Local Shape from Specularity,

Abstract

We show that highlights in images of objects with specularly reflecting surfaces provide significant information about the surfaces which generate them. A brief survey is given of specular reflectance models which have been used in computer vision and graphics. For our work, we adopt the Torrance-Sparrow specular model which, unlike most previous models, considers the underlying physics of specular reflection from rough surfaces. From this model we derive powerful relationships between the properties of a specular feature in an image and local properties of the corresponding surface. We show how this analysis can be used for both prediction and interpretation in a vision system. A shape from specularity system has been implemented to test our approach. The performance of the system is demonstrated by careful experiments with specularly reflecting objects.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1986
Accession Number
ADA327858

Entities

People

  • Glenn Healey
  • Thomas O. Binford

Organizations

  • Stanford University

Tags

Communities of Interest

  • Air Platforms
  • C4I

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Cameras
  • Computer Graphics
  • Computer Science
  • Computer Vision
  • Computers
  • Coordinate Systems
  • Curvature
  • Distribution Functions
  • Geometry
  • Light Sources
  • Materials
  • Shape
  • Specular Reflection
  • Statistics
  • Surface Properties
  • Surface Roughness

Fields of Study

  • Computer science
  • Physics

Readers

  • Electromagnetic Wave Scattering and Antenna Radiation Engineering
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference