Accession Number : ADA118344

Title :   On Segmentation of Time Series and Images in the Signal Detection and Remote Sensing Contexts.

Descriptive Note : Technical rept.,

Corporate Author : ILLINOIS UNIV AT CHICAGO CIRCLE

Personal Author(s) : Sclove,Stanley L

PDF Url : ADA118344

Report Date : 01 Aug 1982

Pagination or Media Count : 22

Abstract : The problem of partitioning a time-series into segments is considered. The segments fall into classes, which may correspond to phases of a cycle (recession, recovery, expansion in the business cycle) or to portions of a signal obtained by scanning (background/ clutter, target, background/clutter again, another target, etc.), or normal tissue, tumor, normal tissue in medical applications. A probability distribution is associated with each class of segment. Parametric families of distributions are considered, a set of parameter values being associated with each class. With each observation is associated an unobservable label, indicating from which class the observation arose. The label process is modeled as a Markov chain. Segmentation algorithms are obtained by applying a method of iterated maximum likelihood to the resulting likelihood function. In this paper special attention is given to the situation in which the observations are conditionally independent, given the labels. A numerical example is given. Choice of the number of classes, using Akaike's information criterion (AIC) for model identification, is illustrated. Similar ideas are applied to the problem of segmenting digital images, where possible applications include SEASAT (and LANDSAT) multi-spectral images. (Author)

Descriptors :   *Images, *Time series analysis, *Segmented, *Detection, Mathematical prediction, Cycles, Forecasting, Background, Markov processes, Probability distribution functions, Maximum likelihood estimation, Clutter, Signal processing, Regression analysis, Identification, Commerce, Autocorrelation, Neoplasms, Models, Value, Algorithms, Iterations, Signals, Paper, Parameters

Subject Categories : Statistics and Probability

Distribution Statement : APPROVED FOR PUBLIC RELEASE