Statistical Models and Methods for Cluster Analysis and Image Segmentation.
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 x's 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)
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 15, 1986
- Accession Number
- ADA169145
Entities
People
- Stanley L. Sclove
Organizations
- University of Illinois at Chicago