A Model for Segmentation and Analysis of Noisy Images Academic Article uri icon

abstract

  • This article proposes a statistical model for image generation that provides automatic segmentation of images into intensity-differentiated regions and facilitates the quantitative assessment of uncertainty associated with identified image features. The model is specified hierarchically within the Bayesian paradigm. At the lowest level in the hierarchy, a Gibbs distribution is used to specify a probability distribution on the space of all possible partitions of the discretized image scene. An important feature of this distribution is that the number of partitioning elements, or image regions, is not specified a priori. At higher levels in the hierarchical specification, random variables representing emission intensities are associated with regions and pixels. Observations are assumed to be generated from exponential family models centered about these values. 1994 Taylor & Francis Group, LLC.

published proceedings

  • Journal of the American Statistical Association

author list (cited authors)

  • Johnson, V. E.

citation count

  • 34

complete list of authors

  • Johnson, Valen E

publication date

  • January 1994