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Smooth Mixture Estimation from Multichannel Image Data

In: Computing Science and Statistics

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  • Finbarr O’Sullivan

    (University of Washington, Dept. of Biostatistics and Statistics)

Abstract

The following problem arises in computer vision, diagnostic medical imaging and remote sensing: At each pixel in an image a vector of observations is measured. The distribution of these measurements is modeled by a mixture of certain pure class distributions and the goal is to estimate the mixing proportions of the classes by pixel in the image together with any unknown parameters in the pure class distributions. In many problems of this type it is appropriate to incorporate constraints on the mixing proportions. This paper deals with spatial smoothness constraints. An estimation methodology using penalized likelihood with multiple smoothing parameters is applied. Numerical methods for evaluating parameters in the model are developed. We make use of the Expectation Maximization (EM) algorithm. An importance sampling technique for approximating the effective degrees of freedom of the model is also described. The methodology is illustrated with some examples.

Suggested Citation

  • Finbarr O’Sullivan, 1992. "Smooth Mixture Estimation from Multichannel Image Data," Springer Books, in: Connie Page & Raoul LePage (ed.), Computing Science and Statistics, pages 143-150, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4612-2856-1_18
    DOI: 10.1007/978-1-4612-2856-1_18
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