Refereed full papers (journals, book chapters, international conferences)

1997

Our aim is to analyze and evaluate different color models to be used for the purpose of 3-D object recognition by color-metric histogram matching according to the following criteria: invariance to the geometry of the object and illumination circumstances, high discriminative power, and noise robustness. Assuming white illumination and dichromatic reflectance, we propose new color models c₁c₂c₃ and l₁l₂l₃ invariant to the viewing direction, object geometry and shading. Further, it is shown that l₁l₂l₃ is also invariant to highlights. Further a change in spectral power distribution of the illumination is considered to propose a new photometric color invariant m₁m₂m₃ for matte objects. To evaluate photometric color invariant object recognition in practice, experiments have been carried out on a database consisting of 500 images taken from 3-D multicolored man-made objects. On the basis of the reported theory and experimental results, it is shown that high object recognition accuracy is achieved by l₁l₂l₃ and hue H followed by c₁c₂c₃ and normalized colors rgb under the constraint of white illumination. Saturation S and m₁m₂m₃ provide slightly worse object recognition accuracy under the same imaging conditions. Finally, it is shown that solely m₁m₂m₃ is invariant to a change in illumination color.