## Cellular Image ProcessingCellular operations will play a critical role in designing and programming nano-scale computers. In this book a cellular operation is defined as an operation which uses information within a neighbourhood to perform either local or global computational tasks. Cellular operations can be used to solve complex and computation-intensive problems such as parallel learning. Cellular operations can also be used to simulate and explain different kinds of physical phenomena such as small-world phenomena. Since many cellular computational platforms, such as cellular automata and cellular neural networks are proven to be as universal as the Turing machine, cellular operations can be used to solve any computable problems in Turing sense. Therefore, a cellular computer based on cellular operations can serve as an all-purpose computer. The cellular image operators presented in this book can help the design of image processing tasks for different hardware platforms based on either CPU or cellular processors. This book also provides a powerful toolbox for designing cellular hardware platforms such as nano-scale array processors and VLSI array processors. automata and fuzzy cellular automata can be used to solve engineering problems. On the other hand, this book can help electrical engineers to design software for cellular computers based on either micro-electronics or nano-electronics. This book can also serve as a handbook of parallel image processing for experts from the image processing community. |

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### Contents

LocationInsensitive Local Rules | 13 |

LocationSensitive Local Rules | 51 |

MorphologicalLogical Cellular Image Processing | 69 |

Copyright | |

6 other sections not shown

### Common terms and phrases

aooyij arbitrary boundary condition B*Uij binary images binary source image black objects black pixels CCD CTCNN cellular image processing Cellular logic yij(t cellular operations CIPA Class CIPA This CIPA CTCNN implementation defuzzifier denote dilation DTCNN at iteration DTCNN outputs edge-detecting CIPA fed into Uij fed into Xij(0 filtering CNN final output fixed 1 boundary fuzzy logic fuzzy set fuzzy variable gray values high-pass image in Fig image is fed image of size implementation is given implementation One CTCNN impulsive noise Initial state insensitive input pattern kind of CIPA lowpass filtering M2CIPA mark image mathematical morphology MCNN membership function NCNN neighborhood patterns original image output at iteration output image output pattern pixels is fed Radon transform results are shown rules are given second binary source second layer shown in Fig shows the output simulation results Simulations The simulation structuring element template Xij(t Xjj(O yy(oo