Abstract
We are focused on a new fast and robust algorithm of image/signal feature extraction in the form of representative keypoints. We analyze various multi-scale representations of a one-dimensional signal in spaces with a closeness relation determined by a symmetric and positive semi-definite kernel. We show that kernels arising from generating functions of fuzzy partitions can be used in a scale space representation of a one-dimensional signal. We show that the reconstruction from the proposed multi-scale representations is of better quality than the reconstruction from MLP with almost double the number of neurons in 4 hidden layers. Finally, we propose a new algorithm of keypoints localization and description and test it on financial time series with high volatility.
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Acknowledgment
The work was supported from ERDF/ESF by the project “Centre for the development of Artificial Inteligence Methods for the Automotive Industry of the region” No. \(CZ.02.1.01/0.0/0.0/17_049/0008414\). Additional support of the grant project SGS18/PrF-MF/2021 (Ostrava University) is kindly announced.
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Perfilieva, I., Adamczyk, D. (2021). Features as Keypoints and How Fuzzy Transforms Retrieve Them. In: Rojas, I., Joya, G., Català, A. (eds) Advances in Computational Intelligence. IWANN 2021. Lecture Notes in Computer Science(), vol 12862. Springer, Cham. https://doi.org/10.1007/978-3-030-85099-9_2
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DOI: https://doi.org/10.1007/978-3-030-85099-9_2
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