An Optimal Edge Detection Using Gravitational Search Algorithm

 Abstract—Edge detection is a fundamental tool used in most image processing applications to obtain information from the image as a precursor step to feature extraction and object segmentation. Gravitational search algorithm (GSA) is a new population-based search algorithm inspired by Newtonian gravity. Algorithm uses the theory of Newtonian gravity and its searcher agents are the collection of masses. Masses attract each other by way of gravity force, and this force causes a global movement of all objects towards the objects with heavier masses. In the proposed approach the edges are detected by the local variation in intensity values and the movement of agents is computed using gravitational search algorithm. The proposed approach is able to detect the edge pixel in an image up to a certain extent. The technique can be further extended for finding more edge pixels by modifying the gravitational search algorithm.

[1]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

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

[3]  Om Prakash Verma,et al.  An optimal edge detection using universal law of gravity and ant colony algorithm , 2011, 2011 World Congress on Information and Communication Technologies.

[4]  Giovanni Ramponi,et al.  Fuzzy operator for sharpening of noisy images , 1992 .

[5]  F. Russo,et al.  A user-friendly research tool for image processing with fuzzy rules , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[6]  R A Kirsch,et al.  Computer determination of the constituent structure of biological images. , 1971, Computers and biomedical research, an international journal.

[7]  Qiang Liu,et al.  A novel approach for edge detection based on the theory of universal gravity , 2007, Pattern Recognit..

[8]  Mengjie Zhang,et al.  A new homogeneity-based approach to edge detection using PSO , 2009, 2009 24th International Conference Image and Vision Computing New Zealand.

[9]  Madasu Hanmandlu,et al.  A Novel Fuzzy Ant System for Edge Detection , 2010, 2010 IEEE/ACIS 9th International Conference on Computer and Information Science.

[10]  Hossein Nezamabadi-pour,et al.  Edge detection using ant algorithms , 2006, Soft Comput..

[11]  Om Prakash Verma,et al.  A Novel Approach for Edge Detection using AntColony Otimization and Fuzz Derivative Technique , 2009, 2009 IEEE International Advance Computing Conference.

[12]  Tae-Sun Choi,et al.  Local threshold and Boolean function based edge detection , 1999, 1999 Digest of Technical Papers. International Conference on Consumer Electronics (Cat. No.99CH36277).