Abstract
In many problems of computer vision, there are some problems for the estimation of homography matrix between images, the homography matrix is necessary to solve different problems in this area of science, one of them is Stitching, which in simple words is the process by several images are combined to produce a panoramic image or a high resolution image, usually through a computer program. The homography matrix is a fundamental basis to perform the Stitching on the images, there are many methods to calculate the homography, the most common to find this estimation is the random sampling consensus (RANSAC). But there are some works that consider the estimation process in a different way, the way in which these works deal with the problem is taking the problem as a multi-objective estimation process, with this approach it is possible to facilitate the calculation of multidimensional problems. In order to solve the multi-objective formulation, many different evolutionary algorithms have been explored, obtaining good results in their tests. In this chapter the problem of the estimation of the homography matrix is considered as a problem of multi-objective optimization and will be faced with the evolutionary algorithm ABC.
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References
P. Brajendra, S. Sudeep, B. Usha, in Innovations in Computational Intelligence: Best Selected Papers of the Third International Conference on REDSET 2016. Studies in Computational Intelligence, vol 713 (2016), p. 212
S. Mann, R. Picard, Virtual bellows: constructing high-quality images from video, in Proceedings of the IEEE First International Conference on Image Processing. IEEE International Conference, 13–16 November 1994 (IEEE, Austin, Texas, 1994)
B. Zitová, J. Flusser, Image registration methods: a survey. Image Vis. Comput. 21(11), 977–1000, 978–980 (2003). https://doi.org/10.1016/s0262-8856(03)00137-9
B. Zitová, J. Flusser, Image registration methods: a survey. Image Vis. Comput. 21(11), 977–1000, 977 (2003). https://doi.org/10.1016/s0262-8856(03)00137-9
B. Zitová, J. Flusser, Image registration methods: a survey. Image Vis. Comput. 21(11), 977–1000, 978 (2003). https://doi.org/10.1016/s0262-8856(03)00137-9
R. Szeliski, Image alignment and stitching: a tutorial. Found. Trends® Comput. Graph. Vis. 2(1), 1–104 (2006). https://doi.org/10.1561/0600000009
R. Szeliski, Computer Vision: Algorithms and Applications (Springer, Berlin, 2010) (online draft)
R. Hartley, A. Zisserman, Multiple View Geometry in Computer Vision (Cambridge University Press, Cambridge, 2004)
J. Lee, G. Kim, Robust estimation of camera homography using fuzzy RANSAC, in Computational Science and Its Applications—ICCSA 2007 (2007), pp. 992–1002. https://doi.org/10.1007/978-3-540-74472-6_81
T. Back, Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms (Oxford University Press, Oxford, 1996), p. 7
V. Erik, V. Jose, A. Diego, A. Margarita, Optimizacion de algoritmos programados con MATLAB (Alfaomega, Mexico, 2016) p. XIV
V. Erik, V. Jose, A. Diego, A. Margarita, Optimizacion de algoritmos programados con MATLAB (Alfaomega, Mexico, 2016), pp. 19–24
V. Erik, V. Jose, A. Diego, A. Margarita, Optimizacion de algoritmos programados con MATLAB (Alfaomega, Mexico, 2016) p. 150.
V. Kachitvichyanukul, Comparison of three evolutionary algorithms: GA, PSO, and DE. Ind. Eng. Manage. Syst. 11(3), 215–223 (2012)
D. Karaboga, An idea based on honey bee swarm for numerical optimization. Technical Report—TR06, Erciyes University, Kayseri, Turkey (2005)
D. Karaboga, B. Basturk, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Global Optim. 39, 171–459 (2007)
D. Karaboga, B. Basturk, A comparative study of artificial bee colony algorithm. Appl. Math. Comput. 214, 108–132 (2009)
Department of Radiology—Medical Physics, University Medical Center Freiburg DCE-MRI-image-registration_en.png. https://www.uniklinik-freiburg.de/mr-en/research-groups/postprocessing
Complete Process to Stitch Images (Image registration methods) RegistrationMethod.png. https://doi.org/10.1016/S0262-8856(03)00137-9
Evolutionary Algorithm Avoiding Local Minimums (Simulated-annealing-optimization-of-a-one-dimensional-objective-function.png). https://medium.com/@duoduoyunnini/introduction-implementation-and-comparison-of-four-randomized-optimization-algorithms-fc4d96f9feea
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Ascencio, C. (2020). Estimation of the Homography Matrix to Image Stitching. 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_10
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