Year: 2019
Type: article
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.
Source: Image Processing On Line
Authors: Pierre Cettour-Janet, Clément Cazorla, Vaia Machairas, Quentin Delannoy, Nathalie Bednarek, François Rousseau, Etienne Décencière, Nicolas Passat
Institutions: Centre de Recherche en Sciences et Technologies de l'Information et de la Communication, Université de Reims Champagne-Ardenne, Université Paris Sciences et Lettres, Centre de Morphologie Mathématique - CMM, École Nationale Supérieure des Mines de Paris, Centre Hospitalier Universitaire de Reims, Inserm, IMT Atlantique, Laboratoire de Traitement de l'Information Médicale
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Funder: Agence Nationale de la Recherche
OpenAlex ID: W4241833802
