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Conference papers

Binomial Convolutions and Derivatives Estimation from Noisy Discretizations

Rémy Malgouyres 1, * Florent Brunet 1 Sébastien Fourey 2
* Corresponding author
2 Equipe Image - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image et Instrumentation de Caen
Abstract : We present a new method to estimate derivatives of digitized functions. Even with noisy data, this approach is convergent and can be computed by using only the arithmetic operations. Moreover, higher order derivatives can also be estimated. To deal with parametrized curves, we introduce a new notion which solves the problem of correspondence between the parametrization of a continuous curve and the pixels numbering of a discrete object.
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https://hal.archives-ouvertes.fr/hal-00333776
Contributor : Sébastien Fourey Connect in order to contact the contributor
Submitted on : Friday, October 24, 2008 - 10:36:31 AM
Last modification on : Tuesday, October 19, 2021 - 11:34:55 PM

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Rémy Malgouyres, Florent Brunet, Sébastien Fourey. Binomial Convolutions and Derivatives Estimation from Noisy Discretizations. Discrete Geometry for Computer Imagery, Apr 2008, Lyon, France. pp.370-379, ⟨10.1007/978-3-540-79126-3_33⟩. ⟨hal-00333776⟩

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