Recovering Intrinsic Scene Characteristics from Images

Abstract

The central theme of our research is the recovery of information about the three-dimensional structure and physical characteristics of surfaces depicted in an image -- their shapes, locations, and photometric properties. The main obstacle to surface recovery is the confounding of the desired properties in the sensory data: images are inherentlly ambiguous. Our approach to resolving this ambiguity rests on the application of generic, low-level knowledge (e.g., such basic assumptions as surface continuity and general position) to constrain the interpretation. The problem may be viewed as that of decomposing the image into its physically meaningful constituents -- surface orientation, reflectance, illumination, and so on. The 'intrinsic image model' provides a conceptual and computational framework in which this view is made explicit. Surface perception plays a fundamental role in early visual processing, both in humans and in machines. Work on surface perception has focused on the discrimination of edge types (e.g., extremal boundary or cast shadow), on the three-dimensional interpretation of edges, and on surface reconstruction by interpolating from edges and using texture geometry.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1981
Accession Number
ADA110757

Entities

People

  • Andrew P. Witkin
  • Martin A. Fischler

Organizations

  • SRI International

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Intelligence Software
  • Automata Theory
  • Collision Avoidance
  • Computer Programs
  • Computer Science
  • Computer Vision
  • Computers
  • Continuity
  • Contracts
  • Coordinate Systems
  • Image Recognition
  • Military Research
  • Object Recognition
  • Perception
  • Shape
  • Three Dimensional

Readers

  • Materials Science and Engineering.
  • Systems Analysis and Design
  • Vision Science/Vision Psychology/Cognitive Neuroscience.