Cellular Automata on FPGAs for Image Processing

Cellular automata (CA) are computational models of physical systems, where space and time are discrete and interactions are local. Specific CA attributes make them ideal for designing complex electronic circuits for the automated image processing. In terms of circuit design and layout, ease of mask generation, silicon-area utilization, and maximization of clock speed, CAs are perhaps one of the most suitable computational structures for hardware realization. In this paper, we present a computational tool designed to create specialized FPGAs that achieve automated image processing such as noise filtering, edge thinning and convex hull detection. The user of the tool specifies the initial parameters and the automation design tool returns the VHDL code needed for the dedicated electronic circuit. Testing the tool using various initial conditions showed that the corresponding CA algorithms have been successfully implemented into hardware.

[1]  Bastien Chopard,et al.  Cellular Automata Modeling of Physical Systems: Index , 1998 .

[2]  Kendall Preston,et al.  Modern Cellular Automata: Theory and Applications , 2013 .

[3]  Paul L. Rosin Training cellular automata for image processing , 2005, IEEE Transactions on Image Processing.

[4]  Tommaso Toffoli,et al.  Cellular Automata as an Alternative to (Rather than an Approximation of) Differential Equations in M , 1984 .

[5]  J. Schwartz,et al.  Theory of Self-Reproducing Automata , 1967 .

[6]  Georgios Ch. Sirakoulis,et al.  A CAD system for the construction and VLSI implementation of Cellular Automata algorithms using VHDL , 2003, Microprocess. Microsystems.

[7]  Stephen Wolfram,et al.  Theory and Applications of Cellular Automata , 1986 .

[8]  Maya Gokhale,et al.  A reconfigurable computing framework for multi-scale cellular image processing , 2007, Microprocess. Microsystems.

[9]  Olu Lafe Cellular Automata Transforms: "Theory And Applications In Multimedia Compression, Encryption, And Modeling" , 2012 .

[10]  Georgios Ch. Sirakoulis,et al.  An FPGA implemented cellular automaton crowd evacuation model inspired by the electrostatic-induced potential fields , 2010, Microprocess. Microsystems.

[11]  Antonios Gasteratos,et al.  Efficient hierarchical matching algorithm for processing uncalibrated stereo vision images and its hardware architecture , 2011 .

[12]  Kai Salomaa,et al.  An Improved Cellular Automata Based Algorithm for the 45-Convex Hull Problem , 2010, J. Cell. Autom..

[13]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Sami Torbey,et al.  Towards a Framework for Intuitive Programming of Cellular Automata , 2009, Parallel Process. Lett..