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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机译：在本文中，我们介绍了水像素的n维版本，即水像素。水像素构成了标准超像素范例的一种简单而有效的替代方法，最初是在计算机视觉领域开发的，用于降低输入图像的空间成本而又不改变其他图像处理/分析程序的准确性。水像素最初是在二维版本中提出的。但是，可以将它们扩展到3维，更广泛地说是n维，尤其是在笛卡尔网格中。实际上，水像素主要依赖于种子显着性分水岭变换，该种子分水岭变换应用于显着图，该显着图定义为梯度图和距离图的线性组合。我们提出了n维笛卡尔网格中水素算法的描述。我们还将讨论其参数及其时间成本。提供了二维和三维水像素版本的源代码，例如二维演示器。本文可以与2015年在《 IEEE Transactions on Image Processing》上发表的“ Waterpixels”一词相随。
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