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Image contour based vision measurement is widely applied in robotmanipulation and industrial automation. It is appealing to realizeobject-agnostic vision system, which can be conveniently reused for varioustypes of objects. We propose the contour primitive of interest extractionnetwork (CPieNet) based on the one-shot learning framework. First, CPieNet isfeatured by that its contour primitive of interest (CPI) output, a designatedregular contour part lying on a specified object, provides the essentialgeometric information for vision measurement. Second, CPieNet has the one-shotlearning ability, utilizing a support sample to assist the perception of thenovel object. To realize lower-cost training, we generate support-query samplepairs from unpaired online public images, which cover a wide range of objectcategories. To obtain single-pixel wide contour for precise measurement, theGabor-filters based non-maximum suppression is designed to thin the rawcontour. For the novel CPI extraction task, we built the Object ContourPrimitives dataset using online public images, and the Robotic Object ContourMeasurement dataset using a camera mounted on a robot. The effectiveness of theproposed methods is validated by a series of experiments.

Contour Primitive of Interest Extraction Network Based on One-Shot Learning for Object-Agnostic Vision Measurement