Non-linear cellular automata based edge detector for optical character images

Design of parallel algorithms for edge detection is extremely important for image analysis and understanding. Cellular automata are the most common and simple models of parallel computation and over the last decade, numerous cellular automata techniques have already been proposed. This paper presents a novel method for edge detection of optical character images based on a variant of cellular automata, called non-linear cellular automata. The method consists of three stages and each stage is simple to understand and implement. A standard binarization technique is applied to a grayscale image and boundary conditions are added to the resultant image. Finally, non-linear cellular automata rules are designed and applied simultaneously to all pixels of the image. The suggested scheme has been validated on optical characters (handwritten as well as printed) of different languages. Furthermore, results are compared with standard edge detection techniques in terms of different performance parameters like entropy, kappa values, true positives, false negatives, etc. It is observed that the suggested scheme is superior to other standard schemes. Hence, the scheme has the potential application for character recognition.

[1]  Jahangir Mohammed,et al.  An efficient edge detection technique by two dimensional rectangular cellular automata , 2014, International Conference on Information Communication and Embedded Systems (ICICES2014).

[2]  J. Sim,et al.  The kappa statistic in reliability studies: use, interpretation, and sample size requirements. , 2005, Physical therapy.

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

[4]  Chun-Ling Chang,et al.  Cellular automata for edge detection of images , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

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

[6]  Banshidhar Majhi,et al.  Salt and Pepper Noise Reduction Schemes Using Cellular Automata , 2016 .

[7]  Amita Pal,et al.  Solutions on 1-D and 2-D Density Classification Problem Using Programmable Cellular Automata , 2009, J. Cell. Autom..

[8]  Yudong Zhang,et al.  Pathological Brain Detection in Magnetic Resonance Imaging Scanning by Wavelet Entropy and Hybridization of Biogeography-based Optimization and Particle Swarm Optimization , 2015 .

[9]  Jun Shen,et al.  Neuro-fuzzy synergism to the intelligent system for edge detection and enhancement , 2003, Pattern Recognit..

[10]  Preetha Phillips,et al.  Pathological brain detection in MRI scanning by wavelet packet Tsallis entropy and fuzzy support vector machine , 2015, SpringerPlus.

[11]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[12]  Yudong Zhang,et al.  Pathological Brain Detection by a Novel Image Feature - Fractional Fourier Entropy , 2015, Entropy.

[13]  Mark S. Nixon,et al.  Feature Extraction and Image Processing , 2002 .

[14]  David Malah,et al.  A study of edge detection algorithms , 1982, Comput. Graph. Image Process..

[15]  Mithun Chakraborty,et al.  Characterization of the Evolution of Nonlinear Uniform Cellular Automata in the Light of Deviant States , 2011, Int. J. Math. Math. Sci..

[16]  M L Mendelsohn,et al.  THE ANALYSIS OF CELL IMAGES * , 1966, Annals of the New York Academy of Sciences.

[17]  Thierry Pun,et al.  A new method for grey-level picture thresholding using the entropy of the histogram , 1980 .

[18]  Amita Pal,et al.  Computers and Mathematics with Applications Investigation of the Global Dynamics of Cellular Automata Using Boolean Derivatives , 2022 .

[19]  Charles F. Hockett,et al.  A mathematical theory of communication , 1948, MOCO.

[20]  Yudong Zhang,et al.  Fruit classification by biogeography‐based optimization and feedforward neural network , 2016, Expert Syst. J. Knowl. Eng..

[21]  Aggelos K. Katsaggelos,et al.  Edge detection using a neural network , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[22]  Yudong Zhang,et al.  Improved image filter based on SPCNN , 2008, Science in China Series F: Information Sciences.

[23]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Lawrence G. Roberts,et al.  Machine Perception of Three-Dimensional Solids , 1963, Outstanding Dissertations in the Computer Sciences.

[25]  Yang Li,et al.  Detection of Dendritic Spines Using Wavelet-Based Conditional Symmetric Analysis and Regularized Morphological Shared-Weight Neural Networks , 2015, Comput. Math. Methods Medicine.

[26]  Andrew Adamatzky,et al.  Cellular Automata in Image Processing and Geometry , 2014 .

[27]  Parimal Pal Chaudhuri,et al.  Theory and Applications of Cellular Automata in Cryptography , 1994, IEEE Trans. Computers.

[28]  Yudong Zhang,et al.  Fruit Classification by Wavelet-Entropy and Feedforward Neural Network Trained by Fitness-Scaled Chaotic ABC and Biogeography-Based Optimization , 2015, Entropy.

[29]  Yudong Zhang,et al.  Dual-Tree Complex Wavelet Transform and Twin Support Vector Machine for Pathological Brain Detection , 2016 .

[30]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[31]  Irwin Edward Sobel,et al.  Camera Models and Machine Perception , 1970 .