OPTIMUM TECHNIQUES FOR LINEAR FEATURE EXTRACTION.

Abstract

Methods are developed for processing a set of measurements to extract their most important features. The problem is approached from the viewpoint of pattern recognition, assuming two pattern classes, with particular emphasis given to the signal-in-noise detection problem. It is supposed that the feature extractor transforms the measurements into a finite-dimensional feature space, of smaller dimension than the measurement space. (Author)

Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1969
Accession Number
AD0708510

Entities

People

  • D. G. Lainiotis
  • T. L. Henderson

Organizations

  • University of Texas at Austin

Tags

DTIC Thesaurus Topics

  • Change Detection
  • Computational Processes
  • Computing-Related Activities
  • Detection
  • Digital Image Processing
  • Extraction
  • Feature Extraction
  • Image Processing
  • Measurement
  • Pattern Recognition
  • Recognition

Readers

  • Computer Vision.
  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Space