Edge detection is one of the most commonly used operations in image analysis and digital image processing. Edge detection technique has a key role in machine vision and image understanding systems. In machine vision motion track and measurement system based on discrete feature, the exact feature edge orientation in the image is the precondition of the successful completion of the vision measurement task. Edges of an image are considered a type of crucial information that can be extracted by applying detectors with different methodology. Most of the classical mathematical methods for edge detection based on the derivative of the pixels of the original image are Gradient operators, Laplacian and Laplacian of Gaussian operators. Gradient based edge detection methods, such as Roberts, Sobel and Prewitts, have used two 2-D or 3-D linear filters to process vertical edges and horizontal edges separately to approximate first-order derivative of pixel values of the image. The Laplacian edge detection method has used a 3-D linear filter to approximate second-order derivative of pixel values of the image. Major drawback of second-order derivative approach is that the response at and around the isolated pixel is much stronger. In this thesis, a robust edge detection method based Cellular Automata (CA) is proposed. Simulation results reveal that the proposed method can detect edges more smoothly in a shorter amount of time compared to the other edge detectors.

1. K. Kaur, V. Mutenja, I. S. Gill. 2010. Fuzzy Logic Based Image Edge Detection algorithm in MATLAB. International Journal of Computer Applications(0975-8887), Vol. 1,pp. 55-58.
2. C. Suliman, C. Boldi?or, R. B?z?van, F. Moldoveanu 2011. A Fuzzy Logic Based Method For Edge Detection. Bulletin of the Transilvania University of Bra?ov Series I: Engineering Sciences, Vol. 4 (53) No. 1.
3. Canny J. 1986. A Computational Approach to Edge Detection", IEEE Trans. Pattern Analysis and Machine Intelligence, 8:679-714.
4. E. Nadernejad, S. Sharifzadeh and H. Hassanpour. 2008. Edge Detection Techniques: Evaluations and Comparisons. Applied Mathematical Sciences, Vol. 2, No. 31, 1507 – 1520.
5. Wafa Barkhoda,Fardin Akhlaqian Tab,Om-Kolsoom Shahryari. 2009. Fuzzy Edge Detection Based on Pixel's Gradient and Standard Deviation Values. Iran.
6. Rong Wang, Li-Qun Gao, Shu Yang, Yan-Chun Liu. 2005. An Edge Detection Method By Combining Fuzzy Logic And Neural Network. In Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, 18-21, pp. 4539-4543.
7. Yinghua Li, Bingqi Liu, and Bin Zhou. 2005. The Application of Image Edge Detection by using Fuzzy Technique. In Proceedings of SPIE Vol. 5637 (SPIE) Bellingham, WA.
8. Dong-Su Kim, Wang-Heon Lee , In-So Kweon. 2004. Automatic edge detection using 3x3 ideal binary pixel patterns and fuzzy-based edge thresholding. Pattern Recognition Letters.
9. Saman S. et al. 2009. A Hybrid Edge Detection Method Based on Fuzzy Set theory and Cellular Learning Automata. In Proceedings of the International Conference on Computational Science and Its Applications, ICCSA. IEEE Computer Society, pp. 208-214.
10. Ke Zhang, Zhong Li, Xiaoou Zhao. 2007. Edge Detection of Images based on Fuzzy Cellular Automata. In Proceedings of SNPD (2). pp. 289~294
11. H. T. Goi. 2003. An Original Method of Edge Detection Based on Cellular Automata. Department of Electrical Engineering and Computer Science Korea Advanced Institute of Science and Technology.
12. L. G. Roberts. 1965. Machine perception of 3-D solids. Optical and Electro-Optical Information Processing. MIT Press.
13. W. Frei and C. -C. Chen. 1977. Fast boundary detection: A generalization and a new algorithm. lEEE Trans. Comput. , vol. C-26, no. 10, pp. 988-998.
14. G. Robinson. 1997. Edge Detection by Compass Gradient Masks. Computer Graphic Image Processing, vol. 6, pp. 492-501.
15. Sobel E. 1970. Camera Models and Machine Perception. PhD thesis. Stanford University, Stanford, California.

Image Processing Edge Detection Cellular Automata