Estimating Scene Properties by Analyzing Color Histograms With Physics-Based Models
Abstract
The goal of machine vision is to allow intelligent systems to describe the world around them by the interpretation of images. The difficulty is that vision is a very complex process, since images may contain shadows, highlights, interreflections, and other phenomena. Images are created through the interaction of light with the world; therefore, any vision system that is to understand images must have a model of those interactions. By using physics- based models to describe image formation, we can analyze images in a systematic way. In applying physical models to machine vision, one of the key tools has been color histogram analysis. A color histogram shows the variation of colors observed within the scene. In the mid-1980s, it was recognized that the color variation for a single inhomogeneous surface can be modeled as a regular physical process with a planar distribution in color space. The identification of this plane and the vectors that define it leads directly to an analysis of object color and illumination color. However there is much more to be said about color histograms. The colors do not fall randomly in a plane, but form clusters at specific points in color space. The colors in the histogram relate not only to the color of the object and the illumination, but also to non-color properties of surface roughness and imaging geometry.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 01, 1992
- Accession Number
- ADA262727
Entities
People
- Carol L. Novak
Organizations
- Carnegie Mellon University