[IPOL announce] new article: A Parallel, O(n) Algorithm for an Unbiased, Thin Watershed

[announcements about the IPOL journal]

A new article is available in IPOL: https://www.ipol.im/pub/art/2022/215/

Théodore Chabardès, Petr Dokládal, Matthieu Faessel, and Michel Bilodeau,
A Parallel, O(n) Algorithm for an Unbiased, Thin Watershed,
Image Processing On Line, 12 (2022), pp. 50–71.
https://doi.org/10.5201/ipol.2022.215

Abstract
The watershed transform is a powerful tool for morphological 
segmentation. Most common implementations of this method involve a 
strict hierarchy on gray tones in processing the pixels composing an 
image. This hierarchical dependency complexifies the efficient use of 
modern computational architectures. This paper introduces a new way of 
computing the watershed transform that alleviates the sequential nature 
of hierarchical queue propagation. It is shown that this method can 
directly relate to the hierarchical flooding. Simultaneous and 
disorderly growth can now be used to maximize performances on modern 
architectures. Higher speed is reached, bigger data volume can be 
processed. Experimental results show increased performances regarding 
execution speed and memory consumption.