Robust and accurate fitting of geometrical primitives to image data of microstructures

This paper presents a new method for robust recognition and separation of outliers in image data of microstructures with subsequent precise fitting of geometrical primitives to the measurement points.The known iterative approaches for robust fitting often yield unsatisfactory results if the start conditions are influenced by outliers. Thus, in a first step the outlier detection and filtering in the coordinate space is done by the Hough transform. The geometrical primitive is calculated by least squares fitting of orthogonal distances to the outlier free coordinates with high and certified accuracy. The performance of this approach is exemplified on the geometrical primitive circle with different outlier populations which are typical for industrial applications.The originality consists in the combination of a recognition approach for robust feature detection with a least squares fitting for highly precise results. This approach has an enormous relevance for applications of non-contact dimensional metrology, where the existence of dirt and burr leads to a high measurement uncertainty.