Waterpixel

• 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

Watervoxel

• 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.

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

References

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.