Using shape to correct for observed non-uniform color in automated egg grading

摘要

We report on algorithmic aspects for the automated visual quality control for grading of brown eggs. Using RGB color images of four different views of every egg enabled to analyze the entire eggshell. The scene was illuminated using a set of white fluorescent tubes placed in a rectangular grid. After detection and approximation of the egg contour (ellipse fitted), the color was corrected to compensate for the elliptical shape of the eggs. A second order polynomial was fitted through points taken from subsequent horizontal lines inside the egg. Iteration was used to reject outliers (most likely points with visual defects). The shape-corrected intensity was calculated as the signed difference between polynomial and measured value, increased with the average egg intensity. Based on the corrected color, dirt regions like yolk, manure, blood, and red mite spots were segmented from the egg-background. Features based on color and shapes were calculated for every segmented region as the combined space and color moments of zeroth, first and second order. A classifier identified most of the defective eggs. Elimination of false rejects due to mirror reflection of the light tubes on some eggs (segmented because of the different color) is currently under investigation.