Optimal Threshold Computing in Automatic Image Thresholding using Adaptive Particle Swarm Optimization

 

P. Moallem*¹, N. Razmjooy²

 

¹ Department of Electrical Engineering University of Isfahan, Hezarjerib Street, Isfahan, Iran. *p_moallem@eng.ui.ac.ir.

² Young Researchers club, Majlesi branch, Islamic Azad University, Isfahan, Iran.

 

Abstract

Selecting an optimal threshold value is the most important step in image thresholding algorithms. For a bimodal histogram which can be modeled as a mixture of two Gaussian density functions, estimating these densities in practice is not simply feasible. The objective of this paper is to use adaptive particle swarm optimization (APSO) for the suboptimal estimation of the means and variances of these two Gaussian density functions; then, the computation of the optimal threshold value is straightforward. The comparisons of experimental results in a wide range of complex bimodal images show that this proposed thresholding algorithm presents higher correct detection rate of object and background in comparison to the other methods including Otsu's method and estimating the parameters of Gaussian density functions using genetic algorithm (GA). Meanwhile, the proposed thresholding method needs lower execution time than the PSO-based method, while it shows a little higher correct detection rate of object and background, with lower false acceptance rate and false rejection rate.

Keywords: histogram-based thresholding, adaptive particle swarm optimization, genetic algorithm, fitness function, object and background detection.

 

DESCARGAR ARTÍCULO EN FORMATO PDF

 

References

[1] E. D. López-Espinoza and L. Altamirano-Robles, "Reference Fields Analysis of a Markov Random Field Model to Improve Image Segmentation," Journal of Applied Research Technology, vol. 8, no.2, pp. 260-273, 2010.         [ Links ]

[2] G. J. Chhajed et al., "Review on Binary Image Steganography and Watermarking," International Journal on Computer Science and Engineering, vol. 3, no. 11, pp. 3645-3651, 2011.         [ Links ]

[3] M. Sezgin and B. Sankur, "Survey over image thresholding techniques and quantitative performance evaluation," Journal of Electronic Imaging, vol. 13, no. 1, pp. 146-165, 2004.         [ Links ]

[4] Y. E. Fernandez et al., "development of a prototype for classification of potato mini-tubers based on artificial vision," 6^th International Conference on Electrical Engineering, Computing Science and Automatic Control, Toluca, 2009.         [ Links ]

[5] P. P. Kumar et al., "A Novel Stroke Width Based Binarization Method to Handle Closely Spaced Thick Characters," International Journal of Computer Applications, vol. 1, no. 23, pp. 32-39, 2010.         [ Links ]

[6] Z. K. Huang and K. W. Chau, "A New Image Thresholding Method Based on Gaussian Mixture Model", Applied Mathematics and Computation, vol. 205, no. 2, pp. 899-907, 2008.         [ Links ]

[7] R. Gonzalez et al., "Digital Image Processing," 3^rd Edition, Prentice Hall, 2008.         [ Links ]

[8] L. M. Palanivelu and P. Vijayakumar, "A Particle Swarm Optimization for Image Segmentation in Multi Application Smart Cards," European Journal of Scientific Research, vol. 70, no. 3, pp. 354-360, 2012.         [ Links ]

[9] P. Kanungo et al., "Image Segmentation Using Thresholding and Genetic Algorithm," Conference on Soft Computing Technique for Engineering Applications, Rourkela, India, 2006.         [ Links ]

[10] C. C. Lai, "A Novel Image Segmentation Approach Based on Particle Swarm Optimization," IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol. E89-A, no. 1, pp. 324-327, 2006.         [ Links ]

[11] S. Cheng et al., "Experimental Study on Boundary Constraints Handling in Particle Swarm Optimization: From Population Diversity Perspective," International Journal of Swarm Intelligence Research, vol. 2, no. 3, pp. 43-69, 2011.         [ Links ]

[12] P. Moallem and N. Razmjooy, "A Multi Layer Perceptron Neural Network Trained by Invasive Weed Optimization for Potato Color Image Segmentation," Trends in Applied Sciences Research, vol. 7, no. 6, pp. 445-455, 2012.         [ Links ]

[13] P.D. Sathya and R. Kayalvizhi, "PSO-Based Tsallis Thresholding Selection Procedure for Image Segmentation," International Journal of Computer Applications, vol. 5, no.4, pp.39-46, 2010.         [ Links ]

[14] J. Zhang et al., "Bidding Strategy Based on Adaptive Particle Swarm Optimization for Electricity Market," 8^th World Congress on Intelligent Control and Automation, Jinan, China, 2010.         [ Links ]

[15] The USC-SIPI Image Database, Available from: http://sipi.usc.edu/database/.         [ Links ]