Object detection is one of the key issues in digital image processing. Over the years, many algorithms have been created for detecting meaningful objects on the image which are based on specific characteristics of object or complex mathematical methods. Circle detection is one of these types of methods. One of the best methods for circle detection on digital images and discussion of machine vision is the Hough transform. The Hough transform can be described as the transformation of a point in the x-y-plane to the parameter space. Parameter space can be defined by the shape of the object. Using the special character of each image in space, we are able to retrieve and extract the image circle. Importantly, this method is time consuming and a large amount of memory is required for the image. The undesirable features have reduced the popularity of this method. The idea of using genetic algorithm for detecting a circle in the picture is very attractive and functional. This method can be used in Robot Soccer, targeting systems and iris recognition. In this method, accuracy and speed are among important parameters. For example, in the case of robot, it should detect ball in monochromatic and sometimes crowded areas (due to accumulation of other bots around the ball). Using a genetic algorithm for circle detection on images, Hough transform weaknesses have been removed. It also increases the computation speed and accurate detection of circle. In this paper, the Hough transform method will be presented and then we will describe the process of implementing genetic algorithms to find a circle in the picture.
| Published in |
International Journal of Intelligent Information Systems (Volume 3, Issue 6-1)
This article belongs to the Special Issue Research and Practices in Information Systems and Technologies in Developing Countries |
| DOI | 10.11648/j.ijiis.s.2014030601.19 |
| Page(s) | 49-55 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2014. Published by Science Publishing Group |
Hough Transform, Image Processing, Digital Image
| [1] | Tahir Rabbani and Frank van den Heuvel,“Efficient Hough transform for automatic detection of cylinders in point clouds.” in Proceedings of the 11th Annual Conference of the Advanced School for Computing and Imaging ,The Netherlands, June 2005. |
| [2] | Just Kjeldgaard Pedersen, Simon,“ Circular Hough Transform.” Aalborg University, Vision, Graphics, and Interactive Systems. November 2007. |
| [3] | B. Jahne, H. Scharr, and S. Körkel, “Principles of filter design,”In Handbook of Computer Vision and Applications. Academic Press, 1999. |
| [4] | Bresenham, J. E. (1), “Algorithm for computer control of a digital plotter,” IBM Systems Journal, pp25–30 January 1965. |
| [5] | Bies, Robert R, Muldoon, Matthew F., Pollock, Bruce G, Manuck, Steven, Smith, Gwenn, Sale, Mark E, “A Genetic Algorithm-Based, Hybrid Machine Learning Approach to Model Selection,”Journal of Pharmacokinetics and Pharmacodynamics, pp 196–221, 2006. |
| [6] | G. Eason, B. Noble, and I. N. Sneddon, “On certain integrals of Lipschitz-Hankel type involving products of Bessel functions,” Phil. Trans. Roy. Soc. London, vol. A247, pp. 529–551, April 1955. |
| [7] | G. Kendall and G. Whitwell, “An evolutionary approach for the tuning of a chess evaluation function using population dynamics,” In Proceedings of the 2001 Congress on Evolutionary Computation, pp 995–1002. IEEE Press, World Trade Center, Seoul, Korea, 2001. |
| [8] | Shah, S.M.; Thaker, C. S, Singh, D, “ Multimedia based fitness function optimization through evolutionary game learning,”Emerging Trends in Networks and Computer Communications (ETNCC), 2011 International Conference, page(s): 164- 168,2011. |
| [9] | H.A.Rowley, S.Baluja, T.Kande, “Rotation invariant neural network-based face detection,” Computer Science Technical Report, CMU-CS-97-201, CMU, Pittsburgh, 1997. |
| [10] | H.Schneiderman, “T.Kande. Probabilistic modeling of local appearance and spatial relationships for object recognition,”IEEE Conference on Computer Vision and Pattern Recognition, 45-51, Santa Barbara, 1998. |
| [11] | T.K.Leung, M.C.Burl, P.Perona. “Finding faces in cluttered scenes using random labeled graph matching,” International Conference on Computer Vision, p: 637-644, Cambridge, MA, 1995. |
APA Style
Navid Khalili Dizaji, Nazila Masoudi, Aidin Sakhvati. (2014). A new Method to Detect Circles in Images Based on Genetic Algorithms. International Journal of Intelligent Information Systems, 3(6-1), 49-55. https://doi.org/10.11648/j.ijiis.s.2014030601.19
ACS Style
Navid Khalili Dizaji; Nazila Masoudi; Aidin Sakhvati. A new Method to Detect Circles in Images Based on Genetic Algorithms. Int. J. Intell. Inf. Syst. 2014, 3(6-1), 49-55. doi: 10.11648/j.ijiis.s.2014030601.19
AMA Style
Navid Khalili Dizaji, Nazila Masoudi, Aidin Sakhvati. A new Method to Detect Circles in Images Based on Genetic Algorithms. Int J Intell Inf Syst. 2014;3(6-1):49-55. doi: 10.11648/j.ijiis.s.2014030601.19
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Download@article{10.11648/j.ijiis.s.2014030601.19,
author = {Navid Khalili Dizaji and Nazila Masoudi and Aidin Sakhvati},
title = {A new Method to Detect Circles in Images Based on Genetic Algorithms},
journal = {International Journal of Intelligent Information Systems},
volume = {3},
number = {6-1},
pages = {49-55},
doi = {10.11648/j.ijiis.s.2014030601.19},
url = {https://doi.org/10.11648/j.ijiis.s.2014030601.19},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.s.2014030601.19},
abstract = {Object detection is one of the key issues in digital image processing. Over the years, many algorithms have been created for detecting meaningful objects on the image which are based on specific characteristics of object or complex mathematical methods. Circle detection is one of these types of methods. One of the best methods for circle detection on digital images and discussion of machine vision is the Hough transform. The Hough transform can be described as the transformation of a point in the x-y-plane to the parameter space. Parameter space can be defined by the shape of the object. Using the special character of each image in space, we are able to retrieve and extract the image circle. Importantly, this method is time consuming and a large amount of memory is required for the image. The undesirable features have reduced the popularity of this method. The idea of using genetic algorithm for detecting a circle in the picture is very attractive and functional. This method can be used in Robot Soccer, targeting systems and iris recognition. In this method, accuracy and speed are among important parameters. For example, in the case of robot, it should detect ball in monochromatic and sometimes crowded areas (due to accumulation of other bots around the ball). Using a genetic algorithm for circle detection on images, Hough transform weaknesses have been removed. It also increases the computation speed and accurate detection of circle. In this paper, the Hough transform method will be presented and then we will describe the process of implementing genetic algorithms to find a circle in the picture.},
year = {2014}
}
TY - JOUR T1 - A new Method to Detect Circles in Images Based on Genetic Algorithms AU - Navid Khalili Dizaji AU - Nazila Masoudi AU - Aidin Sakhvati Y1 - 2014/10/27 PY - 2014 N1 - https://doi.org/10.11648/j.ijiis.s.2014030601.19 DO - 10.11648/j.ijiis.s.2014030601.19 T2 - International Journal of Intelligent Information Systems JF - International Journal of Intelligent Information Systems JO - International Journal of Intelligent Information Systems SP - 49 EP - 55 PB - Science Publishing Group SN - 2328-7683 UR - https://doi.org/10.11648/j.ijiis.s.2014030601.19 AB - Object detection is one of the key issues in digital image processing. Over the years, many algorithms have been created for detecting meaningful objects on the image which are based on specific characteristics of object or complex mathematical methods. Circle detection is one of these types of methods. One of the best methods for circle detection on digital images and discussion of machine vision is the Hough transform. The Hough transform can be described as the transformation of a point in the x-y-plane to the parameter space. Parameter space can be defined by the shape of the object. Using the special character of each image in space, we are able to retrieve and extract the image circle. Importantly, this method is time consuming and a large amount of memory is required for the image. The undesirable features have reduced the popularity of this method. The idea of using genetic algorithm for detecting a circle in the picture is very attractive and functional. This method can be used in Robot Soccer, targeting systems and iris recognition. In this method, accuracy and speed are among important parameters. For example, in the case of robot, it should detect ball in monochromatic and sometimes crowded areas (due to accumulation of other bots around the ball). Using a genetic algorithm for circle detection on images, Hough transform weaknesses have been removed. It also increases the computation speed and accurate detection of circle. In this paper, the Hough transform method will be presented and then we will describe the process of implementing genetic algorithms to find a circle in the picture. VL - 3 IS - 6-1 ER -
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