Features as Keypoints and How Fuzzy Transforms Retrieve Them

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F21%3AA2202APN" target="_blank" >RIV/61988987:17610/21:A2202APN - isvavai.cz</a>
Result on the web
<a href="https://www.springerprofessional.de/en/features-as-keypoints-and-how-fuzzy-transforms-retrieve-them/19587028?searchResult=1.Perfilieva&searchBackButton=true" target="_blank" >https://www.springerprofessional.de/en/features-as-keypoints-and-how-fuzzy-transforms-retrieve-them/19587028?searchResult=1.Perfilieva&searchBackButton=true</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Features as Keypoints and How Fuzzy Transforms Retrieve Them
Original language description
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.
Czech name
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Czech description
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Project
<a href="/en/project/EF17_049%2F0008414" target="_blank" >EF17_049/0008414: Centre for the development of Artificial Intelligence Methods for the Automotive Industry of the region</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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