SURFACE CHARACTERIZATION AND PARAMETER-ESTIMATION FOR INDUSTRIAL VISION SYSTEM

In this paper we have presented some geometric techniques to character ize and parametrize surfaces of industrial parts in range images. The surfaces are characterized to one of plane, sphere, cylinder and cone, because they form the majority of object surfaces in man-made industr ial parts. The problem has been studied for two different situations. In the first case, a priori knowledge about the surface shape is assum ed. In such a situation the problem of surface characterization reduce s to that of surface parameter estimation. The standard deviations of the estimated parameters give a measure of uncertainty of characterizi ng a surface patch to one of the four surface types. In the second cas e, no a priori information regarding the shape of a surface is availab le. This includes partially visible surfaces also. To deal with such a situation, a fuzzy classifier is designed using the uncertainty value s. The fuzzy classifier classifies the unknown surface patch (includin g partially visible surfaces) to one of the four surface types. Experi mental results with synthetic range images are presented to highlight the distinctive features of our technique.