Evaluation of Edge Detection Methods on Different Categories of Images
Abstract
Object detection in an image is a challenging task. Recent developments in the field of computer vision and machine learning contributes to solving the issue in the field of object detection. Deep learning is one of the recent innovations that selects the feature of an object for evaluation. The shape is the most relevant high-level feature that helps to separate different objects. It can be visualized as a collection of edges and can be defined as a set of contiguous pixel positions where an abrupt change of intensity values occur. Hence, the selection of a better edge detection method for an object category gives higher accuracy in recognition. Our objective in this paper is to compare the various edge detection methods by evaluating the entropy as a measure, to find the best suitable method for each category of objects. Understanding the mechanism behind each of the edge detection algorithms is indispensable to improve the quality of the outcome it produces. Results show the best edge detection for a given category of an image from the Caltech 256 Image Dataset.
About this article
Keywords
- Edge Detection
- Entropy
- Canny
- Fuzzy Logic
References
[1] Hankyu Moon, Rama Chellappa, Azriel Rosenfeld, “Optimal Edge-Based Shape Detection”, 11/2002.
[2] Barba Joseph, Paul Fenster, Henrick Jeanty, and Joan Gil, “Applications of Digital Image Processing XI”, 1988.
[3] Joseph Barba-"The use of local entropy measures in edge detection for cytological Image analysis", Journal of Microscopy, 10/1989.
[4] D. Marr and E.Hildreth -“Theory of Edge Detection”. Proceedings of the Royal Society of London. Series B, Biological Sciences, 2/1980.
[5] M. H. Hueckel, “A local visual operator which recognizes edges and line”, J. ACM, 10/1973.
[6] P. Melin, C. I. Gonzalez and J. R. Castro, “Edge-detection method for image processing based on generalized type-2 fuzzy logic. Fuzzy Systems”, IEEE Transactions on vol. 22, 2014.
[7] J. Canny, "A Computational approach to edge detection", IEEE Trans Pattern Analysis and Machine Intelligence, 1986.
[8] Ms. Beant Kaur and Mr. Anil Garg, “Comparative Study of Different Edge Detection Techniques”, 3/2011.
[9] Deroche R, “Optimal edge detection using recursive filtering”, Proc. First Int. Conf. Computer Vision, London, 6/1987.
[10] Rafael C. Gonzalez, Richard E. Woods, “Digital Image Processing”, 2nd ed., Beijing: Publishing House of Electronics Industry, 2007.
[11] Abhishek.S.N, Dr.Shriram.K.Vasudevan, Sundaram, “An enhanced and efficient algorithm for faster, better and accurate edge detection”, Springer, 2016.
[12] V.Sowmya, Neethu Mohan, K.P.Soman, “Edge Detection Using Sparse Banded Filter Matrices”, Elsevier Procedia Computer Science Journal, 10, 2015.
[13] Dr. Shriram K Vasudevan, Dharmendra T,Sivaraman R, Karthick S, “Automotive image processing technique using Canny’s edge detector”,IJEST, 2010.
[14] R.Joseph Manoj, M.D.Anto Praveena, K.Vijayakumar, “An ACO–ANN based feature selection algorithm for big data”, Cluster Computing The Journal of Networks, Software Tools and Applications, ISSN: 1386-7857 (Print), 1573-7543 (Online) DOI: 10.1007/s10586-018-2550-z, 2018.
[15] K. Vijayakumar, C. Arun, Analysis and selection of risk assessment frameworks for cloud based enterprise applications”, Biomedical Research, ISSN: 0976-1683 (Electronic), January 2017.