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An Industrial Vision System For Moderately Unconstrained Conditions

An industrial vision system capable of recognition of non-overlapping parts is presented. The system operates in a moderately unconstrained environment in terms of lighting and object surface characteristics. Typical images exhibit non-homogeneous object surfaces, transparency, shadows and specular reflections. The input image is mapped into a ternary image corresponding to the brightness of each pixel relative to the estimated background. A bounding rectangle is fit to each segmented object by aligning its sides parallel to the principal axis of the object. The shape recognition system uses features which are extracted from the projections of each segmented object onto the vertical and horizontal axes of the bounding rectangle, and from the projections of the skeleton of the segmented object. The classification scheme is chosen so that perfect segmentation is not a requirement of the system. Two uncon-nected object configurations can be recognized. Results are shown for an object domain in which different versions of objects within the same class exhibit different shape and brightness features, and objects in different classes exhibit resembling features. For 37 test images of tools, some with multiple tools in the image, the vision system successfully classified each tool into one of six classes.