The plastic coupled map lattice: A novel image-processing paradigm.

Coupled map lattices (CML) can describe many relaxation and optimization algorithms currently used in image processing. We recently introduced the "plastic-CML" as a paradigm to extract (segment) objects in an image. Here, the image is applied by a set of forces to a metal sheet which is allowed to undergo plastic deformation parallel to the applied forces. In this paper we present an analysis of our "plastic-CML" in one and two dimensions, deriving the nature and stability of its stationary solutions. We also detail how to use the CML in image processing, how to set the system parameters and present examples of it at work. We conclude that the plastic-CML is able to segment images with large amounts of noise and large dynamic range of pixel values, and is suitable for a very large scale integration (VLSI) implementation.

[1]  Tomaso A. Poggio.  Early vision: From computational structure to algorithms and parallel hardware , 1985, Comput. Vis. Graph. Image Process..

[2]  Demetri Terzopoulos,et al.  Multilevel computational processes for visual surface reconstruction , 1983, Comput. Vis. Graph. Image Process..

[3]  L. Kantorovich,et al.  Approximate methods of higher analysis , 1960 .

[4]  Tomaso Poggio,et al.  Computational vision and regularization theory , 1985, Nature.

[5]  Andrew Blake,et al.  Visual Reconstruction , 1987, Deep Learning for EEG-Based Brain–Computer Interfaces.

[6]  Kunihiko Kaneko,et al.  Spatiotemporal chaos in one-and two-dimensional coupled map lattices , 1989 .

[7]  W. Eric L. Grimson,et al.  An implementation of a computational theory of visual surface interpolation , 1983, Comput. Vis. Graph. Image Process..

[8]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[9]  W E Grimson,et al.  A computational theory of visual surface interpolation. , 1982, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[10]  B. Hao,et al.  Directions in chaos , 1987 .

[11]  C Koch,et al.  Analog "neuronal" networks in early vision. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[12]  Kapral,et al.  Domain growth and nucleation in a discrete bistable system. , 1987, Physical review. A, General physics.