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Flood fill finds the region reachable from a seed pixel through allowed neighboring pixels that satisfy a membership rule, often replacing the values in that region. A paint program's fill operation is a familiar application. The seed, membership rule and pixel connectivity together specify what will be filled.

Consider the imageStart at the upper-left pixel, select values equal to one, and allow only horizontal or vertical steps. Exactly three pixels are reachable, giving replacement by nine asWith diagonal steps also allowed, all six ones are reachable and replaced. The fill is a connectivity computation, not a rule to replace every matching value everywhere.

An iterative implementation keeps pending positions in a stack, takes one position and adds eligible unvisited neighbors. Marking a position when it enters the pending collection prevents repeated insertion. This is a depth-first search of the implicit graph. For a fixed neighborhood, visiting a region of $R$ pixels takes $O(R)$ time complexity, excluding initialization or copying of a whole image. Explicit pending and visited storage avoids relying on a deeply nested recursive call for a large region.

Membership need not require exact equality. The scikit-image flood_fill API can accept values within a specified tolerance of the original seed value, with connectivity or a supplied neighborhood footprint controlling adjacency. Comparing each candidate to the seed is different from allowing a chain of small local changes to drift arbitrarily far from the seed value.^[ref]

Record visited positions independently when necessary: replacing a value by itself must not make the algorithm revisit it forever. Repeated flood fills with fresh region labels can implement connected-component labeling.