Blue-noise sampling for human retinal cone spatial distribution modeling

Matteo P. Lanaro Hélène Perrier 1 David Coeurjolly 1 Victor Ostromoukhov 2 Alessandro Rizzi 3
1 M2DisCo - Geometry Processing and Constrained Optimization
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
2 R3AM - Rendu Réaliste pour la Réalité Augmentée Mobile
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : This paper proposes a novel method for modeling human retinal cone distribution. It is based on Blue-noise sampling algorithms that share interesting properties with the sampling performed by the mosaic formed by cone photoreceptors in the retina. Here we present the method together with a series of examples of various real retinal patches. The same samples have also been created with alternative algorithms and compared with plots of the center of the inner segments of cone photoreceptors from imaged retinas. Results are evaluated with different distance measure used in the field, like nearest-neighbor analysis and pair correlation function. The proposed method can describe features of a human retinal cone distribution with a certain degree of similarity to the available data and can be efficiently used for modeling local patches of retina.
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Preprints, Working Papers, ...
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https://hal.archives-ouvertes.fr/hal-02155785
Contributor : David Coeurjolly <>
Submitted on : Thursday, June 13, 2019 - 8:23:58 PM
Last modification on : Thursday, November 21, 2019 - 2:02:56 AM

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  • HAL Id : hal-02155785, version 1
  • ARXIV : 1906.05075

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Matteo P. Lanaro, Hélène Perrier, David Coeurjolly, Victor Ostromoukhov, Alessandro Rizzi. Blue-noise sampling for human retinal cone spatial distribution modeling. 2019. ⟨hal-02155785⟩

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