Waterpixels and Watervoxels



  • approach to simplify (large) images
  • is the reduction of the number of pixels (a.k.a image combinatorics) without the alteration of either of
    • pixel values (a.k.a spectral information or signature)
    • pixel organization (a.k.a pixel topology)
  • Practically it is difficult to satisfy the three constraints of
    • image size reduction
    • information preservation
    • structure non-alteration
  • Several superpixel paradigms exist:
    • waterpixels
      • an alternative to superpixel paradigms
      • are based on the watershed transformation
    • SLIC, Simple Linear Iterative Clustering
      • generates superpixels using k-means clustering


  • Cettour-Janet et al. 2019 (link) outlines their work on Watervoxels,
  • is an n-dimensional extension of the waterpixels, as defined in the article

The images below summarize the result of the application of watervoxels to a 2D image and a 3D MRI image.

Result of the watervoxel on the 2D image
Result of the watervoxel on the 3D MRI image

What are your thoughts on watervoxels? How do you make use of them in your field?


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

R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua and S. Süsstrunk, “SLIC Superpixels Compared to State-of-the-Art Superpixel Methods,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 11, pp. 2274-2282, Nov. 2012, doi: 10.1109/TPAMI.2012.120.