Abstract
The triangulation is a process by which the 3D point position can be calculated from two images where that point is visible. This process requires the intersection of two known lines in the space. However, in the presence of noise this intersection does not occur, then it is necessary to estimate the best approximation. One option towards achieving this goal is the usage of evolutionary algorithms. In general, evolutionary algorithms are very robust optimization techniques, however in some cases, they could have some troubles finding the global optimum getting trapped in a local optimum. To overcome this situation some authors suggested removing the local optima in the search space by means of a single-objective problem to a multi-objective transformation. This process is called multi-objectivization. In this paper we successfully apply this multi-objectivization to the triangulation problem.
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Vite-Silva, I., Cruz-Cortés, N., Toscano-Pulido, G., de la Fraga, L.G. (2007). Optimal Triangulation in 3D Computer Vision Using a Multi-objective Evolutionary Algorithm. In: Giacobini, M. (eds) Applications of Evolutionary Computing. EvoWorkshops 2007. Lecture Notes in Computer Science, vol 4448. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71805-5_36
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DOI: https://doi.org/10.1007/978-3-540-71805-5_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71804-8
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