An Enhanced Approach for Image Edge Detection Using Histogram Equalization (BBHE) and Bacterial Foraging Optimization (BFO)

• Autorzy: Kumar Praveen , Jindal Tanvi , Raj Balwinder

Identyfikatory

DOI

10.24425/ijet.2022.143897

• Języki publikacji: EN

Abstrakty

EN

The Edge detection is a customarily task. Edge detection is the main task to perform as it gives clear information about the images. It is a tremendous device in photograph processing gadgets and computer imaginative and prescient. Previous research has been done on moving window approach and genetic algorithms. In this research paper new technique, Bacterial Foraging Optimization (BFO) is applied which is galvanized through the social foraging conduct of Escherichia coli (E.coli). The Bacterial Foraging Optimization (BFO) has been practice by analysts for clarifying real world optimization problems arising in different areas of engineering and application domains, due to its efficiency. The Brightness preserving bi-histogram equalization (BHEE) is another technique that is used for edge enhancement. The BFO is applied on the low level characteristics on the images to find the pixels of natural images and the values of F-measures, recall(r) and precision (p) are calculated and compared with the previous technique. The enhancement technique i.e. BBHE is carried out to improve the information about the pictures.

Słowa kluczowe

EN

edge detection; bacterial foraging optimization; BBHE; images processing; graphics

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2022
• Tom: Vol. 68. No. 4
• Strony: 875--880
• Opis fizyczny: Bibliogr. 30 poz., fot., rys., tab., wykr.

Twórcy

autor

Kumar Praveen

• Dr. B.R. Ambedkar National Institute of Technology, Jalandhar, India

autor

Jindal Tanvi

• Chitkara Business School, Chitkara University, Punjab, India

autor

Raj Balwinder

• National Institute of Technical Teachers Training and Research, Chandigarh, India

Bibliografia

• [1] C. Olson and D. Huttenlocher, “Automatic target recognition by matching oriented edge pixels,” IEEE Trans. Image Process., vol. 6, no. 1,pp. 103-113, Jan. 1997. https://doi.org/10.1109/83.552100.
• [2] J. Sullivan and S. Carlsson, “Recognizing and tracking human action,”in Proc. 7th Eur. Conf. Comput. Vision I, 2002, pp. 629-644. https://doi.org/10.1007/3-540-47969-4_42.
• [3] M. Kokare, P. K. Biswas, and B. N. Chatterji, “Edge based featuresfor content based image retrieval,” Pattern Recognit., vol. 36, no. 11,pp. 2649-2661, 2003. https://doi.org/10.1016/S0031-3203(03)00174-2.
• [4] S. Ando, “Image field categorization and edge/corner detection fromgradient covariance,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 22,no. 2, pp. 179-190, Feb. 2000. https://doi.org/10.1109/34.825756.
• [5] M. Basu, “Gaussian-based edge-detection methods: A survey,” IEEETrans. Syst., Man, Cybern. C, Appl. Rev., vol. 32, no. 3, pp. 252-260,Aug. 2002. https://doi.org/10.1109/TSMCC.2002.804448.
• [6] G. Papari and N. Petkov, “Edge and line oriented contour detection: State of the art,” Image Vision Comput., vol. 29, nos. 2-3, pp. 79-103, 2011. https://doi.org/10.1016/j.imavis.2010.08.009.
• [7] L. Ganesan and P. Bhattacharyya, “Edge detection in untextured and textured images: A common computational framework,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 27, no. 5, pp. 823–834, Sep. 1997. https://doi.org/10.1109/3477.623235.
• [8] S. Verzakov, P. Pacl ́ik, and R. Duin, “Edge detection in hyperspectral imaging: Multivariate statistical approaches,” in Proc. Joint IAPR Int. Workshops Structural, Syntactic Stat. Pattern Recognit., vol. 4109, no. 10. 2006, pp. 551-5591. https://doi.org/10.1007/11815921_60.
• [9] Y.-Q. Zhao, W.-H. Gui, Z.-C. Chen, J.-T. Tang, and L.-Y. Li, “Medical images edge detection based on mathematical morphology,” in Proc. 27th Ann. Int. Conf. Eng. Med. Biol. Soc., 2005, pp. 6492-6495. https://doi.org/10.1109/IEMBS.2005.1615986.
• [10] Wenlong fu, Mark Johnston, Mengjie Zhang,”low-level Feature Extraction For Edge Detection Using Genetic Programming”, in Proc.IEEE transactions on Cybernetics, vol. 44, No. 8, August 2014. https://doi.org/10.1109/TCYB.2013.2286611.
• [11] Y. Zhang and P. I. Rockett, “Evolving optimal feature extraction using multi-objective genetic programming: A methodology and preliminary study on edge detection,” in Proc. Conf. Genetic Evol. Comput., 2005, pp. 795-802. https://doi.org/10.1145/1068009.1068143.
• [12] D. Boukerroui, J. A. Noble, and M. Brady, “On the choice of band-passquadrature filters,” J. Math. Imag. Vision, vol. 21, no. 1, pp. 53-80, 2004. https://doi.org/10.1023/B:JMIV.0000026557.50965.09.
• [13] Z. Xiao and Z. Hou, “Phase based feature detector consistent with human visual system characteristics,” Pattern Recognit. Lett., vol. 25, no. 10, pp. 1115-1121, 2004. https://doi.org/10.1016/j.patrec.2004.03.018.
• [14] B. A. Thomas, R. N. Strickland, & J. J. Rodriguez, “Color image enhancement using spatially adaptive saturation feedback”, Proc. 4th IEEE Conf. on Image Processing, Santa Barbara, CA, USA, pp. 30-33, 1997. https://doi.org/10.1109/ICIP.1997.631967.
• [15] A. Daskalakis, D. Cavouras, P. Bougioukos, S. Kostopoulos, P. Georgiadis, I. Kalatzis and G. Nikiforidis,, “An Efficient CLAHE-based, Spot-Adaptive, image segmentation technique for improving Microarray Genes’ Quantification”, in 2nd International Conference on Experiments/ Process/ System Modelling/Simulation/ Optimization (IC_EpsMsO), Athens, Greece, July 4-7, 2007.
• [16] uhammad Suzuri Hitam,. Wan Nural Jawahir Hj Wan Yussof, Ezmahamrul Afreen Awalludin,, Zainuddin Bachok, “Mixture Contrast Limited Adaptive Histogram Equalization for Underwater Image Enhancement”, In 2013 International conference on computer applications technology (ICCAT), pp. 1-5. IEEE, 2013. https://doi.org/10.1109/ICCAT.2013.6522017.
• [17] M. Abdullah-Al-Wadud, M. H. Kabir, M. A. A. Dewan, and Oksam Chae, “A dynamic histogram equalization for image contrast enhancement”, IEEE Trans. Consumer Electronics, vol. 53, no. 2, pp. 593-600, May 2007. https://doi.org/10.1109/TCE.2007.381734.
• [18] Manpreet Kaur, Jasdeep Kaur, Jappreet Kaur, “Survey of Contrast Enhancement Techniques based on Histogram Equalization”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 2, No. 7, 2011. https://doi.org/10.14569/IJACSA.2011.020721.
• [19] Vasile V. Buruloin, Mihai Ciuc, Raugaraj M. Rangavyan, Loic Kjj, Constantim Vertan., “Histogram equalization of colour images using the adaptive neighbourhood approach”, Proc. SPIE 3646, Nonlinear Image Processing, X, 330, 1999. https://doi.org/10.1117/12.341099.
• [20] Jyoti Singhai, Paresh Rawat, "Image Enhancement Method for Underwater, Ground and Satellite Images Using Brightness Preserving Histogram Equalization with Maximum Entropy," iccima, vol. 3, pp.507-512, 2007 International Conference on Computational Intelligence and Multimedia Applications, 2007. https://doi.org/10.1109/ICCIMA.2007.359.
• [21] Etta D, Pisano, S. Zong, R. E Jhonston “Contrast limited adaptive histogram equalization image processing to improve the detection of simulated speculation in Dense Monograms”, Journal of Digital Imaging, vol. 11, No. 4, pp 193-200, 1998. https://doi.org/10.1007/BF03178082.
• [22] Y.-T. Kim Contrast Enhancement Using Brightness Preserving Bi-Histogram Equalization , IEEE Transactions on Consumer Electronics, Vol. 43, No. 1, FEBRUARY 1997. https://doi.org/10.1109/30.580378.
• [23] Prateek S. Sengar, Tarun K. Rawat, Harish Parthasarathy, “Color Image Enhancement by Scaling the Discrete Wavelet Transform Coefficients”, IEEE International Conference on Microelectronics, Communication and Renewable Energy, (ICMiCR), 2013. https://doi.org/10.1109/AICERA-ICMiCR.2013.6575994.
• [24] K. K. Sharma, Supriya M.,” Color Image Enhancement using Nonlinear Mapping in Color and Transform Domains”, International Journal of Advanced Electronics & Communication Systems, Issue 2 Vol. 1 2012.
• [25] Jayanta M., and Sanjit K. Mitra, “Enhancement of Color Images by Scaling the DCT Coefficients”, IEEE Transactions on Image Processing, Vol. 17, No. 10, pp. 1783-1794, 2008. https://doi.org/10.1109/TIP.2008.2002826.
• [26] Y.-T. Kim Contrast Enhancement Using Brightness Preserving Bi-Histogram Equalization , IEEE Transactions on Consumer Electronics, Vol. 43, No. 1, FEBRUARY 1997. https://doi.org/10.1109/30.580378.
• [27] D. Martin, C. Fowlkes, and J. Malik, “Learning to detect natural image boundaries using local brightness, color, and texture cues,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 5, pp. 530-549, May 2004. https://doi.org/10.1109/TPAMI.2004.1273918.
• [28] M. Alipoor, S. Imandoost, and J. Haddadnia, “Designing edge detection filters using particle swarm optimization,” in Proc. ICEE, 2010, pp. 548-552. https://doi.org/10.1109/IRANIANCEE.2010.5507008.
• [29] W. Fu, M. Johnston, and M. Zhang, “A hybrid particle swarm optimisation with differential evolution approach to image segmentation,” in Proc. Int. Conf. EvoApplicat., 2011, pp. 173-182. https://doi.org/10.1007/978-3-642-20525-5_18.
• [30] A. Mohemmed, Z. Mengjie, and M. Johnston, “Particle swarm optimization based AdaBoost for face detection,” in Proc. IEEE Congr. Evol. Comput., May 2009, pp. 2494-2501. https://doi.org/10.1109/CEC.2009.4983254.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).