Abstract
Segmentation is considered an important part of image processing. There are commonly used segmentation techniques to improve the threshold process such as Otsu and Kapur. The use of these techniques allows us to find the regions of interest in an image by correctly grouping the pixel intensity levels. On the other hand, the use of thermal images makes it possible to obtain information about the temperature of an object and to capture the infrared radiation of the electromagnetic spectrum, through cameras that transform the radiated energy into heat information. The segmentation of this kind of images represents a challenging problem that requires a huge computational effort. This work proposes the use of metaheuristic algorithms, combined with segmentation techniques and thermal images, to detect faults and contribute to the preventive maintenance of electronic systems.
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References
P. Anitha, S. Bindhiya, A. Abinaya et al., RGB image multi-thresholding based on Kapur’s entropy—a study with heuristic algorithms, in Proceedings of 2017 2nd IEEE International Conference on Electrical, Computer and Communication Technologies, ICECCT 2017 (2017), pp. 0–5. https://doi.org/10.1109/ICECCT.2017.8117823
S. Bangare, S. Patil, Reviewing Otsu’s Method for Image Thresholding (2016)
J. Coates, Encyclopedia of analytical chemistry, in Interpretation of Infrared Spectra, A Practical Approach (2004), pp. 1–23
C.A. Balaras, A.A. Argiriou, Infrared thermography for building diagnostics. Energy Build. 34, 171–183 (2002). https://doi.org/10.1016/S0378-7788(01)00105-0
X.-S. Yang, A new metaheuristic bat-inspired algorithm, in Encyclopaedia of Networked and Virtual Organizations (2010), pp. 65–74
M. Mareli, B. Twala, An adaptive Cuckoo search algorithm for optimisation. Appl. Comput. Inf. 14, 107–115 (2018). https://doi.org/10.1016/j.aci.2017.09.001
I. Koohi, V.Z. Groza, Optimizing Particle Swarm Optimization algorithm, in Canadian Conference on Electrical and Computer Engineering (2014), pp. 1–5. https://doi.org/10.1109/CCECE.2014.6901057
R. Storn, K. Price, Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 1, 341–359 (1997). https://doi.org/10.1023/A:1008202821328
L. Zhang, L. Zhang, X. Mou, D. Zhang, FSIM: a feature similarity index for image quality assessment. IEEE Trans. Image Process 20, 2378–2386 (2011). https://doi.org/10.1109/TIP.2011.2109730
Z. Wang, A.C.A.C. Bovik, H.R.H.R. Sheikh, E.P.E.P. Simoncelli, Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process 13, 600–612 (2004). https://doi.org/10.1109/TIP.2003.819861
A. Horé, D. Ziou, Image quality metrics: PSNR vs. SSIM, in Proceedings—International Conferenceon Pattern Recognition (2010), pp. 2366–2369. https://doi.org/10.1109/ICPR.2010.579
T. Chai, R.R. Draxler, Root mean square error (RMSE) or mean absolute error (MAE)?—Arguments against avoiding RMSE in the literature. Geosci. Model Dev. 7, 1247–1250 (2014). https://doi.org/10.5194/gmd-7-1247-2014
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Appendix
Appendix
The parameters used in each method have been configured according to the reported values in which their best performance is achieved, below is the configuration of these settings, every algorithm was tested using 50 particles of population (Table 5).
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Navarro, M.A., Hernández, G.R., Zaldívar, D., Ortega-Sanchez, N., Pajares, G. (2020). Segmentation of Thermal Images Using Metaheuristic Algorithms for Failure Detection on Electronic Systems. In: Oliva, D., Hinojosa, S. (eds) Applications of Hybrid Metaheuristic Algorithms for Image Processing. Studies in Computational Intelligence, vol 890. Springer, Cham. https://doi.org/10.1007/978-3-030-40977-7_1
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