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Título:
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Robust fitting of Zernike polynomials to noisy point clouds defined over connected domains of arbitrary shape
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Autores:
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Rodríguez Ibáñez, Diego ;
Gómez Pedrero, José Antonio ;
Alonso Fernández, José ;
Quiroga Mellado, Juan Antonio
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Tipo de documento:
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texto impreso
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Editorial:
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The Optical Society Of America, 2016-03-21
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Dimensiones:
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application/pdf
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Nota general:
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info:eu-repo/semantics/openAccess
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Idiomas:
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Palabras clave:
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Estado = Publicado
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Materia = Ciencias: Física: Optica
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Materia = Ciencias Biomédicas: Óptica y optometría: Óptica oftálmica
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Materia = Ciencias Biomédicas: Óptica y optometría: Optoelectrónica
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Tipo = Artículo
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Resumen:
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A new method for fitting a series of Zernike polynomials to point clouds defined over connected domains of arbitrary shape defined within the unit circle is presented in this work. The method is based on the application of machine learning fitting techniques by constructing an extended training set in order to ensure the smooth variation of local curvature over the whole domain. Therefore this technique is best suited for fitting points corresponding to ophthalmic lenses surfaces, particularly progressive power ones, in non-regular domains. We have tested our method by fitting numerical and real surfaces reaching an accuracy of 1 micron in elevation and 0.1 D in local curvature in agreement with the customary tolerances in the ophthalmic manufacturing industry.
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En línea:
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https://eprints.ucm.es/37782/1/Robust%20fitting%20of%20Zernique-OSA%20optcs%20express%202016.pdf
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