Integrated Color Coding and Monochrome Multi-Spectral Fusion

Abstract

This paper describes a new integrated color coding and a contrast-based monochromatic fusion process. The fusion process is aimed for on board real time application and it is based on practical and computationally efficient image processing components. We developed two methods for color coding that utilize the monochrome fused image. Each of the color coding methods provides consistency of color presentation as a function of daytime, background variability and illumination conditions. The new monochrome fusion process maximizes the information content in the combined image, while retaining visual clues that are essential for navigation/piloting tasks. The method is a multi scale fusion process that provides a combination of pixel selection from a single image and a weighting of the two-multiple images. The spectral region is divided into spatial sub bands of different scales, and within each scale a combination rule for the corresponding pixels taken from the two components is applied. Even when the combination rule is a binary selection the combined fused image may have a combination of pixel values taken from the two components at various scales since it is taken at each scale. We also applied a combination rule that takes a weighted sum of the two pixel values.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1999
Accession Number
ADA391488

Entities

People

  • Eli Peli
  • Ken Ellis
  • Robert Stahl
  • Tamar Peli

Tags

Communities of Interest

  • Advanced Electronics
  • Biomedical
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Color Coding
  • Computer Vision
  • Detection
  • Detectors
  • Digital Images
  • Dynamic Range
  • Gray Scale
  • Image Processing
  • Infrared Detectors
  • Navigation
  • Night Vision
  • Object Recognition
  • Orientation (Direction)
  • Recognition
  • Video Images

Readers

  • Atmospheric Remote Sensing.
  • Computer Science/Computer Engineering/Data Science/Digital Signal Processing.
  • Neural Network Machine Learning.