Using Color to Separate Reflection Components.

Abstract

This paper presents an algorithm for analyzing a standard color image to determine intrinsic images of the amount of interface (specular) and body (diffuse) reflection at each pixel. The interface reflection represents the highlights from the original image, and the body reflection represents the original image with highlights removed. Such intrinsic images are of interest because the geometric properties of each type of reflection are simpler than the geometric properties of intensity in a black-and-white image. The algorithm is based upon a physical model of reflection which states that two distinct types of reflection--interface and body reflection--occur, and that each type can be decomposed into a relative spectral distribution and a geometric scale factor. This model is far more general than typical models used in computer vision and computer graphics, and includes most such models as special cases. In addition, the model does not assume a point light source or uniform illumination distribution over the scene. The properties of spectral projection into color space are used to derive a new model of pixel-value color distribution, and this model is exploited in an algorithm to derive the intrinsic images. Suggestions are provided for extending the model to deal with diffuse illumination and for analyzing the intrinsic images of reflection. Additional keywords: Dischromatic reflection model. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Apr 02, 1984
Accession Number
ADA150999

Entities

People

  • S. A. Shafer

Organizations

  • University of Rochester

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Angle Of Incidence
  • Computer Graphics
  • Computer Science
  • Computer Vision
  • Geometry
  • Graphics
  • Intensity
  • Light Sources
  • Materials
  • Optics
  • Physical Properties
  • Reflection
  • Refraction
  • Refractive Index
  • Scattering
  • Three Dimensional

Readers

  • Acoustical Oceanography.
  • Graph Algorithms and Convex Optimization.
  • Human-Computer Interaction (HCI).

Technology Areas

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