Edge Image Detection
INFORMATION PAPER International Journal of Recent Trends in Engineering, Vol. 1, No. 2, May 2009
Edge Detection Techniques for Image Segmentation – A Survey of Soft Computing Approaches
N. Senthilkumaran1 and R. Rajesh2
School of Computer Science and Engineering, Bharathiar University, Coimbatore -641 046, India. 1 senthilkumaran@ieee.org, 2kollamrajeshr@ieee.org logical reasoning[1]. It has been applied to image processing in many ways[19]. Segmentation aims at dividing pixels into similar region i.e. crisp sets[4]. Fuzzy segmentation in turn divides pixels into fuzzy sets i.e. each pixel may belong partly to many sets and regions of image[3][25]. The Second approach, Neural networks are computer algorithms inspired by the way information is processed in the nervous system. An important difference between neural networks and other AI techniques is their ability to learn. The network ”learns” by adjusting the interconnection (called weights) between layers. When the network is adequately trained, it is able to generalize relevant output for a set of input data. A valuable property of neural networks is that of generalization, whereby a trained neural network is able to provide a correct matching in the form of output data for a set of previously unseen input data. Learning typically occurs by example through training, where the training algorithm iteratively adjusts the connection weights [2][24]. The third approach, Genetic algorithms derive from the evolution theory. They were introduced in 1975 by John Holland and his team as a highly parallel search algorithm. Later, they have been mainly used as an optimization device. According to the evolution theory, within a population only the individuals well adapted to their environment can survive and transmit some of their characters to their descendants. GA has been used to solve various problems in digital image processing, including image segmentation [14][23]. This paper is organized as follows. Section II is
Edge Detection Techniques for Image Segmentation – A Survey of Soft Computing Approaches
N. Senthilkumaran1 and R. Rajesh2
School of Computer Science and Engineering, Bharathiar University, Coimbatore -641 046, India. 1 senthilkumaran@ieee.org, 2kollamrajeshr@ieee.org logical reasoning[1]. It has been applied to image processing in many ways[19]. Segmentation aims at dividing pixels into similar region i.e. crisp sets[4]. Fuzzy segmentation in turn divides pixels into fuzzy sets i.e. each pixel may belong partly to many sets and regions of image[3][25]. The Second approach, Neural networks are computer algorithms inspired by the way information is processed in the nervous system. An important difference between neural networks and other AI techniques is their ability to learn. The network ”learns” by adjusting the interconnection (called weights) between layers. When the network is adequately trained, it is able to generalize relevant output for a set of input data. A valuable property of neural networks is that of generalization, whereby a trained neural network is able to provide a correct matching in the form of output data for a set of previously unseen input data. Learning typically occurs by example through training, where the training algorithm iteratively adjusts the connection weights [2][24]. The third approach, Genetic algorithms derive from the evolution theory. They were introduced in 1975 by John Holland and his team as a highly parallel search algorithm. Later, they have been mainly used as an optimization device. According to the evolution theory, within a population only the individuals well adapted to their environment can survive and transmit some of their characters to their descendants. GA has been used to solve various problems in digital image processing, including image segmentation [14][23]. This paper is organized as follows. Section II is
References: [1] Orlando J. Tobias and Rui Seara, ”Image Segmentation by Histogram Thresholding Using Fuzzy Sets”, IEEE Transactions on Image Processing, Vol.11, No.12, 2002, pp. 1457-1465. [2] Dinesh K. Sharma, Loveleen Gaur and Daniel Okunbor, “Image Compression and Feature Extraction with Neural Network”, Proceedings of the Academy of Information and Management Sciences, Vol.11, No.1, 2007, pp. 33-38. [3] M. Abdulghafour, ”Image segmentation using Fuzzy logic and genetic algorithms”, Journal of WSCG, vol. 11, no.1, 2003. [4] Dao Qiang Zhanga and Song Can Chena, “A novel kernelized fuzzy C-means algorithm with application in medical image segmentation”, Artificial Intelligence in Medicine, vol. 32, 2004, pp.37-50. [5] Lei Jiang and Wenhui Yang, ”A Modified Fuzzy C-Means Algorithm for Segmentation of Magnetic Resonance Images”, Proc. VIIth Digital Image Computing: Techniques and Applications, vol. 10-12, 2003,pp.225-231. [6] Kanchan Deshmukh and G. N. Shinde, ” An adaptive Neuro - fuzzy system for color image segmentation”, J. Indian Inst. Sci., vol. 86, Sept.-Oct. 2006, pp.493-506. [7] Jander Moreira and Luciano Da Fontoura Costa, ”Neural-based color image segmentation and classification using self - organizing maps”, Anais do IX SIBGRAPI, 1996, pp.47-54. [8] Mohamed N. Ahmed and Aly A. Farag, ”Two-stage neural network for volume segmentation of medical images ”, Pattern Recognition Letters, vol.18, 1997, pp.1143-1151. [9] Gonzalo A. Ruz, Pablo A. Estevez and Claudio A. Perez, “A neurofuzzy color image segmentation method for wood surface defect detection”, Forest Products Journal, Vol.55, No.4, April 2005, pp.52-58. [10] Mausumi Acharyya and Malay K. Kundu, “ Image Segmentation Using Wavelet Packet Frames and Neurofuzzy Tools”, International Journal of Computational Cognition, Vol.5, No.4, December 2007, pp.27-43. [11] Ibrahiem M. M. El Emary, “On the Application of Artificial Neural Networks in Analyzing and Classifying the Human Chromosomes”, Journal of Computer Science, vol.2(1), 2006, pp.72-75. [12] Bouchet A, Pastore J and Ballarin V, “Segmentation of Medical Images using Fuzzy Mathematical Morphology”, JCS and T, Vol.7, No.3, October 2007, pp.256-262. [13] Mantas Paulinas and Andrius Usinskas, “A Survey of Genetic Algorithms Applicatons for Image Enhancement and Segmentation”, Information Technology and Control, Vol.36, No.3, 2007, pp.278-284. [14] Xian Bin Wen, Hua Zhang and Ze Tao Jiang, ”Multiscale Unsupervised Segmentation of SAR Imagery Using the Genetic Algorithm”, Sensors, vol.8, 2008, pp.1704-1711. [15] Daniel L. Schmoldt, Pei Li and A. Lynn Abbott, ”Machine vision using artificial neural networks with local 3D neighborhoods”, Computers and Electronics in Agriculture, vol.16, 1997, pp.255-271. [16] Ian Middleton and Robert I. Damper, “ Segmentation of magnetic resonance images using a combination of neural networks and active contour models”, Medical Engineering and Physics, vol.26, 2004, pp.71-86. [17] J. Maeda, A. Kawano, S. Yamauchi, Y. Suzuki A. R. S. Marcal and T. Mendonc, ”Perceptual Image Segmentation Using Fuzzy - Based Hierarchical Algorithm and Its Application to Dermoscopy Images”, IEEE Conference on Soft Computing in Industrial Applications (SMCia/08), June 25-27, 2008, Muroran, JAPAN, pp.66-71. [18] L.A. Zadeh,”Some reflections on soft computing, granular Computing and their roles in the conception, design and utilization of information/intelligent systems”, Soft Computing, vol.2, 1998, pp.23-25. [19] Jianxun Zhang, Quanli Liu and Zhuang Chen,”A Medical Image Segmentation Method Based on SOM and Wavelet Transforms”, Journal of Communication and Computer , Vol.2, No.5 (Serial No.6) ,May 2005, pp.46-50. [20] Metin Kaya, ”Image Clustering and Compression Using an Annealed Fuzzy Hopfield Neural Network”, International Journal of Signal Processing,2005, pp.80-88. [21] Hichem Talbi, Mohamed Batouche and Amer Draa, ”A Quantum - Inspired Evolutionary Algorithm for Multiobjective Image Segmentation”, International Journal of Mathematical, Physical and Engineering Sciences, Vol.1 No.2, 2007, pp.109-114. [22] George Karkavitsas and Maria Rangoussi,” Object Localization in Medical Image Using Genetic Algorithms”, International Jnl. of Signal Processing, 2005, pp.204-207. [23] A. Borji, and M. Hamidi, ”Evolving a Fuzzy Rule-Base for Image Segmentation”, International Journal of Intelligent Systems and Technologies, 2007, pp.178-183. [24] Wei Sun and Yaonan Wang, “Segmentation Method of MRI Using Fuzzy Gaussian Basis Neural Network Neural Information Processing”, Letters and Reviews, Vol.8, No.2, August 2005, pp.19-24. [25] N. Senthilkumaran and R. Rajesh, “A Study on Edge Detection Methods for Image Segmentation”, Proceedings of the International Conference on Mathematics and Computer Science (ICMCS-2009), 2009,Vol.I, pp.255-259. [26] N. Senthilkumaran and R. Rajesh, “A Study on Split and Merge for Region based Image Segmentation”, Proceedings of UGC Sponsored National Conference Network Security (NCNS-08) , 2008, pp.57-61. [27] N. Senthilkumaran and R. Rajesh, “Edge Detection Techniques for Image Segmentation - A Survey”, Proceedings of the International Conference on Managing Next Generation Software Applications (MNGSA-08), 2008, pp.749-760. 254 © 2009 ACADEMY PUBLISHER