A SCHEME FOR LINEAR RECOGNITION BASED ON SELF-DETERMINED INTER-CATEGORY FEATURES.

Abstract

In a pattern recognition problem, where observations are described by a mxn grid, it is often the case that each pattern is reduced to a 'mask' and discrimination is performed by comparing an unknown to each mask. Assignment is then determined by the closest mask. This paper discusses a preprocessing technique for feature extraction to reduce the size of the masks or to extract those mask sub-areas most pertinent for recognition. Statistical tests are applied to determine uncommon regions (i.e., differences) between masks. All subsequent recognition is based on and emphasizes these uncommon regions (as distinguishing features). The resulting weights of this method are controlled by differences between the groups and thus cannot be separated into a characteristic set of weights from each individual group. Moreover, this method provides the freedom to elect the level of closeness that the categories must satisfy. (Author)

Document Details

Document Type
Technical Report
Publication Date
Jul 15, 1965
Accession Number
AD0628708

Entities

People

  • Joel Owen

Tags

DTIC Thesaurus Topics

  • Data Science
  • Discrimination
  • Extraction
  • Feature Extraction
  • Identification
  • Information Science
  • Observation
  • Pattern Recognition
  • Preprocessing
  • Recognition
  • Statistical Tests

Readers

  • Computer Vision.
  • Materials Science

Technology Areas

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