbaum, A.: Affine geometry, curve flows, and invariant numerical approximations, Adv. Math. 124 (1996), 154–196.
Calabi, E., Olver, P. J., Shakiban, C., Tannenbaum, A. and Haker, S.: Differential and numerical invariant signature curves applied to object recognition, Internat. J. Computer Vision 26 (1998), 107–135.
Caselles, V., Catte, F., Coll, T. and Dibos, F.: A geometric model for active contours, Numer. Math. 66 (1993), 1–31.
Caselles, V., Kimmel, R. and Sapiro, G.: Geodesic active contours, Internat. J. Computer Vision 22(1) (1997), 61–79. Also in Proc. ICCV, Cambridge, MA, June 1995.
Caselles, V., Kimmel, R., Sapiro, G. and Sbert, C.: Minimal surfaces: A three dimensional segmentation approach, IEEE-PAMI 19(4) (1997), 394–398.
Caselles, V., Kimmel, R., Sapiro, G. and Sbert, C.: Three dimensional object modeling via minimal surfaces, in: Proc. ECCV, Cambridge, UK, April 1996.
Chen, Y. G., Giga, Y. and Goto, S.: Uniqueness and existence of viscosity solutions of generalized mean curvature flow equations, J. Differential Geom. 33 (1991), 749–786.
Chopp, D.: Computing minimal surfaces via level set curvature flows, J. Comput. Physics 106 (1993), 77–91.
Cohen, L. D.: On active contour models and balloons, CVGIP: Image Understanding 53 (1991), 211–218.
Cohen, I., Cohen, L. D. and Ayache, N.: Using deformable surfaces to segment 3D images and infer differential structure, CVGIP: Image Understanding 56 (1992), 242–263.
Cohignac, T., Lopez, C. and Morel, J. M.: Integral and local affine invariant parametrizations and applications to shape recognition, in: Proc. 12th IEEE Int. Conf. Pattern Recognition, Jerusalem, 1994.
Crandall, M. G., Ishii, H. and Lions, P. L.: User's guide to viscosity solutions of second order partial linear differential equations, Bull. Amer. Math. Soc. 27 (1992), 1–67.
Dubrovin, B. A., Fomenko, A. T. and Novikov, S. P.: Modern Geometry — Methods and Applications I, Springer-Verlag, New York, 1984.
Epstein, C. L. and Gage, M.: The curve shortening flow, in: A. Chorin and A. Majda (eds), Wave Motion: Theory, Modeling, and Computation, Springer-Verlag, New York, 1987.
Evans, L. C. and Spruck, J.: Motion of level sets by mean curvature, I, J. Differential Geom. 33 (1991), 635–681.
Faugeras, O.: On the evolution of simple curves of the real projective plane, C.R. Acad. Sci. Paris 317 (1993), 565–570.
Faugeras, O.: Cartan's moving frame method and its application on the geometry and evolution of curves in the Euclidean, affine, and projective planes, in: J. L. Mundy, A. Zisserman and D. Forsyth (eds), Applications of Invariance in Computer Vision, Springer-Verlag, New York, 1994, pp. 11–46.
Faugeras, O. and Keriven, R.: Scale-spaces and affine curvature, in: R. Mohr and C. Wu (eds), Proc. Europe-China Workshop on Geometrical Modeling and Invariants for Computer Vision, 1995, pp. 17–24.
Forsyth, D., Mundy, J. L., Zisserman, A. and Brown C. M.: Projectively invariant representations using implicit curves, Image Vision Comput. 8 (1990), 130–136.
Forsyth, D., Mundy, J. L., Zisserman, A., Coelho, C., Heller, A. and Rothwell, C.: Invariant description of object representation and pose, IEEE PAMI 13 (1991), 971–991.
Fua, P. and Leclerc, Y. G.: Model driven edge detection, Machine Vision Appl. 3 (1990), 45–56.
Gage, M. and Hamilton, R. S.: The heat equation shrinking convex plane curves, J. Differential Geom. 23 (1986), 69–96.
Grayson, M.: The heat equation shrinks embedded plane curves to round points, J. Differential Geom. 26 (1987), 285–314.
Grayson, M.: Shortening embedded curves, Ann. of Math. 129 (1989), 285–314.
Guggenheimer, H. W.: Differential Geometry, McGraw-Hill, New York, 1963.
Gurtin, M. E.: Thermomechanics of Evolving Phase Boundaries in the Plane, Oxford Univ. Press, New York, 1993.
Kass, M., Witkin, A. and Terzopoulos, D.: Snakes: Active contour models, Internat. J. Comput. Vision 1 (1988), 321–331.
Kichenassamy, S., Kumar, A., Olver, P. J., Tannenbaum, A. and Yezzi, A.: Gradient flows and geometric active contour models, in: Proc. ICCV, Cambridge, MA, June 1995, pp. 810–815.
Kichenassamy, S., Kumar, A., Olver, P. J., Tannenbaum, A. and Yezzi, A.: Conformal curvature flows: From phase transitions to active vision, Arch. Rational Mech. Anal. 134 (1996), 275–301.
Kimia, B. B., Tannenbaum, A. and Zucker, S.W.: Toward a computational theory of shape: An overview, Lecture Notes in Comput. Sci. 427 (1990), 402–407.
Kimia, B. B., Tannenbaum, A. and Zucker, S.W.: Shapes, shocks, and deformations, I, Internat. J. Comput. Vision 15 (1995), 189–224.
Kimmel, R.: Invariant framework for differential affine signatures, in: Proc. ICPR '96.
Kimmel, R., Amir, A. and Bruckstein, A. M.: Finding shortest paths on surfaces using level sets propagation, IEEE-PAMI 17(1) (1995), 635–640.
Kimmel, R., Kiryati, N. and Bruckstein, A. M.: Sub-pixel distance maps and weighted distance transforms, J. Math. Imaging and Vision, Special Issue on Topology and Geometry in Computer Vision, 6 (1996), 223–233.
Lindeberg, T.: Scale-Space Theory in Computer Vision, Kluwer Acad. Publ., Dordrecht, 1994.
Lindeberg, T. and Garding, J.: Shape-adapted smoothing in estimation of 3D depth cues from affine distortions of local 2D structures, in: Proc. ECCV, Stockholm, Sweden, May 1994.
Malladi, R. and Sethian, J. A.: A unified approach to noise removal, image enhancement, and shape recovery, IEEE Trans. Image Processing 5 (1996), 1554–1568.
Malladi, R., Sethian, J. A. and Vemuri, B. C.: Evolutionary fronts for topology independent shape modeling and recovery, in: Proc. of the 3rd ECCV, Stockholm, Sweden, 1994, pp. 3–13.
Malladi, R., Sethian, J. A. and Vemuri, B. C.: Shape modeling with front propagation: A level set approach, IEEE Trans. on PAMI 17 (1995), 158–175.
Malladi, R., Sethian, J. A. and Vemuri, B. C.: A fast level set based algorithm for topology independent shape modeling, J. Math. Imaging and Vision 6 (1996), 269–289.
McInerney, T. and Terzopoulos, D.: Topologically adaptable snakes, in: Proc. ICCV, Cambridge, MA, June 1995.
Mumford, D. and Shah, J.: Optimal approximations by piecewise smooth functions and variational problems, Comm. Pure Appl. Math. 42 (1989), 577–685.
Mundy, J. L. and Zisserman, A. (eds): Geometric Invariance in Computer Vision, MIT Press, 1992.
Niessen, W. J., ter Haar Romeny, B.M., Florack, L. M. J. and Salden, A. H.: Nonlinear diffusion of scalar images using well-posed differential operators, in: Proceedings CVPR, IEEE Press, 1994, pp. 92–97.
Olver, P. J.: Applications of Lie Groups to Differential Equations, 2nd edn, Springer-Verlag, New York, 1993.
Olver, P. J.: Equivalence, Invariants, and Symmetry, Cambridge Univ. Press, Cambridge, UK, 1995.
Olver, P. J., Sapiro, G. and Tannenbaum, A.: Differential invariant signatures and flows in computer vision: A symmetry group approach, in [62].
Olver, P. J., Sapiro, G. and Tannenbaum, A.: Affine invariant edge maps and active contours, Geometry Center Technical Report 90, University of Minnesota, October 1995.
Olver, P. J., Sapiro, G. and Tannenbaum, A.: Affine invariant detection: Edges, active contours, and segments, in: Proc. Computer Vision Pattern Recognition, San Francisco, June 1996, pp. 520–525.
Olver, P. J., Sapiro, G. and Tannenbaum, A.: Invariant geometric evolutions of surfaces and volumetric smoothing, SIAM J. Appl. Math. 57 (1997), 176–194.
Osher, S. J. and Sethian, J. A.: Fronts propagation with curvature dependent speed: Algorithms based on Hamilton-Jacobi formulations, J. Comput. Phys. 79 (1988), 12–49.
Pauwels, E. J., Fiddelaers, P. and Van Gool, L. J.: Shape-extraction for curves using geometry-driven diffusion and functional optimization, in: Proc. ICCV, Cambridge, MA, June 1995.
Perona, P. and Malik, J.: Scale-space and edge detection using anisotropic diffusion, IEEE Trans. Pattern Anal. Machine Intell. 12 (1990), 629–639.
Romeny, B. (ed.): Geometry Driven Diffusion in Computer Vision, Kluwer Acad. Publ., Dordrecht, 1994.
Rudin, L. I., Osher, S. and Fatemi, E.: Nonlinear total variation based noise removal algorithms, Physica D 60 (1992), 259–268.
Sapiro, G., Kimmel, R., Shaked, D., Kimia, B. B. and Bruckstein, A. M.: Implementing continuous-scale morphology via curve evolution, Pattern Recog. 26(9) (1993), 1363–1372.
Sapiro, G. and Tannenbaum, A.: On affine plane curve evolution, J. Funct. Anal. 119(1) (1994), 79–120.
Sapiro, G. and Tannenbaum, A.: Affine invariant scale-space, Internat. J. Comput. Vision 11(1) (1993), 25–44.
Sapiro, G. and Tannenbaum, A.: Image smoothing based on an affine invariant flow, in: Proceedings of Conference on Information Sciences and Systems, Johns Hopkins University, March 1993.
Sapiro, G. and Tannenbaum, A.: On invariant curve evolution and image analysis, Indiana Univ. Math. J. 42 (1993), 95–1009.
Sapiro, G. and Tannenbaum, A.: Area and length preserving geometric invariant scale-spaces, IEEE Trans. PAMI 17(1) (1995), 67–72.
Sapiro, G., Tannenbaum, A., You, Y. L. and Kaveh, M.: Experiments on geometric image enhancement, in: First IEEE-International Conference on Image Processing, Austin-Texas, November 1994.
Shah, J.: Recovery of shapes by evolution of zero-crossings, Technical Report, Math. Dept. Northeastern Univ., Boston, MA, 1995.
Soner, H. M.: Motion of a set by the curvature of its boundary, J. Differential Equations 101 (1993), 313–372.
Szeliski, R., Tonnesen, D. and Terzopoulos, D.: Modeling surfaces of arbitrary topology with dynamic particles, in: Proc. CVPR, 1993, pp. 82–87.
Sethian, J. A.: Curvature and the evolution of fronts, Comm. Math. Phys. 101 (1985), 487–499.
Sethian, J. A.: A review of recent numerical algorithms for hypersurfaces moving with curvature dependent flows, J. Differential Geom. 31 (1989), 131–161.
Tek, H. and Kimia, B. B.: Image segmentation by reaction-diffusion bubbles, in: Proc. ICCV, Cambridge, MA, June 1995.
Terzopoulos, D., Witkin, A. and Kass, M.: Constraints on deformable models: Recovering 3D shape and nonrigid motions, Artif. Intell. 36 (1988), 91–123.
Torre, V. and Poggio, T.: On edge detection, IEEE Trans. PAMI 8 (1986), 147–163.
Van Gool, L., Moons, T. and Ungureanu, D.: Affine/photometric invariants for planar intensity patterns, in: Proc. ECCV, Cambridge, UK, April 1996, pp. 642–651.
Weiss, I.: Geometric invariants and object recognition, Internat. J. Comput. Vision (1993), 207–231.
Whitaker, R. T.: Volumetric deformable models: Active blobs, ECRC TR 94–25, 1994.
Whitaker, R. T.: Algorithms for implicit deformable models, in: Proc. ICCV'95, Cambridge, MA, June 1995.
Yezzi, A., Kichenassamy, S., Kumar, A., Olver, P. and Tannenbaum, A.: Geometric snakes for edge detection and segmentation of medical imagery, IEEE Trans. Medical Imaging 16 (1997), 199–210.
Yezzi, A., Kichenassamy, S., Olver, P. and Tannenbaum, A.: A gradient surface approach to 3D segmentation, in: Proceedings of 49th IS&T, Society for Imaging Science and Technology, Springfield, VA, 1996, pp. 305–307.
Zhu, S. C., Lee, T. S. and Yuille, A. L.: Region competition: Unifying snakes, region growing, energy/Bayes/MDL for multi-band image segmentation, in: Proc. ICCV, Cambridge, MA, June 1995.
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Olver, P.J., Sapiro, G. & Tannenbaum, A. Affine Invariant Detection: Edge Maps, Anisotropic Diffusion, and Active Contours. Acta Applicandae Mathematicae 59, 45–77 (1999). https://doi.org/10.1023/A:1006295328209
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DOI: https://doi.org/10.1023/A:1006295328209