Abstract
We propose a new variational illusory shape (VIS) model via phase fields and phase transitions. It is inspired by the first-order variational illusory contour model proposed by Jung and Shen (J Visual Commun Image Represent 19:42–55, 2008). Under the new VIS model, illusory shapes are represented by phase values close to 1 while the rest by values close to 0. The 0–1 transition is achieved by an elliptic energy with a double-well potential, as in the theory of \(\varGamma \)-convergence. The VIS model is non-convex, with the zero field as its trivial global optimum. To seek visually meaningful local optima that can induce illusory shapes, an iterative algorithm is designed and its convergence behavior is closely studied. Several generic numerical examples confirm the versatility of the model and the algorithm.
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Adams, R.A., Fournier, J.J.F.: Sobolev Spaces, 2nd edn. Academic Press, Amsterdam (2003)
Ambrosio, L., Tortorelli, V.M.: Approximation of functionals depending on jumps by elliptic functionals via \({\varGamma }\)-convergence. Commun. Pure Appl. Math. 43, 999–1036 (1990)
Ambrosio, L., Tortorelli, V.M.: On the approximation of free discontinuity problems. Boll. Un. Mat. Ital. 6–B, 105–123 (1992)
Aubert, G., Kornprobst, P.: Mathematical Problems in Image Processing. Springer, New York (2001)
Aubert, G., Vese, L.: A variational method in image recovery. SIAM J. Numer. Anal. 34, 1948–1979 (1997)
Boothby, W.M.: An Introduction to Differentiable Manifolds and Riemannian Geometry, vol. 120, 2nd edn. Academic Press Inc, New York (1986)
Braides, A.: \({\varGamma }\)-Convergence for Beginners. Oxford University Press, Oxford (2002)
Chan, T.F., Shen, J.: Image Processing and Analysis: Variational, PDE, Wavelet, and Stochastic Methods. SIAM Publisher, Philadelphia (2005)
Chan, T.F., Zhu, W.: Capture illusory contours: a level set based approach. UCLA CAM Report 03-65 (2003)
Chan, T.F., Kang, S.-H., Shen, J.: Euler’s elastica and curvature based inpainting. SIAM J. Appl. Math. 63(2), 564–592 (2002)
Chandler, D.: Introduction to Modern Statistical Mechanics. Oxford University Press, New York (1987)
Dal Maso, G.: An Introduction to \(\varGamma \)-Convergence. Birkhauser, Boston (1992)
Folland, G.B.: Real Analysis—Modern Techniques and Their Applications, 2nd edn. Wiley, Hoboken (1999)
Freedman, D., Pisani, R., Purves, R.: Statistics. W. W. Norton and Co, New York (2007)
Fukushima, K.: Neural network model for completing occluded contours. Neural Netw. 23, 528–540 (2010)
Grosof, D.H., Shapley, R.M., Hawken, M.J.: Macaque V1 neurons can signal illusory contours. Nature 365, 550–552 (1993)
Hales, R.: Jordan’s proof of the Jordan curve theorem. Stud. Logic Gramm. Rhetor. 10(23), 45–60 (2007)
Han, F., Zhu, S.C.: Bottom-up/top-down image parsing with attribute graph grammars. IEEE Trans. Pattern Anal. Mach. Intell. 31(1), 59–73 (2009)
Jung, Y.M., Shen, J.: First-order modeling and stability analysis of illusory contours. J. Visual Commun. Image Represent. 19, 42–55 (2008)
Kang, S.-H., Zhu, W., Shen, J.: Illusory shapes via corner fusion. SIAM J. Imaging Sci. 7(4), 1907–1936 (2014)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)
Knill, D.C., Richards, W.: Perception as Bayesian Inference. Cambridge University Press, New York (1996)
Lee, T.S., Mumford, D.: Hierarchical Bayesian inference in the visual cortex. J. Opt. Soc. Am. A 20(7), 1434–1448 (2003)
Lee, T.S., Nguyen, M.: Dynamics of subjective contour formation in the early visual cortex. Proc. Natl. Acad. Sci. USA 98, 1907–1911 (2001)
Léveillé, J., Versace, M., Grossberg, S.: Running as fast as it can: how spiking dynamics form object groups in the laminar circuits of visual cortex. J. Comput. Neurosci. 28, 323–346 (2010)
March, R.: Visual reconstruction with discontinuities using variational methods. Image Vis. Comput. 10, 30–38 (1992)
March, R., Dozio, M.: A variational method for the recovery of smooth boundaries. Image Vis. Comput. 15, 705–712 (1997)
Mumford, D.: Elastica and computer vision. In: Bajaj, C.L. (ed.) Algebraic Geometry and its Applications, pp. 491–506. Springer, New York (1994)
Murray, M.M., Herrmann, C.S.: Illusory contours: a window onto the neurophysiology of constructing perception. Trends Cognit. Sci. 17(9), 471–481 (2013)
Osher, S., Sethian, J.A.: Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. J. Comput. Phys. 79(12), 12–49 (1988)
Sarti, A., Malladi, R., Sethian, J.A.: Subjective surfaces: a geometric model for boundary completion. Int. J. Comput. Vis. 46(3), 201–221 (2002)
Sauer, T.: Numerical Analysis. Pearson, Boston (2011)
Shen, J.: \(\varGamma \)-convergence approximation to piecewise constant Mumford-Shah segmentation. Lect. Notes Comput. Sci. 3708, 499–506 (2005)
Shen, J.: A stochastic-variational model for soft Mumford-Shah segmentation. Int. J. Biomed. Imaging 2006(92329), 1–14 (2006)
Stanley, D.A., Rubin, N.: fMRI activation in response to illusory contours and salient regions in the human lateral occipital complex. Neuron 37, 323–331 (2003)
Vese, L.A.: A study in the BV space of a denoising-deblurring variational problem. Appl. Math. Optim. 44(2), 131–161 (2001)
von der Heydt, R., Peterhans, E.: Mechanism of contour perception in monkey visual cortex. I. Lines of pattern discontinuity. J. Neurosci. 9, 1731–1748 (1989)
von der Heydt, R., Peterhans, E., Baumgartner, G.: Illusory contours and cortical neuron responses. Science 224, 1260–1262 (1984)
Wu, T.F., Zhu, S.C.: A numeric study of the bottom-up and top-down inference processes in and-or graphs. Int. J. Comput. Vis. 93(2), 226–252 (2011)
Yoshino, A., Kawamoto, M., Yhoshida, T., Kobayashi, N., Shigemura, J., Takahashi, Y., Nomura, S.: Activation time course of responses to illusory contours and salient region: a high-density electrical mapping comparison. Brain Res. 1071, 137–144 (2006)
Zhu, W., Chan, T.: Illusory contours using shape information. UCLA CAM Tech. Report 03-09 (2005)
Acknowledgments
Jung has been supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) (2012R1A1A1015492, 2014R1A1A2054763). Shen has been supported by the National Science Foundation (NSF) of USA.
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Dedicated to Gil Strang on the Occasion of His 80th Birthday.
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Jung, Y.M., Shen, J.J. Illusory Shapes via First-Order Phase Transition and Approximation. J Math Imaging Vis 53, 303–313 (2015). https://doi.org/10.1007/s10851-015-0580-1
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DOI: https://doi.org/10.1007/s10851-015-0580-1