@book{64aa48de972b40d69fed46f107c52b9b,

title = "3-D contextual Bayesian classifiers",

abstract = "In this paper we will consider extensions of a series of Bayesian 2-D contextual classification pocedures proposed by Owen (1984) Hjort & Mohn (1984) and Welch & Salter (1971) and Haslett (1985) to 3 spatial dimensions. It is evident that compared to classical pixelwise classification further information can be obtained by taking into account the spatial structure of image data. The 2-D algorithms mentioned above consist of basing the classification of a pixel on the simultaneous distribution of the values of a pixel and its four nearest neighbours. This includes the specification of a Gaussian distribution for the pixel values as well as a prior distribution for the configuration of class variables within the cross that is made of a pixel and its four nearest neighbours. We will extend these algorithms to 3-D, i.e. we will specify a simultaneous Gaussian distribution for a pixel and its 6 nearest 3-D neighbours, and generalise the class variable configuration distributions within the 3-D cross given in 2-D algorithms. The new 3-D algorithms are tested on a synthetic 3-D multivariate dataset.",

keywords = "Classification, Segmentation, Contextual methods, 3-D",

author = "Rasmus Larsen",

year = "1997",

language = "English",

publisher = "Department of Mathematical Modelling, Technical University of Denmark",

}