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https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

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We present a novel architecture for region-based segmentation of stationary and quasi-stationary statistics, which is designed to function correctly under the widest range of conditions. It is robust to the extremes of region topology and connectivity, and automatically maintains region boundaries sampled to the minimum scale at which the region configuration can be determined with statistical confidence. The algorithm is deterministic, and when operating on images from within its domain of validity, contains no adjustable parameters. In contrast to most other techniques directed at the same problem, the progress of the algorithm cannot be described by the optimisation of a global energy criterion.

Efficient region segmentation through ‘creep-and-merge’