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• W2063602776 abstract "Optical flow methods are accurate algorithms for estimating the displacement and velocity fields of objects in a wide variety of applications, being their performance dependent on the configuration of a set of parameters. Since there is a lack of research that aims to automatically tune such parameters, in this work we have proposed an evolutionary-based framework for such task, thus introducing three techniques for such purpose: Particle Swarm Optimization, Harmony Search and Social-Spider Optimization. The proposed framework has been compared against with the well-known Large Displacement Optical Flow approach, obtaining the best results in three out eight image sequences provided by a public dataset. Additionally, the proposed framework can be used with any other optimization technique." @default.
• W2063602776 created "2016-06-24" @default.
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• W2063602776 date "2014-08-01" @default.
• W2063602776 modified "2023-10-01" @default.
• W2063602776 title "Evolutionary Optimization Applied for Fine-Tuning Parameter Estimation in Optical Flow-Based Environments" @default.
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• W2063602776 doi "https://doi.org/10.1109/sibgrapi.2014.22" @default.
• W2063602776 hasPublicationYear "2014" @default.
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