Watervoxels
Pierre Cettour-Janet, Clément Cazorla, Vaia Machairas, Quentin Delannoy, Nathalie Bednarek, François Rousseau, Etienne Décencière, Nicolas Passat
published
2019-10-08
reference
Pierre Cettour-Janet, Clément Cazorla, Vaia Machairas, Quentin Delannoy, Nathalie Bednarek, François Rousseau, Etienne Décencière, and Nicolas Passat, Watervoxels, Image Processing On Line, 9 (2019), pp. 317–328. https://doi.org/10.5201/ipol.2019.250

Communicated by Pascal Monasse and Sébastien Drouyer
Demo edited by Sébastien Drouyer

Abstract

In this article, we present the n-dimensional version of the waterpixels, namely the watervoxels. Waterpixels constitute a simple, yet efficient alternative to standard superpixel paradigms, initially developed in the field of computer vision for reducing the space cost of input images without altering the accuracy of further image processing/analysis procedures. Waterpixels were initially proposed in a 2-dimensional version. Their extension to 3-dimensions -and more generally n-dimensions- is however possible, in particular in the Cartesian grid. Indeed, waterpixels mainly rely on a seeded watershed transformation applied on a saliency map defined as the linear combination of a gradient map and a distance map. We propose a description of the algorithmics of watervoxels in n-dimensional Cartesian grids. We also discuss its parameters and its time cost. A source code for 2- and 3-dimensional versions of watervoxels is provided, such as a 2-dimensional demonstrator. This article can be seen as the companion of the article 'Waterpixels', published in 2015 in IEEE Transactions on Image Processing.

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