A Contrast Metric for 3-D Vehicles in Natural Lighting

Abstract

Ground vehicles in natural lighting tend to have significant and systematic variation in luminance over the presented area. This arises, in large part, from the vehicle surfaces having different orientations and shadowing relative to the source of illumination and the position of the observer. These systematic differences create the appearance of a structured 3-D object. 3-D appearance is an important factor in search, figure-ground segregation and object recognition. This paper presents a contrast metric based on the 3-D structure of the vehicle, and an analysis of search performance for the Search_2 imagery. The analysis employs the traditional P-infinity-times-negative-exponential model of search time distribution. P-infinity and mean search time are modeled as functions of the target signature. The signature metric is one over the product of vehicle size and contrast. The value of the metric is measured by the ability to account for variance in observed search performance. The 3-D structure contrast metric performs better than RSS contrast, and both perform dramatically better than the area- weighted average contrast. Target height performs better than either target area or square root of area. The signature metric accounts for over 80% of the variance in probability of detection and 75% of the variance in search time as measured in the TNO perception tests. When false alarm effects are discounted, the metric accounts for 89% of the variance in probability of detection and 95% of the variance in search time. The predictive power of the signature metric when it is calibrated to half the data and evaluated against the other half, is 90% of the explanatory power.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2000
Accession Number
ADP010542

Entities

People

  • G. Gerhart
  • G. Witus

Tags

Communities of Interest

  • Advanced Electronics
  • Energy and Power Technologies
  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Acquisition
  • Computer Vision
  • Data Sets
  • Detection
  • False Alarms
  • False Targets
  • Gray Scale
  • Object Recognition
  • Perception
  • Probability
  • Random Variables
  • Square Roots
  • Target Acquisition
  • Target Detection
  • Target Signatures
  • Three Dimensional
  • Two Dimensional

Readers

  • Computer Vision.
  • Regression Analysis.