An autonomous approach for learning the colors of specific objects assumed to have known body spectral reflectances is developed for daylight illumination conditions. The main issue is to be able to find these objects autonomously in a set of training images captured under a wide variety of daylight illumination conditions, and to extract their colors to determine color space regions that are representative of the objects' colors and their variations. The work begins by modeling color formation under daylight using the color formation equations and the semi-empirical model of Judd, MacAdam and Wyszecki (CIE daylight model) for representing the typical spectral distributions of daylight. This results in color space regions that serve as prior information in the initial phase of learning which consists in detecting small reliable clusters of pixels having the appropriate colors. These clusters are then expanded by a region growing technique using broader color space regions than those predicted by the model. This is to detect objects in a way that is able to account for color variations which the model cannot due to its limitations. Validation on the detected objects is performed to filter out those that are not of interest and to eliminate unreliable pixel color values extracted from the remaining ones. Detection results using the color space regions determined from color values obtained by this procedure are discussed.

Date Published: 16 September 1999
PDF: 15 pages
Proc. SPIE 3826, Polarization and Color Techniques in Industrial Inspection, (16 September 1999); doi: 10.1117/12.364314

Published in SPIE Proceedings Vol. 3826:
Polarization and Color Techniques in Industrial Inspection
Elzbieta A. Marszalec; Emanuele Trucco, Editor(s)