1 October 2004 Gaussian mixture model for edge-enhanced images
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J. of Electronic Imaging, 13(4), (2004). doi:10.1117/1.1790507
Abstract
In this paper we present a new stochastic model for pixels 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.
Gregory W. Cook, Edward J. Delp, "Gaussian mixture model for edge-enhanced images," Journal of Electronic Imaging 13(4), (1 October 2004). http://dx.doi.org/10.1117/1.1790507
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KEYWORDS
Expectation maximization algorithms

Edge detection

Detection and tracking algorithms

Data modeling

Image processing

Image segmentation

Signal to noise ratio

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