Digital image thresholding, based on topological stable-state

A new approach for image segmentation for scenes that contain distinct objects is presented. A sequence of graphs N"s(t) is defined, where N"s(t) is the number of connected objects composed of at least s pixels, for the image thresholded at t. The sequence of graphs is built in almost linear time complexity, namely at O(@a(n, n). n), where @a(n, n) is the inverse of the Ackermann function, and n is the number of pixels in the image. Stable states on the graph in the appropriate ''resolution'' s^* correspond to threshold values that yield a segmentation similar to a human observer. The relevance of a Percolation model to the graphs N"s(t) is discussed.

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