Accession Number:

ADA169145

Title:

Statistical Models and Methods for Cluster Analysis and Image Segmentation.

Descriptive Note:

Final rept. 15 Jun 82-15 Jun 85,

Corporate Author:

ILLINOIS UNIV AT CHICAGO CIRCLE DEPT OF INFORMATION AND DECISION SCIENCES

Personal Author(s):

Report Date:

1986-03-15

Pagination or Media Count:

18.0

Abstract:

Clustering of individuals, segmentation of time series and segmentation of numerical images can all be considered as labeling problems, for each can be described in terms of pairs x sub t, g sub t, t 1,2,...,n, where x sub t is the observation at instance t and g sub t is the unobservable label of instance t. The labels are to be estimated, along with any unspecified distributional parameters. In cluster analysis the values of t are the individuals cases observed and the xs are independent. In time series the values of t are time instants and there is temporal correlation. In numerical image segmentation the values of t denote picture elements pixels and spatial correlation between neighboring pixels can be utilized. The idea in segmentation is that signals and time series often are not homogeneous but rather are generated by mechanisms or processes with various phases. Similarly, images are not homogeneous but contain various objects. Segmentation is a process of attempting to recover automatically the phases or objects. Keywords Statistical pattern recognition Classification Optimization by relaxation method. Author

Subject Categories:

  • Optical Detection and Detectors

Distribution Statement:

APPROVED FOR PUBLIC RELEASE