On Segmentation of Signals, Time Series, and Images.

Abstract

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. A model for representing such signals, time series, and images is discussed. Some approaches to estimation and segmentation in this model are presented. Keywords include: Statistical pattern recognition, Classification, Temporal correlation, Spatial correlation; and Optimization by relaxation method.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1985
Accession Number
ADA153396

Entities

People

  • S. L. Sclove

Organizations

  • University of Illinois at Chicago

Tags

Communities of Interest

  • Biomedical
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Business Administration
  • Computer Vision
  • Data Analysis
  • Image Processing
  • Image Segmentation
  • Information Processing
  • Information Science
  • Pattern Recognition
  • Probability
  • Random Variables
  • Recognition
  • Signal Processing
  • Statistical Inference
  • Statistics
  • Stochastic Processes

Fields of Study

  • Engineering

Readers

  • Computer Vision.
  • Radio communications and signal processing.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference