The Use of Texture Measures in Improving Mine Classification Performance

Abstract

Research over the last 9 years has resulted in an effective mine classification approach that involves the use of image-segmentation based screening methods followed by multilayer perceptron networks for mine classification. The present approach centers around a baseline 23 Feature set related to highlight, shadow, and highlight/shadow contrast statistic based segmentations, and the use of associated statistical and shape related factors. In the work described here we investigate the improvement of baseline performance by incorporating image texture related features such as Cooccurrence Matrix related factors.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2003
Accession Number
ADA498466

Entities

People

  • Gerald J. Dobeck
  • Martin G. Bello

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Classification
  • Computer Vision
  • Data Sets
  • Detection
  • Discrete Distribution
  • Distribution Functions
  • False Alarms
  • Feature Selection
  • Filtration
  • Genetic Algorithms
  • Information Science
  • Intensity
  • Machine Learning
  • Statistics
  • Warning Systems

Fields of Study

  • Computer science

Readers

  • Acoustical Oceanography.
  • Neural Network Machine Learning.