In this paper we present a new stochastic model for pix- els in an edge-enhanced image. The model is robust because it allows for the possibilities of false and multiple edges, and may be efficiently estimated using an expectation-maximization technique with a minimum description length metric. The direct applicability of the model for the sequential edge linking algorithm is investigated and shown to improve edge detection for low signal-to-noise ratio cases. © 2004 SPIE and IS&T. (DOI: 10.1117/1.1790507)
['Gregory W. Cook', 'Edward J. Delp']
Gaussian mixture model for edge-enhanced images